Neuron

Perspective

Striatal Local Circuitry:A New Framework for Lateral Inhibition

Dennis A. Burke,1,2 Horacio G. Rotstein,3,4 and Veronica A. Alvarez1,5,*1Laboratory on Neurobiology of Compulsive Behaviors, Intramural Research Program, National Institute on Alcohol Abuse and Alcoholism,NIH, Bethesda, MD 20892, USA2Department of Neuroscience, Brown University, Providence, Providence, RI 02912, USA3Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ 07102, USA4Institute for Brain and Neuroscience Research, New Jersey Institute of Technology, Newark, NJ 07102, USA5Intramural Research Program, National Institute on Drug Abuse, NIH, Baltimore, MD 21224, USA*Correspondence: alvarezva@mail.nih.govhttps://doi.org/10.1016/j.neuron.2017.09.019

This Perspective will examine the organization of intrastriatal circuitry, review recent findings in this area, anddiscuss how the pattern of connectivity between striatal neurons might give rise to the behaviorally observedsynergism between the direct/indirect pathway neurons. The emphasis of this Perspective is on theunderappreciated role of lateral inhibition between striatal projection cells in controlling neuronal firingand shaping the output of this circuit. We review some classic studies in combination with more recentanatomical and functional findings to lay out a framework for an updated model of the intrastriatal lateral in-hibition, where we explore its contribution to the formation of functional units of processing and theintegration and filtering of inputs to generate motor patterns and learned behaviors.

IntroductionThe striatum, as the input nucleus to the basal ganglia, is func-

tionally implicated in motor planning, action selection, reward

guided learning, and other motivated behaviors and cognitive

processes. Not surprisingly given these broad functions, striatal

dysfunction is involved in a variety of diverse neurological and

psychiatric diseases including, but not limited to, Parkinson’s

disease, Huntington’s disease, schizophrenia, depression, and

addiction. By receiving glutamatergic inputs from cortical,

thalamic, and limbic regions combined with dopaminergic input

from the midbrain, the striatum acts as an integrative hub that

assists in the selection of appropriate behaviors through its

outputs to downstream basal ganglia structures (Redgrave

et al., 1999). This integrative function of the striatum that is crit-

ical for the processing of input information is thought to happen

at the level of the local intrastriatal circuitry. However, it is still

unclear exactly how local striatal circuitry transforms information

flow and ultimately affects behavioral output. This Perspective

summarizes the most recent finding on the properties and

organization of the local circuitry in the striatum with special

emphasis on the connectivity between the striatal projection

neurons. This component of the circuitry has been largely

ignored in the past because the connectivity is considered rela-

tively sparse. However, recent studies suggest that this synaptic

connection between projection neurons, which is mediated by

their local axon collaterals within the striatum, can exert potent

lateral inhibition to the striatal circuitry, which can profoundly

regulate the excitability of other projection neurons. Focusing

on this underappreciated component of the striatal circuitry,

this Perspective further speculates that these connections are

functionally organized to aid in the information processing that

shapes striatal output. In doing so, we provide a conceptual

model in which to test how particular patterns of connectivity be-

tween projection neurons can affect the firing. This model pro-

vides a framework for understanding the role of striatal lateral

inhibition with the hope of inspiring future connectomics

research into the organization of the intrastriatal connectivity

and how it ultimately assists in coordinating behavior and

learning both in health and in disease.

The layout of the Perspective is as follows: General Overview

provides a general overview of the different cell types within the

striatum as well as its functional role and inputs. Local Striatal

Circuitry focuses on the local connectivity within the striatum

and describes the wide variety of synaptic connections among

striatal neurons. SPN Collateral Transmission Drives Lateral

Inhibition concentrates on the synapses formed between projec-

tion neurons and offers a historical view from their initial dis-

covery to current findings. It discusses possible functional roles

for this lateral inhibition and presents evidence for the existence

of striatal ensembles. Finally, A Conceptual Model of Synaptic

Organization of the Lateral Inhibition builds on this empirical

data and lays out a theoretical framework for how the synapses

between striatal projection neurons might be organized to aid in

formation of ensembles or functional units. Under this hypothet-

ical model, the pattern of connectivity of the lateral inhibition

determines the degree of co-activation of striatal projection neu-

rons and, as such, shapes the striatal output. It also demon-

strates that different patterns of intrastriatal connectivity will

affect the degree of neuronal co-activation, determining which

units of cells fire together, ultimately contributing to each

ensemble. Further, it opens the door to propositions involving

modulators of lateral inhibition in dynamically re-shaping the

ensembles, which might underlie forms of striatal learning.

This last section aims to stimulate discussion and generate

testable hypotheses that drive research on the topic of striatal

organization.

Neuron 96, October 11, 2017 Published by Elsevier Inc. 267

Neuron

Perspective

General OverviewHeterogeneity of Striatal Subregions and Cell Types

Striatal Subregions. The rodent striatum is traditionally subdi-

vided into two regions, dorsal and ventral, which contain subdi-

visions based on their inputs and/or immunohistochemical

markers. The dorsal striatum, or neostriatum, can be divided

into the dorsal lateral striatum (DLS) and the dorsal medial

striatum (DMS) based on glutamatergic inputs. The ventral

striatum includes the olfactory tubercule and the nucleus

accumbens (NAc), which can be further subdivided into ‘‘core’’

and ‘‘shell’’ subregions based on inputs and immunohistochem-

ical markers.

Another dimension along which the striatum can be divided is

the ‘‘patch’’ (striosome) and ‘‘matrix’’ compartments. First iden-

tified by the expression patterns of proteins such as mu-opioid

receptors and acetylcholinesterase, there are now over 60 genes

known to be differentially expressed in patch versus matrix

compartments (Crittenden and Graybiel, 2011). Though patches

only represent a small proportion of the striatum by volume,

many of the differentially expressed genes are neuromodulators

and their receptors suggesting that the patch and matrix have

distinct pharmacological properties (Banghart et al., 2015; Brim-

blecombe and Cragg, 2017). Afferents to patch and matrix are

also thought to arise from different sources, with patches

receiving limbic inputs and matrix neurons receiving sensori-

motor inputs (Gerfen, 1984). However, recent viral tracing

methods have cast doubt on this strict segregation (Smith

et al., 2016).

The striatal subregions are also functionally segregated. In

general terms, the dorsal striatum is involved in motor planning,

action selection, and stimulus-response habit learning, while the

NAc plays a role in processing-motivated behavior and reward-

related learning. However, there is much overlap in the known

roles of the dorsal and ventral striatum in controlling motor and

motivated behaviors and learning (Isomura et al., 2013). Tech-

nical limitations make it difficult to distinguish motivation from

motor ability and to discriminate learning and memory from

performance. Also the overlapping functions between these

striatal subregions is hypothesized to be embedded within the

circuit connectivity in the form of ‘‘ascending spiral loops’’ that

allow for information flow from the ventral to the dorsal striatum

via the circuitry within the basal ganglia (Haber et al., 2000).

Spiny Projection Neurons. In addition to potential functional

overlaps, the dorsal and ventral striatum share multiple features

including their main cell-type composition. The principal projec-

tion neurons in the striatum are the GABAergic spiny projection

neurons (SPNs; also referred to as ‘‘medium spiny neurons’’),

which comprise over 95% of the neurons in this region (Gravel-

and and DiFiglia, 1985) and are the only projection neurons of

the striatum. SPNs are classically divided in two subclasses

based on their projection targets and expression of dopamine

receptors (Gerfen et al., 1990). Direct pathway SPNs (dSPNs)

represent approximately half of the SPN population, mainly ex-

press Gs/olf-coupled dopamine D1 receptors, and send dense

projections to the ventral tegmental area (VTA) or the substantia

nigra pars reticulata and internal globus pallidus/entopeduncular

nucleus through the ‘‘direct’’ pathway of the basal ganglia. Indi-

rect pathway SPNs (iSPNs) mainly express Gi/o-coupled dopa-

268 Neuron 96, October 11, 2017

mine D2 receptors and project through the ‘‘indirect’’ pathway

to the external globus pallidus (GP) and the ventral pallidum

(VP). It is clear now that this dichotomy is an over-simplification

of the existing subtypes of SPNs and that the segregation is

imperfect. For example, a subset of SPNs co-express both

D1 and D2 receptors in the adult striatum (�2%–5% dorsal

striatum,�6%–7% in the NAc core,�12%–15% in the NAc shell)

that are morphologically distinct from other SPNs and have a

poorly understood function (Bertran-Gonzalez et al., 2008; Gag-

non et al., 2017). Furthermore, a recent study shows large cell-

to-cell variability in the pattern of overall gene expression within

each subclass of SPN (Gokce et al., 2016). Another consider-

ation is that the projections are not fully segregated in the sense

that D1R expressing SPNs in the dorsal striatum also send collat-

eral axon projections to the GP (Cazorla et al., 2014; Kawaguchi

et al., 1990) and those in the NAc send numerous projections the

VP (Kupchik et al., 2015). While the classic segregation of SPNs

into direct- and indirect-pathway neurons is incomplete and

oversimplified, it has proven extremely useful for advancing

our understanding of this brain region and the function of the

basal ganglia as a whole. With these caveats in mind, keeping

with field nomenclature, throughout this manuscript we will use

dSPN to refer to SPNs expressing D1 receptors and iSPNs to

refer to those expressing D2 receptors.

Interneurons. The remaining 5% of neurons in the striatum are

either cholinergic or GABAergic interneurons. Cholinergic inter-

neurons (CIN) are large, tonically active neurons that make up

about 1% of the cells in the striatum and, as their name implies,

release acetylcholine (Zhou et al., 2002). These cells have been

classically referred to as TANs for ‘‘tonically active neurons’’

(but see below that they are not the only type of spontaneously

active interneuron). Despite their relatively low abundance,

CINs extend wide axonal arborizations throughout the region

profoundly influencing striatal function through a variety of

muscarinic and nicotinic acetylcholine receptors (reviewed in

Gonzales and Smith, 2015). Recently, CINs were shown to also

co-release glutamate (Guzman et al., 2011; Higley et al., 2011;

Nelson et al., 2014a). Further experiments using paired record-

ings will be useful in confirming this finding.

TheGABAergic interneurons of the striatum can be subdivided

in three classical groups and at least three new additional re-

ported subpopulations based on a combination of molecular,

physiological, and pharmacological properties (Figure 1).

The three classically described GABAergic interneurons are

the fast-spiking (FSI), the low-threshold spiking (LTSI), and the

calretinin expressing (CR) interneuron. FSIs, the best-studied

subtype of the GABAergic interneurons, express parvalbumin

(PV), can fire action potentials at high frequencies, and mediates

strong feedforward inhibition onto SPNs. LTSIs are tonically

active, and although striatal ‘‘TANs’’ is used in the literature to

refer to presumed cholinergic interneurons, this is an important

consideration for in vivo electrophysiology experiments. LTSIs

exhibit low-threshold Ca2+ spikes and can be identified based

on the expression of somatostatin (SOM), nitric oxide synthase

(NOS), and neuropeptide Y (NPY). Not much is known with re-

gards to the physiology or synaptic function of CR interneurons

likely due to their low abundance in mouse compared to primate

striatum (Petryszyn et al., 2014).

GABA

ACh

PV

SOM/NOS/NPY

NPY

CR

FS ChAT

TAN

LTS*

FA5HT3R

TH

?

IIIIIIIV

NGF-like

striatal interneurons

PV/5HT3R

Figure 1. Diversity of Cell Types of Striatal InterneuronsThe plot represents the estimated proportions of each subtype of striatalinterneuron identified based on their expression of neurotransmitter (innerdonut), other molecular markers (middle donut), and electrophysiologicalproperties (outer donut). Abbreviations: ACh, acetylcholine; ChAT, choline-acetyl transferase; TAN, tonically active neurons; TH, tyrosine hydroxlase;CR, calretinin; SOM, somatostatin; NOS, nitric oxide synthase; NPY, neuro-peptide Y; LTS, low-threshold spiking; NGF, neurogliaform; 5HT3R, serotonintype-3 receptor; FA, fast adapting; PV, parvalbumin; FS, fast spiking. *, SOM/NOS LTS neurons are also tonically active, although not classically referredas TANs.

Neuron

Perspective

Recently, BAC transgenic mice have facilitated further classi-

fication of novel GABAergic striatal interneuron subtypes based

on molecular, physiological, and pharmacological properties

(Figure 1). For example, a recently characterized population of

striatal interneurons expresses tyrosine hydroxlase (TH). TH

interneurons all release GABA, and this group does not appear

to overlap with other known subtypes of striatal interneurons.

Characterization of these neurons has revealed four distinct

subtypes based on electrophysiological properties, termed

Type I-IV (Ibanez-Sandoval et al., 2010). Interestingly, though

tyrosine hydroxylase is the rate limiting enzyme in dopamine

synthesis and is used as a marker for dopamine neurons, TH

interneurons do not appear to release dopamine (Xenias

et al., 2015).

Two recently identified novel interneuron subtypes are the

NPY-neurogliaform (NPY-NGF) interneuron and the fast-adapt-

ing interneuron (FAI). NPY-NGF interneurons express NPY, but

not SOM and NOS, as LTSIs do, and share similarities with

cortical NPY-expressing neurogliaform cells (Ibanez-Sandoval

et al., 2011). NPY-NGF neurons are excited by acetylcholine

through nicotinic receptors and differ substantially from SOM/

NOS/NPY-expressing LTS neurons in both electrophysiological

and anatomical characteristics, as well as in the synaptic

response that they elicit in SPNs.

FAIs have been identified by expression of the serotonin

5-HT3 receptor (5HT3R) and given their name due to a distinct

spike-frequency adaptation during repetitive firing (Faust et al.,

2015). FAIs express nicotinic acetylcholine receptors. The

5HT3R is not a perfect marker for the FAI, however, as in many

interneurons in the striatum the 5HT3R is coexpressedwith other

interneuron markers, in particular PV. Neurons that express both

PV and 5HT3R appear to have FSI electrophysiological charac-

teristics similar to those neurons that express PV alone (Munoz-

Manchado et al., 2016). For reviews of striatal interneurons, see

Tepper et al. (2010) and Tepper and Koos (2017).

Functional Overview

Classical models of basal ganglia function posit that activation

of dSPNs disinhibits principal thalamic neurons to facilitate

behavior, while activation of iSPNs ultimately inhibits thalamic

neurons to suppress motor and reward-based behaviors. Dysre-

gulation in the balance of dSPN and iSPN activity is thought to be

the underlying cause of several basal ganglia movement disor-

ders (Albin et al., 1989; DeLong, 1990). This model is supported

by experiments using cell-specific genetic perturbation, cell

ablation, or optogenetic activation (Bateup et al., 2010; Bock

et al., 2013; Durieux et al., 2009; Kravitz et al., 2010, 2012;

Lobo et al., 2010; Tai et al., 2012).

Glutamate inputs drive SPN firing and, without these excit-

atory inputs, SPNs are quiescent and their membrane potential

is very hyperpolarized. During a desirable action, dopamine is

thought to modulate the neuronal excitability to acutely raise

the signal-to-noise ratio through activation of Gs/olf-coupled D1

receptors (D1Rs) on dSPNs and the Gi/o-coupled D2 receptors

(D2Rs) on iSPNs. Also, by triggering synaptic plasticity and

inducing changes in gene expression, dopamine biases future

behavior toward repeating desirable actions, mediating pro-

cesses that underlie learning (Alexander and Crutcher, 1990;

Cohen and Frank, 2009; Gerfen and Surmeier, 2011; Surmeier

et al., 2014; Tritsch and Sabatini, 2012).

There is accumulating evidence for a more nuanced view of

the interaction between the direct and indirect pathways in

driving behavior. Recent work in the dorsal striatum using cal-

cium imaging with fiber photometry (Cui et al., 2013; Tecuapetla

et al., 2014), deep brain calcium imaging (Barbera et al., 2016),

and in vivo electrophysiology, in which SPN subtypes were iden-

tified with optogenetic tagging (Jin et al., 2014) or post hoc in situ

hybridization (Isomura et al., 2013), have all demonstrated that

both dSPNs and iSPNs are more active when an animal is

performing a task or freely moving and less active when the

animal is not moving or engaged in a task (though dSPNs and

iSPNs have differing activity patterns). These data imply that

iSPNs and dSPNs are coactive during action initiation and that

the direct and indirect pathways are both necessary to facilitate

actions. Further support for this idea comes from studies

showing that inhibition of either iSPNs or dSPNs can disrupt

action initiation or ongoing behavior (Tecuapetla et al.,

2014, 2016).

Extending this idea to the ventral striatum, optogenetic inhibi-

tion of either iSPN or dSPNs in the NAc after trial start cue in an

operant food task reduced motivated responding on the task

(Natsubori et al., 2017), and inhibiting synaptic transmission

from either iSPNs or dSPNs prevents the acute locomotor

response to cocaine and methamphetamine, a behavior known

to involve the NAc (Hikida et al., 2010). These studies provide

evidence that both dSPNs and iSPNs are more active during

actions than at rest, and activity in both SPN subtypes is needed

for driving normal motivated behavior.

Neuron 96, October 11, 2017 269

Neuron

Perspective

In addition, fiber photometry recordings that examine the

activity of dSPNs and iSPNs in the NAc during an operant food

responding task show that both SPN subtypes are more active

after a trial start cue and reward delivery than at rest (Natsubori

et al., 2017). Furthermore photometry recordings in the NAc dur-

ing a risky decision task have demonstrated that iSPN activity is

highest following a risky choice that led to no reward and that this

activity predicts future ‘‘safe’’ choices (Zalocusky et al., 2016).

When recording calcium transients in iSPNs during a cocaine-

conditioned place preference paradigm, iSPNs show marked

decreases in their activity after the animal moves into the drug

paired side of the chamber, similar to their response to cocaine

(Calipari et al., 2016). Three main considerations with fiber

photometry experiments are: (1) this technique measures cal-

cium signals, an indirect assessment of spikes, and while it is

capable of detecting single spikes under the right conditions,

this is almost rarely verified, (2) the time course of the photometry

signal is largely determined by properties of the calcium indica-

tor, not the spiking, and (3) the spatial information is lost when

collecting photons through a fiber and as such photometry

measures bulk calcium signals that are amplified when there is

simultaneous activation. Thus, photometry signals in vivo are

most likely a reflection of 1–3 action potentials and the signal

detection is biased toward synchronized events that can sum-

mate in time. Photometry signals might reflect the activity of

ensembles rather than single neurons (Alvarez, 2016). While it

is important to consider these factors, neither of these consider-

ations undermines the main findings that co-activation of iSPNs

and dSPNs is seen during a wide range of behaviors. Altogether

these studies suggest that both the direct and indirect pathways

are necessary to drive behavioral output in both the dorsal and

the ventral striatum.

An important question then is how to reconcile the findings

from optogenetic stimulation experiments suggesting that

iSPN activation inhibits movement (Kravitz et al., 2010) with

those from calcium dynamics showing iSPN activation during

action initiation and behavior (Barbera et al., 2016; Cui et al.,

2013; Natsubori et al., 2017). The most parsimonious explana-

tion is that during behavior, dSPNs facilitate the desired

behavior, while iSPNs act simultaneously to inhibit all other

competing undesirable behaviors (see more in Local Striatal

Circuitry). Indeed, this concept of the basal ganglia serving as

a selection mechanism between competing behavioral pro-

grams was proposed over 20 years ago by Jonathon Mink

(Mink, 1996, 2003; Mink and Thach, 1993) and Okihide Hikosaka

(Hikosaka, 1991, 1998), and it has been incorporated into

computational models of basal ganglia function (Collins and

Frank, 2014; Frank, 2005). Consistent with this, global optoge-

netic stimulation of iSPNs would therefore suppress all behav-

ioral programs and inhibit movement.

If this model were correct, precise regulation of the activity of

dSPNs and iSPNs that control competing behavioral programs

would be required (Tecuapetla et al., 2016), making it important

to determine how striatal circuitry can give rise to this coordi-

nated activity. In order for the model to work properly, coordi-

nated activation of dSPNs that facilitate a desired behavior and

activation of iSPNs that suppress competing behaviors should

be achieved. This coordination could be dictated by the inputs

270 Neuron 96, October 11, 2017

to the striatum, the organization of the local intrastriatal connec-

tivity, or a combination of both. In the next sections, we will first

review inputs into the striatum and then discuss the local intra-

striatal circuity.

Main Inputs to the Striatum

The striatum receives two main types of inputs: glutamatergic

and dopaminergic. Glutamatergic projections arise from most

cortical regions, as well as thalamic and limbic regions. Cortical

inputs descend in a topographic fashion with neighboring

regions of cortex projecting to adjacent regions of the striatum

(Bolam et al., 2000; Dudman and Gerfen, 2015). The DLS

receives inputs from sensorimotor cortex, while the DMS is

innervated by associative regions of cortex. Both DMS and

DLS receive inputs from the thalamus. The NAc is targeted by

associative and limbic regions of cortex, as well as amygdala,

hippocampal, and thalamic nuclei (Britt et al., 2012; Bubser

and Deutch, 1998; MacAskill et al., 2012). Cortical inputs target

both subtypes of SPNs, with single axons making synapses

onto both dSPNs and iSPNs (Doig et al., 2010). While anatomical

studies have suggested that dSPNs and iSPNs receive inputs

from different types of cortical neurons, with intertelencephalic

(IT) neurons more likely to synapse onto dSPNs and pyramidal

tract neurons (PT) more likely to synapse onto iSPNs (Lei et al.,

2004), physiological studies have shown that both types of

cortical neurons synapse onto both iSPNs and dSPNs (Kress

et al., 2013). Retrograde viral tracing methods have suggested

that there may be differences in dSPN and iSPN innervation by

cortical region (Wall et al., 2013). However, a subsequent study,

in which the number of presynaptic neurons labeled outside the

striatum was an order of magnitude greater, found no difference

in dSPN or iSPN innervation by cortical region (Guo et al., 2015).

This suggests that the initial findings of segregated inputs

could have suffered from sampling bias and cautions against

drawing quantitative conclusions from viral methods with low

infection rates.

In the dorsal striatum, both dSPN and iSPNs receive glutama-

tergic inputs from not only cortex, but also thalamus, and single

axons of both types contact both dSPNs and iSPNs (Doig et al.,

2010). However, cortical and thalamic synapses differ in their

properties: cortical synapses onto SPNs have amuch higher fail-

ure rate and are facilitating, while thalamic synapses have a

lower rate of failure and are depressing (Ding et al., 2008). Syn-

apses from different thalamic nuclei display a great deal of het-

erogeneity in their strength, short-term dynamics, and SPN sub-

type targets (Ellender et al., 2013; Smith et al., 2009).

Furthermore, through their interactions with cholinergic interneu-

rons, thalamic neurons appear to ‘‘gate’’ cortical information

flow, suggesting an interesting interaction between these two in-

formation streams for striatal processing (Ding et al., 2010).

The striatum is densely innervated by dopamine neurons.

Dopamine neurons of the substantia nigra pars compacta

target the dorsal striatum, while those of the VTA target the

ventral striatum. Dopamine’s influence on striatal function is

vast and complex due to the variety of D1-like (D1 and D5)

and D2-like (D2, D3, and D4) receptors located on both axon

terminals and postsynaptic sites in the striatum. In addition to

differential effects in directly modulating D1 containing dSPN

and D2 containing iSPNs, dopamine powerfully influences

Neuron

Perspective

striatal processing through its actions on cholinergic and

GABAergic interneurons as well as presynaptic terminals of

inputs to the striatum. These actions of dopamine on the inputs

can be acute and reversible or expressed in the form of long-

term changes in synaptic strength (for reviews, see Gerfen

and Surmeier, 2011; Kreitzer and Malenka, 2008). For reviews

covering afferents to GABAergic interneurons, see Tepper

et al. (2010) and Tepper and Koos (2017).

It is now known that the midbrain provides another source of

glutamate to these regions. The VTA contains a population of

glutamate neurons that projects to the striatum (Hnasko et al.,

2012; Yamaguchi et al., 2007, 2011, 2015) aswell as a population

of neurons that co-release dopamine and glutamate (Hnasko

et al., 2010; Stuber et al., 2010; Tecuapetla et al., 2010). VTA

glutamate neurons projecting to the NAc shell synapse onto

FSIs and mediate behavioral aversion (Qi et al., 2016). In addi-

tion, it has recently been reported that nondopaminergic

glutamate neurons in the VTA can also co-release GABA and

that stimulation of these neurons can be transiently reinforcing

(Yoo et al., 2016).

Further, dopamine neurons have been shown to co-release

GABA along with dopamine in the dorsal striatum (Tritsch

et al., 2012) and the NAc, though dopamine neuron IPSCs re-

corded in the NAc are smaller than in the dorsal striatum (Kim

et al., 2015). Interestingly, dopamine neurons do not synthesize

GABA through canonical GAD65 and GAD67 enzymes but rather

uptake it through the plasmamembrane (Tritsch et al., 2014) and

synthesize it through a non-canonical aldehyde dehydrogenase

1a1 pathway (Kim et al., 2015).

Functional GABA projections from cortex have recently been

described in both NAc and dorsal striatum. The mPFC sends

GABA projections to the NAc shell, and activation of these

afferents produces avoidance behavior in a real-time place-pref-

erence paradigm (Lee et al., 2014). In the dorsal striatum, cortical

GABA projections from layer 5 and 6 SOM+ cells to both dSPNs

and iSPNs have recently been described in both auditory and

motor cortex, implying that this may be a general feature of

cortex (Rock et al., 2016). These findings are in agreement with

anatomical studies that identified cortical GABA projections to

the striatum (Tomioka et al., 2015) and challenge the common

assumption that cortical projections to the striatum are exclu-

sively glutamatergic.

Another large source of GABA to the striatum comes from

back projections from the GP. Recently identified arkypallidal,

or GP-TA, neurons in the GP exclusively target the striatum

with �10,000 boutons per cell and synapse onto SPNs as well

as both cholinergic and GABAergic (PV+ and NOS+) interneu-

rons, suggesting that these cells may provide the largest

extrinsic source of GABA to the striatum (Mallet et al., 2012).

Functionally, in vivo recordings of these neurons during a

behavior task, in which animals must abruptly abort an action,

imply that arkypallidal cells may be responsible for sending a

‘‘stop’’ signal to the striatum to cancel evolving action plans

(Mallet et al., 2016). In addition to arkypallidal cells, GP neurons

that project to downstream structures send GABA projections to

the striatum that target PV interneurons (Bevan et al., 1998). VTA

contains nondopaminergic GABA neurons that project to the

NAc (Van Bockstaele and Pickel, 1995). In addition to locally

modulating the excitability of VTA dopamine neurons (Tan

et al., 2012; van Zessen et al., 2012), these neurons project to

cholinergic interneurons in the NAc and can pause their firing,

which plays a role in associative learning (Brown et al., 2012).

Thus, in addition to GABA co-released from midbrain dopamine

neurons, GABA projection neurons in midbrain also influence

striatal circuits via their projections to CINs.

Another novel inhibitory projection from the bed nucleus of the

stria terminalis to the striatum has also been described, terminat-

ing on patch and exo-patch SPNs in the dorsal striatum (Smith

et al., 2016). Using tract tracing, another study identified an

extrinsic source of acetylcholine arising from brainstem nuclei,

the pedunculopontine nucleus and the lateral dorsal tegmental

nucleus (Dautan et al., 2014). The function of these newly

identified projections still needs to be determined. Other minor

afferents to the striatum release neuromodulators, such as sero-

tonin from the dorsal raphe (Miguelez et al., 2014) and histamine

and oxytocin from hypothalamus (Bolam and Ellender, 2016;

Dolen et al., 2013).

3. Local Striatal CircuitryThere is increasing appreciation for the role that local striatal

circuitry plays in allowing the striatum to compute sensory,

motor, and limbic information into behavioral or cognitive output.

It can filter, modulate, amplify, and ultimately transform informa-

tion flow through this region and produce an output that could

not be predicted by the summation/integration of the inputs to

SPNs alone.

The local connectivity is mediated by the striatal interneurons

and by the synapses formed by collateral axons of SPNs that

linger within the striatum and contact other SPNs. While some

of these local connections are reciprocal, most of them are

unidirectional in nature. Local connectivity can shape the output

by directly affecting the activity of projection neurons via classic

axo-somatic and axo-dendritic synapses onto SPNs that alter

neuronal excitability, cell signaling, dendritic integration, and

gene expression. Alternatively, local connectivity can act indi-

rectly to affect striatal output via axo-axonal synapses, which

can modulate the synaptic inputs to SPNs or the synaptic trans-

mission from SPNs (Figure 2). Below, we will briefly outline the

different synaptic connections between local striatal interneu-

rons and projection neurons (Figure 3).

Axo-axonal Connections

In axo-axonal synapses, neurotransmitter release from one

synaptic bouton activates receptors located presynaptically on

another bouton or axon fiber and act directly to suppress, facil-

itate, or even trigger neurotransmitter release from the ‘‘postsyn-

aptic’’ axon terminal, sometimes without affecting the firing of

action potentials from the postsynaptic neuron (Figure 2). While

anatomical evidence for axo-axonal synapses in the striatum is

still lagging behind, functional data indicate that presynaptic

heteroreceptors can be activated quickly and reliably upon

neurotransmitter release from other terminals (Cachope et al.,

2012; Kosillo et al., 2016; Threlfell et al., 2012; J.H. Shin, M.F.

Adrover, and V.A.A., unpublished data). A recent example of

an axo-axonal connection that was functionally identified in the

striatum is between CINs and dopamine (DA) fibers. Synchro-

nized release of ACh from cholinergic interneurons can activate

Neuron 96, October 11, 2017 271

DA

GABA

ACh

D2R

GABA

Glut

5 M

D2

2D4/2

M

D2M2/4

nAR

M4

2D

DA

GABA

ACh

D2R

GABA

Glut

5 M

D2

2D4/2

M

D2M2/4

nAR

M4

2D?

Figure 2. Axo-axonal Modulation in theStriatumSimple diagram of known axo-axonal presynapticmodulation by dopamine and acetylcholine (ACh)in the striatum. D2, dopamine D2 receptor; M5,M5muscarinic ACh receptor; M2/4, M2 and M4muscarinic ACh receptors; nAR, nicotinic AChreceptor.

Neuron

Perspective

presynaptic nicotinic acetylcholine receptors (nAChRs) located

on DA axons and evoke DA release in both ventral and dorsal

striatum (Cachope et al., 2012; Threlfell et al., 2012). Via presyn-

aptic muscarinic M5 receptors on DA axon fibers, ACh can

further enhance DA transmission in the striatum (Shin et al.,

2015). Through activation of presynaptic muscarinic M2/M4 re-

ceptors on glutamatergic terminals from cortex and thalamus,

ACh can also suppress glutamate transmission onto SPNs

(Ding et al., 2010). GABA transmission from FSIs onto SPNs is

also inhibited by ACh through presynaptic muscarinic receptors

(Koos and Tepper, 2002). In addition, the existence of nAChRs

on glutamatergic terminals has been suggested (Kaiser and

Wonnacott, 2000). Further confirmation that CIN stimulation

and/or ACh release from these interneurons can produce these

modulatory effects on GABA and glutamate transmission will

be important for the understanding the spatial and temporal lim-

itation of ACh transmission

Other neurotransmitters act presynaptically in the striatum as

well: through activation of presynaptic D2 receptors, DA inhibits

its own release from DA terminals through autoreceptors,

GABA transmission from iSPNs (Dobbs et al., 2016; Tecuapetla

et al., 2009), and acetylcholine release from CINs. It is proposed

to inhibit glutamate transmission fromcortical afferents (Bamford

et al., 2004), though because cortical D2 receptor expression is

sparse, postsynaptic effects of dopamineonglutamate transmis-

sion likely playa large role in observedmodulation (HigleyandSa-

batini, 2010). Further, single NPY-NGF interneuron firing can

inhibit cortical glutamate release through presynaptic GABAB re-

ceptors, suggesting an axo-axonal synapse (Logie et al., 2013).

Axo-dendritic and Axo-somatic Connections

CIN-GABA Interneuron. Cholinergic interneurons form synapses

onto FSIs and release both glutamate and acetylcholine, though

these synapses are weak and sometimes undetected (English

et al., 2011; Nelson et al., 2014a). ACh has been shown to both

depolarize FSIs through nicotinic receptors and tomodulate their

synaptic transmission by inhibiting GABA transmission onto

SPNs through presynaptic muscarinic receptors (Koos and Tep-

per, 2002; Luo et al., 2013). Interestingly, despite this evidence of

nicotinic stimulation and depolarizing responses to cholinergic

stimulation, FSIs do not appear to play a major role in the disy-

naptic inhibition of SPNs through cholinergic interneurons

(Nelson et al., 2014b). CIN to FSI synaptic connections are not

reciprocal as it has been shown that FSI cells do not form

272 Neuron 96, October 11, 2017

synapses onto cholinergic interneurons

(Straub et al., 2016; Szydlowski

et al., 2013).

Cholinergic interneurons excite both

5HT3R-expressing and NPY-NGF neu-

rons through nicotinic receptors (English et al., 2011; Faust

et al., 2015). It is unclear whether these connections are recip-

rocal. However, stimulation of a single CIN is capable of eliciting

polysynaptic GABAA-mediated synaptic responses both in itself

and in nearby cholinergic interneurons (Sullivan et al., 2008). This

recurrent feedback inhibition requires activation of nAChRs and

while the cell-type identity of this source of GABA is unknown, it

is possible that GABA interneurons are responsible for the

recurrent inhibition. Though generally not detected in paired

recordings between cells in close proximity (Gittis et al., 2010;

Ibanez-Sandoval et al., 2011), optogenetic experiments have

shown that LTSIs form GABAergic synapses onto distant cholin-

ergic interneurons inhibiting them through GABAA receptors

(Straub et al., 2016). In addition, there is evidence that LTSIs

can induce slow depolarizations in cholinergic interneurons

mediated by nitric oxide (Elghaba et al., 2016).

TH interneurons are depolarized by nicotinic agonists, indi-

cating the presence of nicotinic receptors and suggesting that

TH interneurons receive synaptic contacts from cholinergic

interneurons or afferents (Ibanez-Sandoval et al., 2015; Luo

et al., 2013). However, direct synaptic responses from cholin-

ergic neurons have not yet been recorded.

CIN-SPN. Cholinergic neurons acutely modulate SPN excit-

ability and synaptic function directly though muscarinic recep-

tors (for review, see Oldenburg and Ding, 2011) and have a

role in controlling striatal plasticity (Wang et al., 2006). Indirectly

cholinergic interneurons can modulate SPNs through disynaptic

release of DA from midbrain terminals (Cachope et al., 2012;

Threlfell et al., 2012) and release of GABA from FAIs or NPY-

NGF interneurons (English et al., 2011; Faust et al., 2015, 2016)

as well as from DA terminals (Nelson et al., 2014b).

SPNs are known to synapse onto cholinergic interneurons and

release GABA (Chuhma et al., 2011). There is some anatomical

evidence that dSPNs in the dorsal striatum make more synaptic

contacts onto cholinergic interneurons than iSPNs do (Martone

et al., 1992); however, rigorous quantification is lacking, and

whether this is generalizable to the ventral striatum is unknown.

GABA Interneurons-GABA Interneurons. FSIs weakly and

sparsely inhibit LTSIs (Gittis et al., 2010; Lee et al., 2017; Szy-

dlowski et al., 2013). Paired recordings have failed to detect

LTSI synapses onto FSIs; however, as LTSI synapses onto

distant CINs and SPNs have also been missed by paired record-

ings, it remains possible that LTSIs synapse onto distant FSIs.

dSPN

iSPN dSPN

iSPN

ChAT

ChATNPY-NGF

PV

5HTR3

PV

SOM/NOS/NPY

TH

dSPN

iSPN

local circuitry

Figure 3. Main Identified SynapticConnections between Local StriatalNeuronsSchematic showing intrastriatal connectivity be-tween interneurons and projection neurons. In-terneurons displayedwithmolecular markers usedto identify cells during experiments. Abbrevia-tions: dSPN, direct pathway projecting mediumspiny neuron; iSPN, indirect pathway projectingmedium spiny neuron; ChAT, choline-acetyltransferase; TH, tyrosine hydroxlase; SOM, so-matostatin; NOS, nitric oxide synthase; NPY,neuropeptide Y; NGF, neurogliaform; 5HT3R,serotonin 3 receptor; PV, parvalbumin;

Neuron

Perspective

FSIsstronglysynapseonotherFSIs, communicatingchemically

through GABA synapses as well as electrically through gap junc-

tions (Gittis et al., 2010; Koos and Tepper, 1999). NPY-NGF inter-

neurons receive GABAergic inhibition from FSIs (Lee et al., 2017).

GABA Interneurons-SPNs. FSIs strongly synapse on SPNs

(Gittis et al., 2010; Koos and Tepper, 1999; Planert et al.,

2010). In vivo, FSIs respondmore quickly and to lower thresholds

of cortical stimulation than SPNs, providing a powerful mecha-

nism for feedforward inhibition from cortex (Mallet et al., 2005).

FSIs appear to have a slight bias in targeting dSPNs over iSPNs

(Gittis et al., 2010; Planert et al., 2010). There is no evidence for

SPNs synapsing onto FSIs (Chuhma et al., 2011). LTSIs have

been found in paired recordings with SPNs to evoke sparse

GABA responses on SPNs that were much weaker than those

evoked from FSIs to SPNs (Gittis et al., 2010; Luo et al., 2013).

Recent work using viral tracing and optogenetics has shown

that this difference in connectivity detected with paired record-

ings from cells in close proximity is likely due to differences in

spatial organization of these circuits: LTSIs target distant

SPNs, while FSIs target SPNs in close proximity (Straub et al.,

2016). Further, LTSIs synapse on distal dendrites of SPNs, while

FSIs target the soma and proximal dendrites of SPNs.

FAIs provide fast GABAA-mediated inhibition to SPNs, while

NPY-NGF neurons mediate slow GABAA synaptic responses

(English et al., 2011; Faust et al., 2015). NPY-NGF activation

inhibits SPN firing in vivo (Lee et al., 2017). Because NPY-NGF

interneurons and FAIs are excited by cholinergic stimulation,

this provides a both quick and prolonged mechanism for disy-

naptic cholinergic inhibition of SPNS. TH interneurons synapse

onto SPNs and release GABA. Interestingly, TH interneurons

are the only identified GABAergic interneurons to receive inputs

form SPNs (Ibanez-Sandoval et al., 2010), suggesting a unique

role for these interneurons in striatal processing.

Collateral Transmission between SPNs

In addition to their long-range projection targets, SPNs extend

dense axon collaterals that stay within the striatum or nucleus

accumbens and target other SPNs (Chang and Kitai, 1985;

Pennartz et al., 1991; Somogyi et al., 1981; Wilson and Groves,

1980). These GABAergic synapses are mostly formed on distal

dendrites of SPNs (Wilson and Groves,

1980) and are relatively weak compared

to FSI inputs that are made at the soma

(Czubayko and Plenz, 2002; Koos et al.,

2004; Tunstall et al., 2002). The connec-

tivity is sparse in the sense that only �30% of paired recordings

between SPNs produce a synaptic response, and these connec-

tions are all unidirectional.

The network of collateral axons can provide the circuit with a

sufficient degree of lateral inhibition to shape the output of the

striatum. Indeed, despite the early work showing weak synaptic

connections between discrete pairs of SPNs, synchronized

stimulation of axon collateral from multiple SPN can provide a

strong source of inhibition to other striatal SPNs (Dobbs et al.,

2016). The properties and regulation of this network of lateral

inhibition are discussed in more detail below. In addition, we

present some models for the organization of the lateral inhibition

and its implications for circuit level function.

4. SPN Collateral Transmission Drives Lateral InhibitionAnatomical and Functional Evidence

Early anatomical studies identifying SPNs in the dorsal striatum

as the projection neurons of this region made note of their dense

axons collaterals within the striatum (Grofova, 1975; Kemp and

Powell, 1971). Indeed, some of the earliest studies identifying

SPNs, including those by Ramon y Cajal (1911), identified them

as interneurons due to their intrastriatal axon branches and the

limitations of the Golgi technique (Tepper et al., 2010). By

combining antidromic stimulation recordings with cell filling

and anatomical reconstruction, it became clearer that SPNs

have both projections out of the striatum and an extensive collat-

eral plexus within the striatum (Preston et al., 1980). The use of

light and electron microscopy in the early 1980s allowed both

Wilson and Groves and Somogyi and colleagues to show that

SPN axons, in addition to projecting out of the striatum, target

other SPNs within the striatum (Somogyi et al., 1981; Wilson

and Groves, 1980). As the idea of separate direct/indirect

pathway SPN subpopulations began to emerge, Kawaguchi

and colleagues demonstrated that SPNs form intrastriatal

axon collaterals regardless of the downstream projection target

(i.e., whether that cell was an iSPN or dSPN) (Kawaguchi et al.,

1990). Electron microscopy was combined with immunocyto-

chemistry to show that iSPNs and dSPNs were synaptically

interconnected and synapses were formed between SPNs of

Neuron 96, October 11, 2017 273

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Perspective

different subclasses (Yung et al., 1996). Anatomical studies in the

NAc confirmed that similar to the dorsal striatum, NAc SPNs

have dense axon collaterals that remain within the accumbens

(Chang and Kitai, 1985; Pennartz et al., 1991) in addition to the

long-range axon projections to the midbrain and pallidum.

Despite the wealth of anatomical evidence, the resulting

functional effect of the high density of local synapses between

SPNs has been harder to grasp. Electrophysiological studies us-

ing antidromic stimulation hinted at early evidence of functional

GABAergic synapses from the axon collaterals in both dorsal

striatum (Park et al., 1980) and nucleus accumbens (Chang

and Kitai, 1985). Despite this encouraging functional evidence,

one of the first studies directly addressing the existence of func-

tional synapses using sharp intracellular electrode recordings in

pairs of SPNs in the dorsal striatum of rats found no evidence for

functional synaptic connections (Jaeger et al., 1994). Another

study using paired whole-cell recordings between fast-spiking

PV interneurons and SPNs found that PV cells form strong

GABAergic connections to SPNs (Koos and Tepper, 1999).

Together, these findings suggested that PV interneurons provide

a larger source of GABAergic inhibition to SPNs than SPN collat-

erals do, contrary to previous hypotheses based on the anatom-

ical data (Tepper et al., 2008).

In 2002, the first evidence of monosynaptic GABAergic con-

nections between SPNs was demonstrated by Tunstall and

colleagues, followed shortly by Czubayko and Plenz (Czubayko

and Plenz, 2002; Tunstall et al., 2002). Tunstall and colleagues

used sharp intracellular electrode recordings in adult rat striatal

slices and found that 9/45 pairs of SPNs formed unidirectional

synapses that were blocked by GABAA antagonists. In addition

to the sparse connectivity (20% of pairs), the synaptic responses

were of small size (IPSP amplitudes of a few hundred microvolts)

and showed high rate of failure. As a consequence, averaging

hundreds of events was required in order to resolve IPSPs,

possibly explaining the discrepancy with the previous study of

SPN pairs. Similarly, using young rats and organotypic cultures,

Czubayko and Plenz found further evidence for the existence of

functional synapses between SPNs. They found synaptic con-

nections between 5/38 (13%) of pairs of SPNs with high failure

rates (�0.5) in rat slices, in agreement with the connectivity rates

reported by Tunstall et al. IPSPs reported in Czubayko and Plenz

were an order of magnitude larger than those reported in Tunstall

et al. (�2 mV versus �250 mV), likely due to differences in the

recording configurations (sharp electrode versus whole cell).

Two years later, Taverna and colleagues showed that SPNs in

the NAc also form functional collateral synapses with each other

(Taverna et al., 2004). Like in the dorsal striatum, these connec-

tions are relatively sparse (13/38 pairs, 34%), mediated by

GABAA receptors, and mainly unidirectional in nature.

Properties and Organization of the Inhibitory Collateral

Transmission

The creation of BAC transgenic mice expressing fluorescent

proteins under the control of the Drd1 or Drd2 promotor (Gong

et al., 2003) allowed for the examination of collateral synapses

in identified dSPNs and iSPNs. In both the dorsal striatum

and the NAc, Taverna and colleagues found asymmetries in

both the connection probability and strength of the synapses be-

tween the SPN subclasses (Taverna et al., 2008). They found that

274 Neuron 96, October 11, 2017

while iSPNs form synapses on postsynaptic dSPN (13/47, 28%)

and iSPNs (14/39, 36%) with a similar probability, dSPNs were

much more likely to form synapse onto other dSPNs (5/19,

26%) than onto iSPNs (3/47, 6%). In addition, presynaptic iSPNs

generate larger inhibitory postsynaptic currents (IPSCs) than

dSPNs do. In similar experiments, Planert and colleagues found

slightly different connection probabilities between the sub-

classes but largely supported the finding that iSPNs are more

likely to form collateral synapses than dSPNs are (Planert

et al., 2010). Future studies will determine whether the asymme-

try in the connection strength and probability is observed in vivo.

These findings are important because they imply that the pattern

of collateral synapses between SPNs is organized in a specific

nonrandom fashion, and they suggest that there is an asymmetry

in the strength and probability of contacts among the sub-

classes.

Though new hypotheses emerged on how SPN collaterals

could shape information flow through the striatum (Plenz,

2003), the concept that PV interneurons are the main source of

GABAergic inhibition in the striatum persisted in the field (Tepper

et al., 2004, 2008). This was due in large part to the fact that PV/

SPN synapses are more likely formed on the SPN soma and

generate larger IPSPs than SPN /SPN synapses, which form

mainly on distal dendrites (Koos et al., 2004). As a result, many

broad theories and overviews of basal ganglia function do not

incorporate a possible role for SPN collateral synapses in influ-

encing basal ganglia output (Calabresi et al., 2014; Cazorla

et al., 2015; Sesack and Grace, 2010; Silberberg and Bolam,

2015). This is in large part because their function remains unclear

but also because collaterals are thought to be weak and infre-

quent. However, estimates suggest that there are >2,800 SPNs

within the dendritic field of a given SPN (Oorschot, 1996) and

thus, 28%–36% of them represent more than 790–1,000 likely

synaptic partners for each SPN. A clever analysis of synapses

in the striatum based on electron microscopy data and synaptic

density estimates that each SPN receives around 1,200 to 1,800

synaptic contacts from other SPNs, implying that lateral inhibi-

tion could strongly affect SPNs and thus shape output (Wil-

son, 2007).

Indeed, recent studies from our laboratory have demonstrated

an important functional role for SPN collaterals in controlling the

excitability of SPNs and locomotion. Simultaneous activation of

iSPN axon collaterals using channelrhodopsin caused notice-

able inhibition of dSPN firing (Dobbs et al., 2016), proving further

confirmation of the anatomical predictions. Important to note

with this study is that synchronous activation with channelrho-

dopsin in a slice does not necessarily recapitulate the in vivo

behavior of the network. However, these experiments do shed

light on what is possible given the existing synapses within the

striatal network. Altogether, the anatomical and functional find-

ings show that while connection probability between isolated

pairs of SPNs is relatively low, and it involves only a handful of

synapses, concerted activation of axon collaterals from multiple

connected SPNs can have a significant inhibitory effect on the

firing of their synaptic partners. Also, activation of D2Rs ex-

pressed in iSPNs suppresses the collateral GABA transmission,

and as such, dopamine and D2-like receptor agonists reduce the

inhibition from iSPNs to dSPNs (Dobbs et al., 2016; Lemos et al.,

Neuron

Perspective

2016). By controlling the degree of lateral inhibition from iSPNs to

dSPNs, D2Rs can gate dSPN action potential firing and output.

In animals with targeted deletion of D2Rs in iSPNs, neither eleva-

tion of endogenous dopamine by cocaine nor a D2-like agonist is

able to suppress GABAergic transmission from iSPNs /

dSPNs, and mice show impaired cocaine-induced locomotion

(Dobbs et al., 2016). In addition, animals lacking D2Rs in iSPNs

display hypolocomotion, bradykinesia, and reduced in vivo firing

of SPNs throughout the striatum. In slices prepared from these

animals, SPNs were shown to receive more potent inhibition

from GABA transmission, likely caused in part by an enhanced

collateral transmission that followed the chronic loss of D2R-

mediated suppression of iSPN collaterals (Lemos et al., 2016).

Taken together, these studies suggest an important functional

role for the network of collateral axons and synapses in regu-

lating the excitability of SPNs within the striatum and in gating

behavioral output of the basal ganglia. The behavioral implica-

tions of the lateral inhibition are multiple and seem to involve

locomotion and stimulant-induced activity.

Other groups have visualized SPN network activity using cal-

cium indicators both in vitro and in vivo to provide evidence

that SPNs are organized into neuronal clusters based on syn-

chronized activation that might represent functional ensembles

(Barbera et al., 2016; Carrillo-Reid et al., 2008; Carrillo-Reid

et al., 2011). In light of this, many computational models have at-

tempted to explain how feedforward inhibition, mediated by PV

cells, and lateral inhibition, mediated by SPN collaterals, could

generate these ensembles and thereby transform information

flow through the striatum (Humphries et al., 2009, 2010; Moyer

et al., 2014; Ponzi and Wickens, 2010, 2012, 2013; Yim et al.,

2011). These models suggest that lateral inhibition mediated

by SPN collaterals might play a role in driving ensemble coher-

ence on slower timescales or in synchronizing active ensembles

and suppressing unsynchronized cells. However, these models

all assume a random connection probability between SPNs

and deserve to be reconsidered in light of the recent evidence

of asymmetries in both strength and connection probability of

the collateral connections between SPN subclasses and the

possible existence of neuronal clusters or ensembles.

Evidence of Striatal Functional Units

An important question to ask is whether there exists a funda-

mental unit of information processing in the striatum. Though

not easily visualized, are there functional units throughout the

striatum analogous to the canonical cortical microcircuit

columns? A convergence of evidence suggests that ensemble

activity in the striatum is important for information transfer

through the region and the resulting behavior.

On one hand, little evidence of synchronized firing is found be-

tween SPNs during in vivo electrophysiological recordings in

anesthetized animals (Stern et al., 1998). This could speak

against the existence of ensembles but given the low firing rate

of SPNs in vivo, little spontaneous synchrony is expected. On

the other hand, in vivo recordings from freely moving animals

show greater evidence of synchronized firing among SPNs

(Miller et al., 2008). In behaving animals, SPN firing is seen

time locked to specific aspects of the behavioral task, suggest-

ing synchronized spiking of functionally related populations

(Adler et al., 2012; Barnes et al., 2005; Cacciapaglia et al.,

2011; Carelli, 2002; Jin et al., 2014; West and Carelli, 2016)

that are not necessarily in close proximity (Bakhurin et al.,

2016). Ensembles are likely formed by sparsely distributed rather

than tightly clustered neurons, and as such, the chances of

simultaneously recording their firing from the same electrode

array might be low. In support of this concept, a recent study

that used a large-scale recording approach found higher

correlation in the resting-state firing of neurons that were respon-

sive to reward predictive cue compared to non-responsive

neurons when investigating the overall firing of a large number

of striatal neurons during a Pavlovian reward conditioning task

(Bakhurin et al., 2016).

Fiber photometry recordings of SPNs in the NAc and dorsal

striatum also showed large transients of calcium concentrations

time locked to cues and specific behaviors during behavioral

tasks (Calipari et al., 2016; Cui et al., 2013; Natsubori et al.,

2017; Zalocusky et al., 2016), suggestive of population syn-

chrony during actions, though again not necessarily due to cells

in close proximity. Moreover, deep brain calcium imaging, which

provides awide field of view, shows clusters of SPNs that display

synchronized calcium transients during free locomotion. The

clusters are observed in both the dorsomedial and dorsolateral,

and they involve both dSPNs and iSPNs, which supports the

concept of striatal functional units where iSPNs and dSPNs

work together to produce a behavioral output (Barbera

et al., 2016).

Further, in the ventral striatum, which has long been hypothe-

sized to be organized into functionally distinct ensembles of cells

(Pennartz et al., 1994), further support for the idea that sparse en-

sembles of cells work together to control striatal behaviors

comes from the analysis of immediate-early gene expression.

For example, examination of immediate-early gene c-fos expres-

sion after NAc-dependent behaviors, such as context-based

cocaine-induced locomotor sensitization has suggested that

sparse ensembles of SPNs are necessary for behavioral expres-

sion (Hope et al., 2006). Lending support to this, causal manipu-

lations of c-fos expression or cell excitability in the sparsely

labeled cells that express c-fos during locomotor sensitization

or conditioned place preference has been shown to disrupt

that behavior (Koya et al., 2009; Tolliver et al., 2000).

How are these ensembles formed? Are the cells in the striatum

simply following cortical commands or does intrastriatal circuitry

play a role in their activity? The topographical organization of the

cortex is conveyed through its projections to the striatum and is

proposed to persist through the basal ganglia in segregated

information streams (Alexander et al., 1986; Dudman and Ger-

fen, 2015). Thus, cortical inputs could contribute to the creation

of discrete functional units in part defined by their inputs.

However, while cortical inputs are critical for generating spikes

in SPNs, it is unlikely that they are defining the units, based on

the current experimental evidence. Systematic tracing and map-

ping of cortical projections into the dorsal striatum has identified

29 distinct subregions, suggesting the existence of numerous

discrete zones of striatal processing (Hintiryan et al., 2016).

In congruence with the anatomy, the in vivo firing of neurons

in the dorsolateral striatum, which receives sensorimotor input,

correlates with specific body sensations, movements, or even

direction of movement of a specific limb (Carelli and West,

Neuron 96, October 11, 2017 275

Neuron

Perspective

1991; West et al., 1990). However, since cortical axons in the

striatum display a great deal of divergence and nearby SPNs in

general receive few shared cortical inputs (Kincaid et al., 1998),

it is unlikely that these inputs completely define striatal en-

sembles.

Furthermore, in vitro calcium imaging experiments have iden-

tified synchronized cell assemblies in the striatum active in the

presence of tonic excitatory input (Carrillo-Reid et al., 2008; Car-

rillo-Reid et al., 2011), demonstrating that glutamate is neces-

sary to generate activity in these cell assemblies. However,

blocking GABA abolishes much of the sequential activation

of the assemblies in the network, implying that local circuitry

within the striatum is important for the observed network

behavior of the cell assemblies. Note that similar sequentially

switching cell assemblies have been identified in the primate

striatum in vivo during behavior, suggesting that there is behav-

ioral relevance for the sequential activation pattern of the cell as-

semblies (Adler et al., 2012). Taken together, all these studies

support the idea that SPNs in the striatum are organized into

discrete functional units responsible for driving specific behav-

iors and that local striatal circuitry plays a role in their dynamics.

5. A Conceptual Model of Synaptic Organization of theLateral InhibitionIf indeed the striatum operates as a collection of functional units

that are responsible for driving specific behaviors, as the most

recent evidence seems to indicate, then how are these functional

units created and organized? What is the synaptic organization

of the functional units both within and between units that allow

for the selection of appropriate behaviors and other striatal func-

tions?We hypothesize that the lateral inhibition between SPNs is

critical in defining the functional units and that the organization of

the SPN collateral transmission within the striatum shapes this

lateral inhibition to define behaviorally relevant ensembles of

SPNs. This Perspective will mainly focus on the role of the

most abundant striatal neurons, SPNs; however, each functional

unit is likely to also contain interneurons that regulate activity

both within and between functional units.

In this section, we will use the current anatomical and

functional data on SPN collateral transmission to draw some

conceptual models and speculate how the connectivity pattern

of collateral transmission can bring functional relevance to the

lateral inhibition within the striatum in order to effectively aid in

the integration and filtering of inputs. A variety of theories on

striatal function emerged early on that incorporated the idea

that lateral inhibition shaped information flow through the

striatum (Parent et al., 2000; Smith and Bolam, 1990). Groves,

drawing on ‘‘winner-take-all’’ principles of lateral inhibition first

established in the retina, hypothesized that SPN collaterals proj-

ect to all their neighbors allowing the network to temporally and

spatially sharpen the broad input from the cortex and that dysre-

gulation in this system leads to movement disorders (Groves,

1983). Similarly, Penney and Young posited that lateral inhibition

between projection neurons (SPNs) served to suppress or main-

tain desired cortical input and hypothesized that the extent and

patterning of these axon collaterals may develop during acquisi-

tion of new motor and cognitive capacities (Penney and Young,

1983). Extending the idea that SPN collaterals sharpen and filter

276 Neuron 96, October 11, 2017

information into the ventral striatum, Swerdlow and Koob pro-

posed that in the NAc, a disruption of this circuitry, and the re-

sulting inability to filter emotional or cognitive inputs, could be

an underlying cause of psychiatric diseases like depression

and schizophrenia (Swerdlow and Koob, 1987).

Despite the fact that there is no easily visualized pattern of

SPN connectivity, several pieces of evidence point to an overall

structure: (1) the connection probability is asymmetrical between

SPN subclasses where iSPNs are more likely than dSPNs to be

connected to both dSPNs and other iSPNs, while dSPNs are

more likely to synapse on other dSPNs (Planert et al., 2010;

Taverna et al., 2008); (2) the synaptic contacts made by iSPNs

produce larger responses than those from dSPNs (Planert

et al., 2010; Taverna et al., 2004, 2008; Tunstall et al., 2002);

(3) connections are unidirectional in nature and sparse so they

do not just contact every nearby SPN (Planert et al., 2010;

Taverna et al., 2004, 2008; Tunstall et al., 2002); and (4) SPNs

synapse mainly onto distal dendrites of other SPNs, although

there is some diversity on location suggesting different types

of collateral synapses (Oorschot et al., 2013). These properties

suggest a more complex, nonrandom functional organization

of lateral inhibition in the striatum. They also argue strongly

against the idea of an all-to-all/winner-take-all network of lateral

inhibition in which the first, or most strongly activated, neuron is

able to inhibit all of its neighboring SPNs, like early models of

striatal function hypothesized (Groves, 1983).

The wiring diagram in Figure 4A illustrates a possible pattern of

connectivity between SPNs in an attempt to merge the current

findings at the behavioral level with the circuit level/anatomical

data. We know that (1) dSPN activation facilitates behavior, (2)

iSPN activation suppresses behavior, (3) both dSPNs and iSPNs

are active during behavior, and (4) there is collateral transmission

between SPNs. This supports a conceptual model in which each

behavior is controlled by a functional unit (FU, rectangles) con-

sisting of both dSPN and iSPN clusters (green and red circles),

where the dSPN cluster that drives the desired behavior (say,

behavior A) and the iSPN cluster that inhibits the competing

behavior (say, behavior B) are simultaneously active during a

task (dark colored circles), while the complementary clusters

are inactive (light colored circles) (Mink, 1996).

The model then incorporates recent evidence showing that

iSPN collateral transmission within the striatum is important for

setting the magnitude of the lateral inhibition onto dSPNs. It

proposes that the active cluster of iSPNs within FU2, which sup-

presses behavior B through long-range indirect-pathway projec-

tions (into the globus pallidus and ventral pallidum), also extends

intrastriatal collateral axons to inhibit the dSPN cluster (in the

same functional unit) and diminish its output through long-range

direct pathway projections to the midbrain, which would other-

wise promote the competing behavior B.

Paired recording data suggest that iSPNs are connected to a

third of nearby iSPNs, and so we hypothesize that the active

iSPN cluster in each functional unit also extends axon collaterals

that synapse onto the iSPN cluster in the competing functional

unit and therefore exerts lateral inhibition that limits any possible

inhibition of, in this case, the desired behavior A. Thus, under this

proposed organization of the collateral transmission, the active

iSPN cluster not only suppresses the activation of competing

A

B

Figure 4. Proposed Model of Organization of Striatal FunctionalUnits with Lateral Inhibition within and between Units(A) Colored circles represent multiple SPNs from each subclass that areactivated together during behavior forming a cluster or ensemble. Eachfunctional unit contains one dSPN cluster and one iSPN cluster. Clusters thatare active during execution of behavior A are highlighted and have strongoutput that enhances behavior A (green arrow) and suppresses competingbehavior B (red brake). Lateral inhibition from the active clusters further limitsactivity of the other silent dSPN and iSPN clusters.(B) Basic connectivity of the lateral inhibition within and between the twofunctional units described. Note that all clusters receive excitatory inputsas well. For simplicity, all interneurons are not included in this early concep-tual model.

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Perspective

behaviors but also suppresses the inhibition of the desired

behavior A.

With regards to the active dSPN cluster in FU1, the model also

incorporates the reported dSPN/dSPN collateral transmission

and the existence of weaker dSPN/iSPN transmission. The

model proposes that the active dSPN cluster in FU1 exerts

lateral inhibition onto the dSPN cluster in FU2 to further limit

the competing behavior B, and via the weaker connections to

the iSPN cluster in FU1, the lateral inhibition dampens the sup-

pression of the desired behavior A. In summary, the active

dSPN cluster in this FU1 promotes the desired behavior A via

the canonical long-range direct pathway projections, and, at

the same time via the axon collaterals that remainwithin the stria-

tum, it provides lateral inhibition within and across functional

units to limit competing behaviors and dampen the inhibition of

the desired behavior. This conceptual model will provide a

framework for future studies.

Recent work has shown the strength of local GABA transmis-

sion provided by iSPN axon collaterals can be modulated by

dopamine via activation of D2 receptors (Dobbs et al., 2016,

2017; Lemos et al., 2016). Acute activation of D2 receptors in

iSPNs suppresses the lateral inhibition onto dSPNs and chronic

loss of the D2 receptors in these neurons causes an increase in

the strength of the GABA-mediated tonic current on dSPNs,

which reduces the in vivo firing of SPNs and impairs locomotion

(Lemos et al., 2016). If we hypothesize that iSPN collaterals then

‘‘gate’’ dSPNs, and when collateral transmission is inhibited by

dopamine, dSPNs are better able to fire action potentials and

drive behavior (Dobbs et al., 2016).

How Can This Hypothesized Connectivity Pattern Aid in

the Processing of Inputs?

The impact that this proposed pattern of axon collateral trans-

mission has on the activity of the SPN clusters will have to be

determined and tested in future studies. To illustrate the possible

ways in which the connectivity patterns in this conceptual model

can aid in the processing and filtering of the excitatory inputs to

the striatum, we applied a standard conductance-based

formalism to the individual neurons and synapses that reflects

the main network properties rather than reproducing the under-

lying biology. In this simple model, all cells are identical and

receive identical inputs. We consider two patterns of synaptic

connectivity and determine how the different network scenarios

affect the firing of action potentials of the SPN clusters in the two

representative functional units.

In a model without lateral inhibition, each SPN cluster fires ac-

tion potentials at relatively high frequency in response to excit-

atory inputs, and the clusters fire in synchrony because the

inputs are identical and the cells are homogeneous (Figure 5A).

Addition of lateral inhibition with an all-to-all connectivity pattern

and equal strength among connections (symmetric connectivity

patterns) results in synchronous firing at a lower frequency

(Figure 5B). Weakening transmission from dSPNs to introduce

the experimentally reported asymmetry in the connectivity

strength among SPN subclasses results in non-trivial firing pat-

terns where iSPN clusters become more dominant (Figure 5C).

iSPN clusters fire in anti-phase bursts and dSPN clusters fire a

single spike at every cycle. While it is interesting to see that func-

tional units are taking turns, the low activity of SPN clusters will

not be conducive to the generation of motor behaviors. Going

back to the pattern of Figure 5B, removal of the crossed connec-

tions between the functional leaves the originally proposed orga-

nization of the lateral inhibition (structured, Figure 5D), which

produces synchrony in all clusters, similar to the firing pattern

observed in Figure 5B (with slightly lower frequency). This illus-

trates the different balances that can be operating in the network

to produce the same type of patterns. However, when these

structured connections are no longer symmetrical between

SPN subclasses, the firing patterns can change drastically. As

shown in the example of Figure 5E, the asymmetric structured

pattern can result in synchronized activation of dSPN and iSPN

clusters belonging to different competing functional units where

iSPN and dSPN clusters fire in phase, while the other clusters are

silent. In other words, some clusters are shut down while others

are activated in phase. Note that the excitatory inputs to the si-

lent SPN clusters are still present and have not changed, and

as such this shows that, at least in this conceptual model, the

lateral inhibition can indeed filter excitatory inputs to SPNs to

shut down the firing of a selected subset of iSPN and dSPN

Neuron 96, October 11, 2017 277

no connections

all-to-all symmetrical

7000 7100 7200 7300 7400 7500 7600 7700 7800 7900 8000time

Raster plots dSPN clusteriSPN cluster

FU1

all-to-all asymmetrical

structured asymmetrical

structured symmetrical

A

B

E

D

C

FU2

FU1

FU2

FU1

FU2

FU1

FU2

FU1

FU2

dSPN dSPN

dSPN dSPN

dSPN dSPN

dSPN dSPN

dSPN dSPN

iSPN iSPN

iSPN iSPN

iSPN iSPN

iSPN iSPN

iSPN iSPN

Figure 5. Diverse Connectivity Patterns forthe Lateral Inhibition Generate DiverseFiring PatternsLeft: diagram of the connectivity pattern for thelateral inhibition within and between the two fun-ctional units. Right: raster plot output from aconductance-based model of SPNs showing thefiring of action potentials in response to identicalexcitatory inputs for each SPN cluster (green fordSPNand red for iSPN clusters) for each functionalunit (top raster for FU1, bottom raster for FU2).(A) No lateral connections between cells.(B) ‘‘All-to-all’’ connections between cells withequal synaptic strengths.(C) ‘‘All-to-all’’ connections between cells withasymmetrical connection strengths (iSPN> dSPN).(D) Proposed organization of lateral inhibition be-tween and within units (‘‘structured’’) with equalconnection strengths.(E) ‘‘Structured’’ connections between cells withasymmetrical connection strengths (iSPN> dSPN).

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Perspective

clusters. This example illustrates how the lateral inhibition orga-

nized in the proposed ‘‘structured’’ fashion and with the asym-

metry in the connections can generate the predicted pattern of

co-activation of iSPNs and dSPNs that is observed in vivo and

that is conducive to activation of behavior A and inhibition of

behavior B. In this way, lateral inhibition can help the striatum

in its important task of selecting motor actions.

This model is meant to serve as a proof of concept that the

patterning of the lateral inhibition between SPNs would be able

to transform synchronous inputs to all cells into selective activa-

tion by a subset of SPNs, while silencing others. It is a simplified

model with a small number of cells created for illustrative

278 Neuron 96, October 11, 2017

purposes. Thus, it does not capture all

known aspects of SPN biology, most

importantly, as detailed earlier, the fact

that synapses from other SPNs are

formed mainly onto the distal dendrites.

Dendriticmodeling work suggests that in-

hibition in distal dendrites can exert

powerful shunting of excitatory inputs

synapsing on dendrites, as glutamate in-

puts to SPNs tend to do (Gidon and Se-

gev, 2012). However, it is unlikely that a

single SPN in vivo is capable of suppress-

ing spikes in other SPNs. In order for the

computation illustrated with the model

to occur in vivo, there would likely need

to be some degree of synchronized acti-

vation of axon collaterals, which we

have recently demonstrated can shunt

action potential firing (Dobbs et al.,

2016). Thus, synchronized firing of en-

sembles would likely be needed to affect

firing. How reasonable is that assumption

based on the data? As discussed previ-

ously (see Evidence of Striatal Functional

Units), in vivo electrophysiology and

calcium imaging data indicate evidence

for synchronized firing of SPNs during specific aspects of

behavior. Though cells in close proximity might not always fire

in synchrony (Stern et al., 1998), given the broad reach of SPN

axons and dendrites, these units need not consist of cell bodies

in close proximity. Indeed, the model predicts that SPNs that are

co-active are not connected by collateral synapses and thus co-

active SPNs could be spaced further beyond the region of over-

lap between their axonal arborization and the dendritic field.

Considering the sparse, disparate pattern of cells that express

immediate-early genes during behaviors like context-induced

cocaine locomotor sensitization and the wide spatial distribution

of correlated, task-responsive neurons (Bakhurin et al., 2016), it

Neuron

Perspective

seems likely that the cells in these units would, in fact, not be in

close proximity (but note Barbera et al., 2016).

Another possibility that has been suggested (Koos et al., 2004;

Plenz, 2003) is that local collaterals of SPNs are important for

controlling transition to depolarized, plateau ‘‘upstates,’’ which

prime SPNs for firing, rather than affecting the spiking itself. As

SPN collaterals synapse mainly on distal dendrites (Oorschot

et al., 2013), and plateau upstate potentials are generated by

regenerative events in the distal dendrites (Plotkin et al., 2011),

GABA released from SPN could provide sufficient shunting to

prevent this transition. Indeed, in vitro studies show that GABA

antagonists prolong SPN upstate duration, suggesting that

GABA inputs play a role in upstate timing (Vergara et al., 2003).

By controlling the upstate transitions that precede spiking and

bursting, as well as directly shunting excitatory inputs, inhibition

in the dendrites would still provide a powerful means of effecting

cell output (Gidon and Segev, 2012; Lovett-Barron et al., 2012).

Thus, the local SPN synapses between and within units could be

controlling the excitability of neighboring cells in the way pro-

posed by the model, without necessarily directly suppressing

spiking.

Open Questions

This proposed model is intended as a preliminary proposition to

stimulate discussion and research regarding how local circuitry

within the striatum might be organized given known functional

and behavioral data. As a result, there are many open questions

regarding this framework. Howmany cells do these units consist

of? How much overlap is there between units? How might these

units arise? Are they fixed developmentally, completely plastic

based on learning, or somewhere in between? Given the known

role of the striatum in certain forms of learning and the numerous

examples of plasticity in the region, it is likely that any organiza-

tion in the region is malleable. Dopamine release during behavior

could modulate the strength of the lateral inhibition to alter the

connectivity pattern in ways that it favors the activation of

selected FUs and the inhibition of others to ultimately raise the

signal-to-noise ratio and reinforce desirable behaviors. To what

degree is this the case, and are certain units more flexible and

plastic than others? Furthermore, how would the various inter-

neurons and their numerous known synapses detailed earlier

fit within this functional unit framework?

Concluding RemarksGiven the growing diversity of striatal inputs, cell types, and

connections described in recent years, it is important to develop

a framework for how the circuitry within the striatum is wired to

drive processing of information and affect output. Here we pre-

sent a hypothesis on how one aspect of the striatal circuitry, the

lateral inhibition between SPNs, could be organized given avail-

able data. It is our hope that this will drive the future work needed

tobothdetermine the validity of thismodel and to understandhow

other elements of striatal circuitry could fit within this framework.

ACKNOWLEDGMENTS

This work was supported by the Intramural Programs of National Institute onAlcohol Abuse (ZIA-AA000421) and Alcoholism and National Institute ofNeurological Disorders and Stroke Grant (ZIA-AA000421) to V.A.A. and bythe National Science Foundation (DMS-1313861) to H.G.R. We are grateful

to Dr. Miriam E. Bocarsly and the other members of the Alvarez laboratoryfor their valuable comments on this Perspective.

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