Tracking burst patterns in hippocampal cultures with high-density CMOS-MEAs

IOP PUBLISHING JOURNAL OF NEURAL ENGINEERING

J. Neural Eng. 7 (2010) 056001 (16pp) doi:10.1088/1741-2560/7/5/056001

Tracking burst patterns in hippocampalcultures with high-density CMOS-MEAs

M Gandolfo1, A Maccione2, M Tedesco1, S Martinoia1,2 andL Berdondini2

1 Neuroengineering and Bio-nano Technology Group, Department of Biophysical and Electronic

Engineering (DIBE), University of Genova, Vai Opera Pia 11a, 16145 Genova, Italy2 Department of Neuroscience and Brain Technologies, Italian Institute of Technology (IIT),

Via Morego 30, 16163 Genova, Italy

E-mail: [email protected]

Received 15 February 2010

Accepted for publication 8 July 2010

Published 18 August 2010

Online at stacks.iop.org/JNE/7/056001

Abstract

In this work, we investigate the spontaneous bursting behaviour expressed by in vitro

hippocampal networks by using a high-resolution CMOS-based microelectrode array (MEA),

featuring 4096 electrodes, inter-electrode spacing of 21 µm and temporal resolution of 130 µs.

In particular, we report an original development of an adapted analysis method enabling us to

investigate spatial and temporal patterns of activity and the interplay between successive

network bursts (NBs). We first defined and detected NBs, and then, we analysed the spatial

and temporal behaviour of these events with an algorithm based on the centre of activity

trajectory. We further refined the analysis by using a technique derived from statistical

mechanics, capable of distinguishing the two main phases of NBs, i.e. (i) a propagating and

(ii) a reverberating phase, and by classifying the trajectory patterns. Finally, this methodology

was applied to signal representations based on spike detection, i.e. the instantaneous firing

rate, and directly based on voltage-coded raw data, i.e. activity movies. Results highlight the

potentialities of this approach to investigate fundamental issues on spontaneous neuronal

dynamics and suggest the hypothesis that neurons operate in a sort of ‘team’ to the

perpetuation of the transmission of the same information.

S Online supplementary data available from stacks.iop.org/JNE/7/056001/mmedia

(Some figures in this article are in colour only in the electronic version)

1. Introduction

In the last 15 years, multi-electrode arrays (MEAs) have

been shown to provide a suitable experimental framework in

which network dynamics, plasticity, learning and information

processing can be studied in vitro, under well-controlled

environmental conditions and for prolonged periods of time.

Featuring multi-site recording and stimulation capabilities,

MEAs are an enabling technology for electrophysiological

measurements of different types of neuronal preparations, such

as dissociated cultures, acute and organotypic slices. These

devices are used to investigate basic mechanisms of learning

and memory (Jimbo et al 1999, Eytan et al 2003, Marom

and Eytan 2005, Baruchi and Ben-Jacob 2007), to study

complex signal behaviour and processing (van Pelt et al 2005,

Chiappalone et al 2006, Wagenaar et al 2006) or to screen

pharmacological agents (Chiappalone et al 2003, Pancrazio

et al 2003, Stett et al 2003). More recently, these systems

have been employed to simulate sensory-motor closed loops to

study neuronal information encoding strategies targeting new

neuroprosthetic solutions (DeMarse et al 2001, Schwartz et al

2006, Novellino et al 2008). Within this context, neuronal

population dynamics is usually investigated by considering

the timing and position of spike occurrences. Afterwards, first

and higher statistical moments of the temporal distribution are

applied to explore neuronal network functionalities (Bonifazi

1741-2560/10/056001+16$30.00 1 © 2010 IOP Publishing Ltd Printed in the UK

J. Neural Eng. 7 (2010) 056001 M Gandolfo et al

et al 2005, Eytan and Marom 2006, Pasquale et al 2008,

Raichman and Ben-Jacob 2008, Garofalo et al 2009).

In vitro cortical and hippocampal neurons coupled to

MEAs typically exhibit, after the first week of culture,

brief periods of intense activity, where spike count increases

temporally (rate) and spatially (number of involved electrodes)

with respect to the initial conditions (Maeda et al 1995, Segev

et al 2001, Streit et al 2001, van Pelt et al 2004b, Wagenaar

et al 2006). In the literature, such quasi-synchronized firing

episodes have been widely studied for their relevance; they are

called barrages (Wagenaar et al 2004), network spikes (Eytan

and Marom 2006), population bursts (Giugliano et al 2004,

Wagenaar et al 2005) or synchronized bursting events (Segev

et al 2004). Here, following the definition given in van Pelt

et al (2004a) we will refer to these events as network bursts

(NBs). For instance, NBs are believed to play an important

role in the network formation (Corner et al 2002) and are

a fundamental indicator of the culture status, such as age,

health or chemically induced effects (Corner and Ramakers

1992, Gross et al 1995, Kamioka et al 1996, van Pelt et al

2004b, Chiappalone et al 2006). Moreover, NBs seem to

be involved in the low-frequency rhythms appearing during

slow-wave sleep (non-REM sleep), which is thought to be

responsible for memory consolidation of neural information

acquired during wakefulness (Steriade and Timofeev 2003,

Dan and Boyd 2006, Steriade 2006, Tononi and Cirelli 2006,

Corner et al 2008).

Usually lasting for hundreds ofmilliseconds and recurring

every few seconds, NBs are characterized by a phase of

increasing activity, which reaches a widespread and intense

peak on a firing pattern, followed by a relative long-lasting

phase of activity, which decreases and finally ends in a

refractory silent phase (van Pelt et al 2004a, 2005). Even

if this general description is commonly accepted, bursting

behaviour exhibits extremely varied patterns both in terms

of NBs’ timing distribution (Wagenaar et al 2006) and,

especially, in terms of spatial–temporal firing dynamics among

individual NBs (van Pelt et al 2004b). This latter phenomenon

cannot be investigated with conventional MEAs due to spatial

undersampling and the limited number of recording sites (i.e.

typically, the pitch is 200 µm for 60–100 electrodes) (van Pelt

et al 2005).

The recently introduced high-density MEA technologies

(Eversmann et al 2003, Lambacher et al 2004, Berdondini

et al 2005b, Heer et al 2007) are certainly very attractive

for studying NBs dynamics since they allow us to resolve

in detail the spatial–temporal patterns. In particular, we

used the active pixel sensor microelectrode array (APS-MEA)

technology introduced in Berdondini et al (2002), providing

4096 square electrodes (21 µm) arranged in a 64 × 64 grid

with inter-electrode separations of 21 µm. The high spatial

resolution combined with a sampling rate of 7.7 kHz per

channel (at full frame resolution) enables a multi-dimensional

access to the network activity, i.e. ranging from cellular to

network levels (Berdondini et al 2009a). This permits the

precise spatial–temporal localization of signals at the micro-

scale while allowing the observation of the collective network

behaviour. Moreover, in addition to conventional visualization

of single channels and analysis based on spike identification,

the APS-MEA platform enables visualization and analysis

approaches derived from the image/video field.

In this paper, taking advantage of the APS-MEA platform

performances, we focus on the development of analysis

tools for describing spontaneous spatial–temporal patterns

and for identifying and classifying NBs. The relevance of

this approach is also motivated by the recently presented

work on NB motifs (Raichman and Ben-Jacob 2008) and

their involvement in neuronal coding (Baruchi and Ben-

Jacob 2007). In particular, here we address specific issues

aimed at (i) identifying NB initiation and propagation and

(ii) quantifying the repeatability of the observed patterns.

Thus, by implementing a spatial–temporal representation

of neuronal network dynamics as activity movies (i.e.

each frame is a pseudo-colour map representation of the

electrophysiological voltage signals sensed on the whole

electrode array), it is possible to obtain a detailed view

of activity patterns (Maccione et al 2010). Furthermore,

this high-resolution time-lapse imaging approach allows, for

the first time and to the best of our knowledge, clearly

distinguishing the different activity dynamics involved within

and between NB events. Indeed, it is possible to observe

leading igniting channels aswell as the progressive recruitment

of the network and the articulated wave-like diffusion

pathways. This recruitment process reaches a maximum in

terms of activated channels, and then gets into a transitional

phase where the array’s channels activate and deactivate

repeatedly. While the former process seems to be related to

a diffusion phase in which neurons fire consecutively within

a particular pathway or chain (propagating phase), the latter

phase seems to reflect a kind of excited status in which a large

number of neurons keep on firing as more decoupled and,

apparently, random events (reverberating phase).

The propagation profile of NBs has already been

successfully used to characterize NBs. For instance, in

Raichman and Ben-Jacob (2008) the authors focus on the

first recorded spike at each electron spike, thus reducing the

involved variables to depict the activation profile (motif) and

then deriving a classification of NBs. Here, we propose to

use an improved methodology based on the centre of activity

trajectory (CAT) (Chao et al 2005, Chao et al 2007) to

identify NB propagations at the whole network level. In

particular, improvements were required to distinguish the

different dynamics involved in the NBs, i.e. the propagating

and reverberating phases. Thus, to evaluate the time-switch

between the two phases, we investigate methods derived from

statistical mechanics that allow estimating the correlation

coefficients during the CAT’s evolution.

Furthermore, the results achieved by computing the CATs

based on spike data (i.e. ‘digital data’ represented by time

stamps of the detected spiking activity) are compared with

a new approach directly based on the voltage-coded activity

(i.e. ‘analogue data’ represented by activity movies). This

second method is particularly suitable for high-resolution

MEAs and allows reducing the computational cost required

for this analysis by avoiding spike detection.

2


Figure 1. APS-MEA platform and image/video generation scheme. Schematic overview of the fundamental blocks involved in thegeneration of functional pseudo-colour images and activity movies representative of the recorded activity. Signals coming from the 64 ×

64 high-density electrodes–pixels are multiplexed on 16 analogue channels and sent to an FPGA where conversion to digital format andpre-processing is performed. Data are then arranged in a CameraLink format and sent to a frame grabber on the host PC and finally storedand/or visualized through pseudo-colour images/videos. Dark filled arrows indicate hardware steps, while the open arrow indicates asoftware process. An example of a still pseudo-colour image (single frame) generated by converting the recorded potential can be seen for aNB recorded from hippocampal neurons. By observing these images in a video sequence, one can recognize activity patterns propagation.

2. Materials and methods

2.1. Hippocampal neuronal cultures

The APS-MEA devices were sterilized in ethanol 80% for20 min, rinsed in sterile water and dried under laminar flow.Successively, devices were coated with laminin (0.1 µg µl−1;Sigma L-2020) (3 h), poly-D-lysine (0.1 µg µl−1; SigmaP-6407) (overnight) and then rinsed again with sterilizedwater. Principles of laboratory animal care of the EuropeanCommunities Council (86/609/EEC) were followed for thepreparation of dissociated neuronal cultures. Primary cultureswere obtained from the brain tissue of Sprague Dawley rats atembryonic day 18 (E18). Briefly, embryos were removedand dissected under sterile conditions. Hippocampi weredissociated by enzymatic digestion in Trypsin 0.125%—20 min at 37 ◦C—and finally triturated with a fire-polishedPasteur pipette. Dissociated neurons were plated on theactive area of the APS-MEAs by using drops of variousvolumes of cell suspensions, ranging from 25 to 30 µl at acellular concentration of 700 cells µl−1. The obtained finaldensities were between 2500 and 3000 cells mm−2. One hourlater, when cells adhered to the substrate, 1 ml of mediumwas added to each device. The cells were incubated with1% Glutamax, 2% B-27 supplemented neurobasal medium(Invitrogen), in a humidified atmosphere 5% CO2, 95% airat 37 ◦C; 50% of the medium was changed every week.No antimitotic drug was used, because application of serum-free medium limits the growth of non-neuronal cells (Araqueet al 1999). Spontaneous electrical activity recordings wereperformed starting from the third week of cell culturing byusing a small metallic box surrounding the APS-MEA and fedwith a continuous stream of humidified airflow with 9% CO2(Keefer et al 2001). Before starting an experimental sessionwe waited for about 30 min to let the cultures stabilize afterremoval from the incubator (Streit et al 2001).

2.2. High-density MEA platform

A comprehensive description of the APS-MEA technologyand of the high-density platform used for our experiments

can be found in Berdondini et al (2005a, 2005b) and Imfeld

et al (2008). Here, we briefly summarize the main concepts

and features provided by this platform. Electrophysiological

signals are amplified and filtered on-chip and successively

multiplexed to reduce the number of output channels. An

external FPGA interface provides the analogue-to-digital

conversion, real-time pre-processing features and a serializer

used for sending the data to a host PC through a high-speed

CameraLink interface (figure 1). A full frame acquisition

consists of 4096 electrodes (21 µm per side) arranged in a

64 × 64 grid and tightly packed (pitch of 42 µm).

When acquiring at full frame, the sampling frequency per

microelectrode is 7.7 kHz, which sets a sufficient temporal

resolution for resolving activity propagations between adjacent

electrodes (Berdondini et al 2009b). Electrophysiological

activity can be displayed live, as voltage versus time for

selected single channels and as image sequences (activity

movies) representing the whole array activity levels coded

in colour maps.

Given the large number of microelectrodes and a

resolution of 12 bits per electrode, this platform generates

a large number of data (about 35 GBytes for 10 min of

recording at 7.7 kHz). A high-performance ad hoc software

tool (Maccione et al 2008) was developed for managing the

acquisition and for speeding up the data processing with both

conventional and novel algorithms. Standard analysismethods

include first-order statistics based on the identification of

events such as spikes or bursts and the analysis of their

temporal distribution (Vato et al 2004). New analysis methods

are aimed at describing the firing dynamics with a time-lapse

imaging approach. Both strategies have been used to depict

NBs as propagating patterns and results have been compared

in order to assess the reliability of the novel technique.

2.3. Network burst detection and analysis methods

2.3.1. Spike detection. APS-MEAs achieve low-noise

performances (11 µVrms) and exhibit good signal-to-noise

ratio (SNR) levels (Imfeld et al 2007, Berdondini et al 2008).

However, they present slightly variable noise levels among

3


channels and over time due to mismatch in the integrated

electronic circuits and due to a constant calibration procedure

aimed at managing dc offsets. Hence, spike detection was

performedwith amodified version of the PTSD (precise timing

spike detection) algorithm presented in Maccione et al (2009)

to take into account noise variability. Briefly, before starting

with the spike detection, the noise is pre-evaluated on each

electrode channel by considering three randomly selected time

windows (TWs) of 100 ms each. The standard deviation (SD)

is computed over each TW and the lowest value is retained,

thus excluding TWs of possible intense spiking activity. It

should be noted that the probability of picking up a burst (i.e.

tightly packed spiking activity) in all three selected TWs is

less than 3% for a bursting rate of 1 s−1 and a burst duration

of 300 ms.

Successively, the algorithm scans the full raw data of each

electrode for local maxima and minima and assigns, within a

period of 2 ms, a spike to each pair of extremes whose peak-

to-peak amplitude is above a threshold set to 7.5 times the SD.

The evaluation of the noise is constantly updated during the

scanning of the raw data. Indeed, spike-free raw values are

continuously collected and feed a buffer of 100 ms. As soon as

the buffer is filled up, its values are used in order to re-calculate

the noise at step i. The noise estimation is therefore updated

by the following: vc(i) = αupvc(i)+ (1−αup)vc(i −1), where

vc(i) is both the mean and the SD value of the noise at step i

for channel c and αup is an updating rate constant that has been

experimentally adjusted to αup = 0.2.

The SNRmean values for each experimentwere computed

as the mean of the SNR values found for each channel as

follows:∑

nc10 log10

(

Ai

6·SDi

)2

nc, (1)

where nc is the number of identified spikes at channel c, Ai is

the peak-to-peak amplitude of the ith identified spike and SDi

is the SD of the noise computed in a TW preceding the ith

spike.

Spike sorting was not applied to our data since the main

topic of this work consists in describing neuronal activity

during NB events and their propagation patterns are not

affected by multi-unit signal recordings (Eytan and Marom

2006).

2.3.2. Network burst detection. The most evident properties

of NBs are the spike densities that result in shorter inter-spike

intervals (ISI) when compared with quiescent periods, and the

recruitment of many electrode sites. Thus, commonly adopted

algorithms for NB detection involve criteria derived from

the single channel burst detection by considering the inter-

spike time intervals (e.g. Cocatre-Zilgien and Delcomyn 1992,

van Pelt et al 2004b, Eckmann et al 2008) and occasionally

completed by including criteria on the number of recruited

channels (Chr) (van Pelt et al 2004b, Chiappalone et al 2005).

In our approach we opted for a modified version of NB

detection as described in Eckmann et al (2008). Briefly, first

the timing of the spike events of all channels is collapsed into

a summed train representing the global activity. Secondly, the

algorithms look into this summed activity trace for the disjoint

portions featuring consecutive tightly packed spikes whose

time distance is below a threshold defined by the user (i.e.

maximum ISI value; ISImax). Finally, a portion of the summed

activity trace is elected to be a NB whether the number of

recruited channels exceeds a given threshold (nr). The NB

is consequently defined to start at the temporal and spatial

position defined by the first firing channel, followed by all

the spikes within the defined ISImax. Under our experimental

conditions we found that ISImax = 50 ms is appropriate to

identify rapidly propagating burst events, while nr set to, at

least, 15% of the active channels, avoids detection of false

positive NBs (e.g. due to accidental spike proximity in the

whole array). Afterwards, the firing rate histograms inside

NB regions were computed by using consecutive time bins of

10 ms and the NB firing peak was settled to the centre of the

bin, where the number of counted network-wide spikes was

maximum.

2.3.3. Centre of activity by means of spiking activity.

To identify spatial–temporal propagations based on detected

spiking events, we used the concept of CAT introduced inChao

et al (2005). The centre of activity (CA) is analogous to the

centre of mass as it accounts for spatial activity distribution

during subsequent instants of the activity flow. However, a

limitation of this method is that consecutive movements of

CA represent the propagating activity well only if NBs involve

single ignition sites.

We assume that ATW(k) represents the number of spikes

at active electrode channels k within a small TW, and Col(k),

Row(k) are the column number and the row number of channel

k respectively. The value of CA at time t is a vector whose

components are defined as

→

CA = [CAx,CAy]

=

∑nak=1 ATW(k) · [Col(k) − Rcol,Row(k) − Rrow]

∑nak=1 ATW(k)

, (2)

where Rcol and Rrow are the coordinates of the physical centre

of the electrode array and na is the total number of active

channels. The trajectory between two reference time positions

t0 and t1 with a time resolution 1t is consequently (see also

figure 4)

→

CAT(t0, t1)

= [→

CA(t0),→

CA(t0 +1t),→

CA(t0 + 21t), . . . ,→

CA(t1)]. (3)

2.3.4. Centre of activity by means of activity movies. The

APS-MEA platform allows us to capture electrophysiological

signals as a sequence of frames, i.e. activity movies (Imfeld

et al 2008). In this respect, the concept of the CAT for

identifying spatial–temporal propagations can be modified

for its direct computation on activity movies, thus without

explicitly requiring spike detection. Indeed, definition (2) can

be modified by substituting the information on spikes count

(ATW) with the measure of the voltage amplitude that signals

can achieve within a TW (PTW). As previously presented

(Berdondini et al 2009a), we found that the maximum

4


difference between voltage amplitude values (signal variation)

along the integration TW works well.

By using a voltage coding for computing the CAT, all

electrodes–pixels contribute to the CA temporal position

with values in a quasi-continuous scale (i.e. limited by the

quantization step), instead of the roughly discrete values due

to the spike count (cf section 2.4.3). With this approach,

the specificity of the CAT is preserved with the advantage

that small and local sub-threshold fluctuations, that are not

recognizable upon spike detection, can still contribute to

CA (see figure 3, first two rows). Such information can

be enhanced by using an exponential scale on the voltage

values thus positively affecting the computation of the activity

centre in the direction of the signal propagation. Finally, to

reduce the influence of statistically uncorrelated background

activity (i.e. noise), a low threshold (LT) has been introduced

in order to increase the efficacy of the ‘analogue’ activity

movies (figure 3, last row). In other words, this threshold

allows the number of considered channels to be reduced by

removing those presenting a signal variation below LT times

the maximum range. According to the above observations,

equation (2) has been changed to

→

CA(t) = [CAx,CAy]

=

∑nk=1 PTW(k) · [Col(k) − Rcol,Row(k) − Rrow]

∑nk=1 PTW(k)

, (4)

where PTW is

PTW(k) =exp

(

diffLTTW(k)/

c)

exp(M/c)· M. (5)

M is the full-scale value chosen, diffLTTW(k) is the highest max–

min difference of the signal computed on a TW at the channel

k and ranging between LT and M; c is a constant defining the

rate of change that was experimentally fixed to 1/5 of M.

2.4. Activity pattern diffusion analysis

With the aim of defining the timing at which NBs get into the

reverberating phase, we analysed CATs as two-dimensional

random walks. Thus, we applied the concepts developed

in statistical mechanics for deriving scaling factors able to

identify CATs with long-range correlations, as should be

expected for the propagating phase. Random walk models

and stochastic methods, used to test long-range power-

law temporal correlations, have been applied to investigate

the dynamic nature of different non-stationary physiological

systems and phenomena (Gerstein andMandelbrot 1964, Peng

et al 1994, 1995, Bhattacharya et al 2005). In a comprehensive

study, Collins and De Luca (1992) described a generalized

methodology, conventionally referred to as stabilogram

diffusion analysis, to derive the statistical properties of centre-

of-pressure (COP) trajectories collected with force platforms

during human quiet standing. Given the analogy of COP with

CAT data, both in terms of dynamical properties and space-

bounded type measure, we adapted this method to our specific

application.

At first, CATs were undersampled at 1 kHz and then

decomposed (Collins and De Luca 1994) to obtain time series

for column and row increments representing the electrode

position in the array. Each of these realizations can be thought

as a fractional Brownian motion (fBm) (Mandelbrot and Van

Ness 1968), i.e. a generalization of the ordinary Brownian

motion taking into account also the dependences on step

sequences of the random process. Under this condition, the

mean square displacement 〈1j 2〉 between all pairs of points

of the time series is related to the time interval defined by

〈1j 2〉 ∼ 1t2H , (6)

where j = row, column, H is the Hurst exponent ranging

within the interval [0, 1] and angled brackets denote the

average among all pairs separated by 1t (i.e. by m points

in the time series made of N points; N > m). The mean

square displacement can be expressed as follows:

〈1j 2〉1t =

∑N−mi=1 (1ji)

2

(N − m). (7)

H gives a measure of the degree of correlation for a fBm

being directly related to the correlation function C that in turn

quantifies the similarity between past increments and future

increments. The correlation function is defined as

C = 2 · (22H−1 − 1). (8)

H = 0.5 is the reference value for the classical Brownian

motion or pure random walk (Collins and De Luca 1992)

denoting that each step is independent of the previous one;

H > 0.5 denotes a positive correlated random walk (C > 0),

and H < 0.5 indicates a negative correlation (C < 0) for past

and future increments. By summing the displacements over

rows and columnswe obtain the planar displacement r (Collins

and De Luca 1995).

H is computed on planar displacements by substituting

j with r in relation (6) and it gives an indication of the type

of trajectory depicted by the two-dimensional random walk:

H > 0.5 implies persistence, i.e. the CAT on average

keeps the same direction, while H < 0.5 implies anti-

persistence, i.e. the CAT tends to fold and thus suppresses the

diffusion.

〈1r2〉 values are computed over the entire set of identified

NBs. An activity pattern diffusion plot (APDP) (by analogy

with the stabilogram diffusion plot) is obtained by estimating

〈1r2〉 values through equation (6) and by constructing a log–

log plot of 〈1r2〉 versus 1t. In order to avoid uncertainty on

〈1r2〉 values due to the underestimation of its variability, we

limited their estimation up to m = N/3, that is one-third of

the longest CAT. This choice derives from equation (7) that

states less square displacements to average upon, as increasing

values of 1t are selected. Thus, the H parameter can easily

be estimated as half of the slope of the best fitting line for the

obtained plot. Aswas found in all our experiments, the double-

logarithmic plot shows two different types of behaviour: (i) a

short-term region with a higher slope and a scaling exponent

value characteristic of positively correlated random walks

(H > 0.5); and (ii) a long-term region with a lower

slope indicative of negatively correlated random walks

(H < 0.5).

Nevertheless, after a careful observation of our trajectories

extracted from our experimental data, we observed that a

5


correct estimation of the time-switch between the propagating

phase and the reverberating phase was not always correctly

identified by the medium point used for identifying the change

in slope in the log–log plot. Indeed, calculation of planar

displacements over the entire CATs produces spurious results,

i.e. CAT points from the propagating phase that tends to

progressively increment their displacement are taken together

with those of the reverberating phase. Moreover, it should

be noted that CATs show a single slope only within the first

milliseconds of the NB. Thus, with the objective of looking for

a reliable and general method for the time-switch estimation,

we introduced a correction in this approach. CATs were

truncated at incremental time distances from the beginning

of the NB. Then, for each sequence, we constructed the APDP

to be approximated with a linear fitting and we computed

the root-mean-square error (RMSE). The plot of the RMSE

values against the CAT truncating time (tcut) shows, for the

first part of the curve, low values, expression of a single

slope APDP, and then increasingly higher values due to the

onset of the anti-persistence characteristic slope in the APDP

plot (see figure 5). This behaviour is maintained up to an

asymptotic error value given by the coexistence of the two

slopes. Therefore, we considered the time-switch point (t∗cut)

as the time indicating a deviation from the linear propagation

and corresponding to the instance when the RMSE starts to

increase.

2.5. Evaluation of voltage-based CAT performance

2.5.1. Classification of CATs. CATs computed on spike-

timing and on activity movies were calculated up to the time-

switch point related to each experiment, by using a TW of

20 ms and a sampling rate of 250 Hz. The obtained CATs

were then separately classified according to their shape. The

k-means algorithm and the silhouette coefficient introduced by

Kaufman and Rousseeuw (1990) (Matlab ©) were used to test

grouping efficiency for a number of clusters varying between

2 and 10. Practically, for the classification we followed the

methodology reported in Chao et al (2005) with the only

exception of introducing a strategy to take into account CATs

of different lengths. For this, we opted for interpolation instead

of padding shorter trajectories with zeros. Thus, for each CAT

ensemble the longest trajectory was selected and its number of

points was taken as reference. Based on these data, the column

and row time series of overall CATswere first interpolatedwith

spline functions and then recombined to form equally long

trajectories. This method allows grouping CATs of similar

shape, but featuring slightly different lengths. Finally, in order

to improve the classification results, outliers were identified by

calculating the relative Euclidean distance from the centre of

mass of the corresponding cluster. A threshold of three times

the SDof the cluster distributionwas used to detect the outliers.

2.5.2. Likeness of classification results. The set of CATs

computed from activity movies (i.e. voltage-coded data) was

compared with the set of CATs derived from spike time

firing dynamics. Since the computed trajectories can differ

between the two techniques, the above classification method

was separately applied to both sets of trajectories and a likeness

factor was defined in order to evaluate the degree of matching

between the classification outputs.

Given N observations and two independent sets Ä and Ä′

that group together the observations in k and h subsets so that

Ä = {S1, S2, . . . , Sk} and Ä′ = {S ′1, S

′2, . . . , S

′h}, it is possible

to construct a k × h heat map matrix where each element mi,j

represents the number of matching observations between the

two subsets Si and S ′j :

mi,j = #(Si ∩ S ′j ); 1 6 i 6 k; 1 6 j 6 h. (9)

The degree of matching between the two sets is related to

the sum of the heat map values belonging to a permitted

combinationCp that is a combination in which two heat values

cannot belong both to the same row and to the same column.

Indeed, a subset from any set can be related to one and only one

subset in a second set. The number of permitted combinations

is given by

Np =max{k, h}!

|k − h|!. (10)

The addition among the heat values of a permitted

combination, gives a heat score sp that represents the number

of paired observations across the two sets. Thus, the likeness

factor Ŵ can be defined as the maximum heat score normalized

by the number of observations:

Ŵ =

maxp

{∑

Cpmi,j

}

N=

smax

N. (11)

The likeness factor varies from 1/k and 1 as can easily be

derived for exact matching clustering. The former situation

comes out when the intersection between sets is minimized,

i.e. the observations are equally distributed both in Ä and in

Ä′. In such a case, having for instance h < k, each subset Si

would contain N/k observations, while each subset S ′i would

share N/(k · h) observations with S1, N/(k · h) observations

with S2 and so on. Thus Ŵ would be

Ŵ =smax

N=

Nh·k

· h

N=1

k.

3. Results

The results of this work are based on a dataset acquired from

seven hippocampal cultures recorded for 10 min (except for

experiment 7 that lasted 5 min) between 18 and 32 DIVs

(table 1). All the preparations showed the typical behaviour

constituted by broad and repetitive NBs. Upon spike detection

with a threshold equal to 7.5 times the SD of the noise, we

found that the mean SNR values (ranging between 2.7 and

3.1 dB; see section 2) are consistent with those (2.6–3 dB)

found for similar hippocampal cultures on standard MEA

(Multichannel System, Reutlingen, Germany). Afterwards,

active channels (Cha), i.e. channels showing a spike rate greater

than 0.01s−1, were identified. The number of active channels

can vary significantly (e.g. from 7% up to 50% of the total

number of available electrodes–channels). Since many factors

are involved in determining the number of spontaneously

active sites, this large variability is not surprising and it is

6


(a) (b)

(c)

(d)

Figure 2. Spontaneous activity in a hippocampal culture and example of NB detection. (a) Raster plot of spiking activity on 10 min ofrecording (experiment 3) from 603 active channels. The synchronization of the pattern and the recruitment of many channels, together withthe peaks on the average firing rate (AFR) highlight the presence of NBs. (b) Zoom on 30 s of the raster plot. The orange regions delimitNB events as found through the algorithm described in section 2. (c) Close-up on a single NB displayed by the raster plot and the AFRhistogram. As can be noted the NB algorithm can identify both the beginning and tailing activities. (d) Distribution of the percentage of therecruited channels with respect to the active channels among the entire set of detected NBs. The distribution is almost uniform, rangingfrom 15% (the threshold for NB detection used here) and 75% of the active channels.

Table 1. NB statistics for the performed experiments. The different columns report the following: the experiment identification number; thenumber of days in vitro (DIV) at which the recording took place; the number of active channels (Cha); the NB rate; the mean NB duration;the mean interval between consecutive NBs and the mean percentage of recruited channels (Chr). The inter-NB interval is computed as thedifference between the beginning of a NB and the end of the preceding NB. The Chr percentage is referred to the number of Cha.

Experiment Cha NB rate Mean NB Mean inter-NB Mean(no) DIV (no) (NB s−1) duration (ms) interval (s) Chr (%)

1 18 281 0.18 407 5.12 42.32 28 602 0.25 783 2.78 45.33 20 603 0.21 336 4.40 46.94 32 868 0.40 640 1.88 35.95 32 1035 0.62 381 1.22 36.46 28 2255 0.35 232 2.61 49.87 24 736 0.19 541 4.72 34.6

related to the cell culture conditions, including cell plating

densities, age and uniformity of the culture. To identify

bursting activity at the network level, NB detection was

performed. We discarded NB episodes shorter than 100 ms

that cannot be further analysed and classified because of the

reduced number of data points. However, it should be noted

that the number of short NB episodes was less than 5% of the

total number of identified NBs.

The main characteristics of the activity of our

hippocampal cultures are summarized in table 1. As previously

introduced, a large variability is observed for both NB rate and

mean duration, which range between 0.18 and 0.62 NB s−1

and 232 and 783 ms respectively. In particular, the mean

duration seems not to be directly correlated with the mean

inter-NB interval, since the extreme values correspond to

similar intervals (2.61 and 2.78 s). The percentage of recruited

channels (Chr) with respect to the total active channels (Cha),

shows instead a more stable trend, indicating that on average

in a single experiment less than half of the active channels are

recruited during NBs. Figure 2 illustrates, as a representative

example, the activity recorded from experiment 3. Spatial–

temporal dynamics are represented by means of the spatial

distribution and number of active channels, raster plots and

average firing rates. A repetitive increase in the network firing

rate can be observed globally (figure 2(a)), with a period of

about 80 s, and in a close-up (figure 2(b)) where identified

NBs are clearly distinguishable. The periodic oscillations

at coarser time scales were observed in three of the seven

experiments. The close-up in figure 2(c) shows a single NB

and the correspondent identified region. The orange bars

indicate the beginning and the end of the NB, demonstrating

the efficiency of the algorithm to include both leading and

7


Figure 3. NB onset visualization by spiking or voltage values and corresponding derived CATs. 68 milliseconds of recorded signals from ahippocampal neuronal network cultured on APS-MEA during a NB event onset, represented as activity-images sequences at 125 Hz andCATs. Sequences are temporally synchronized with each other and organized one per each row and type of method used. Eachactivity-image is representative of the activity recorded along a 20 ms TW and the time step between consecutive images is 8 ms. The64 × 64 coloured pixels depict the level of activity at each of the 4096 electrodes according to the corresponding measure. Last columnreports the CATs based on each measure with a line whose colour is time-varying. CATs are traced on a CA space that has been zoomed ona 30 × 30 pixels area comprising pixels between the 14th and 44th on rows and the 30th and 60th on columns. First row depicts the NBevolution measuring the number of spikes falling in the TW at each recorded channel (note the discontinuous colour scale) and the resultingCAT is reported at the end. Second row represents instead the max–min difference on amplitude values within TW (see section 2) using afull range exponential scale. As can be appreciated, the propagation is clearly visible and the information is increased with respect tospike-based images. Nonetheless the resulting CAT is conditioned by the high number of background channels. Last row is built with thesame method but a LT established at 20% of the max–min scale (asterisk on the colour scale) is applied resulting in a non-noise-driven CAT.

tailing spike events. Figure 2(d) reports the distribution of

the percentage of recruited electrodes during NB for this

experiment. As was observed for all the other experiments, no

preferred distribution in terms of involved channels is clearly

found. Additionally, the maximum number of Chr for a single

NB among all the experiments reaches 75% of the total active

channels; while considering the entire NB dataset of a single

experiment, all the active channels participate at least once in

a NB.

CAT was then calculated using the two techniques

reported in section 2, i.e. based on the detected spike activity

and on activity movies (i.e. voltage-coded data). Figure 3

shows one example ofNBonsets (i.e. first tens ofmilliseconds)

represented by means of time-lapse image sequences and

corresponding CAT results. In the figure, the upper row

represents the activity in terms of the firing rate (i.e. after spike

detection); the second row shows the same activity by means

of the recorded max–min signal variation (see section 2); and

the last row reports the voltage values with the application of a

LT adjusted in order to cut the background activity (i.e. 20% of

the full range). Indeed, even if the propagation can be similarly

appreciated in the voltage domain, both with the second and

the third approach, CAT computation on unthresholded data

is affected by the large number of electrode channels that

account for uncorrelated background signals. By looking at

the CAT results, it is rather evident that the voltage-coded

images better illustrate the activity propagation during bursting

activity. These ‘analogue’ activity movies better exploit the

full potentiality offered by the increased spatial resolution of

the high-density APS-MEA devices and demonstrate a high

robustness (after thresholding) against noisy channels. In

fact, point process analysis (as for analysis on spike-detected

activity) can be dominated by noisy or spurious signals,

while pseudo-continuous voltage-coded image analysis is less

sensitive to noisy voltage variations (i.e. no spikes are added

to the firing rate) as only the amplitude of a specific electrode

in a specific time interval is affected.

The two phases of NBs spatial–temporal behaviour, i.e.

the propagating phase followed by a reverberating phase, can

be clearly identified in correspondence with the different CAT

behaviour (figure 4). Indeed, the CAT representation of the

NB moves first (i.e. burst onset) towards a specific direction

with a quasi-continuous course, and successively starts to jump

continuously, and apparently randomly, between distant points

of the CA space. This latter phase being highly variable,

we decided to limit further analysis to the first phase of

the NB. The occurring time of the reverberating phase can

change both within the same experiment and, more often,

across different experiments. As introduced in section 2, in

order to identify the average critical point (t∗cut) that separates

the propagating phase from the reverberating phase within

the same experiment, CATs were computed considering the

spatial–temporal patterns of identified spikes. t∗cut was thus

derived for each experiment by examining CATs with the

activity pattern diffusion analysis (APDA). Since classification

requires aligned CATs with the NB firing profile, the critical

point was calculated with respect to the NB firing peak time.

For each experiment all NBs were taken together and the

8


(a)

(b)

(c)

Figure 4. Illustration of a single CAT reflecting two NB phases: thepropagating and the reverberating phases. (a) Colouredtime-varying line depicting a CAT for a NB lasting 570 ms recordedfrom a 28 DIV hippocampal culture. CAT is made up of 550 pointsconstructed by summarizing recorded spikes with a TW of 20 msand by using a time step of 1 ms. (b, c) Decomposition of CAT intime series for row and column increments respectively. Theprogressive course in the CAT during the first approx 150–200 mscorresponds to low increments in the components domain, while theapparently random jumping behaviour rising up suddenly in thefollowing ms is reflected in high variability in the time series.

CATs were estimated. Each single CA point was computed

integrating the number of spikes over a TW of 20 ms, and a

time step of 1 ms was chosen to construct the trajectories.

CATs were organized in different ensembles as specified

in the following: each ensemble collects CATs that share the

same truncating time (tcut) with respect to the firing rate peak

of the NB to which each CAT is related; different ensembles

were obtained by successively increasing tcut with a time step

of 5 ms. Hence, the first ensemble contains CATs computed

over all NBs and truncated at the peaks of the corresponding

firing rate histograms; the second ensemble accounts for CATs

truncated at 5 ms after the peak and upwards, to reach the

average NB duration. APDPs were built by calculating the

mean square displacement 〈1r2〉 between all pairs of points

Table 2. Results on CATs’ analysis for all the performedexperiments. Critical points, corresponding scaling exponents andnumber of identified NB classes are shown. t∗

cut is expressed in timedistance from the firing rate histogram peak and has been determinedon average for all CATs constructed for a single experiment. Thescaling exponent H has been estimated on the log–log plots ofplanar displacement values versus time intervals for CATs truncatedat t∗

cut. It always attains values >0.5, as would be expected for apositively correlated random walk. The number of NB classes, asidentified by an automatic k-means clustering procedure applied onspline interpolated CATs, is reported both for spike-based andvoltage-based cases. Results are consistent except for experiment 3,where one more class was found for the voltage-based CATs.

Experiment t∗cut No of NB classes No of NB classes

(no) (ms) H (spike-based) (image-based)

1 45 0.57 2 22 50 0.60 3 33 20 0.58 2 34 40 0.60 2 25 15 0.56 2 26 55 0.55 2 27 50 0.56 2 2

of the enclosed CATs at a given time interval 1t. The double

logarithmic plot of 〈1r2〉 values versus 1t was constructed

for each ensemble. In all experiments, the existence of short-

range behaviour with a scaling exponent value (H) above

0.5 was observed, implying persistence and thus a diffusive

process combined with the progressive appearance, as tcut was

increased, of a late phase with H values increasingly closer

to zero (figure 5(a)). To ensure that our findings were not

biased by the number of CATs grouped together (i.e. by the

resulting cumulative number of points that can be used to

calculate displacements), we shuffled our data for the shorter

CAT ensemble and we constructed an APDP from which we

obtained H = 0 (data not shown), according to what was

expected for an uncorrelated random process (Roerdink et al

2006). Finally, linear regression and error estimation were

used in order to determine the truncating time at which only

the propagating phase can be observed. RMSEs were plotted

against tcut and the critical point t∗cut was established as the

last point at which RMSE starts to monotonically increase

(figure 5(b)).

Table 2 reports the critical truncating times with respect

to the firing rate peak for all seven experiments together with

the scaling exponent computed on the corresponding truncated

CATs. Except for experiments 3 and 5, t∗cut values are around

50 ms. H values all indicate a positive correlation between

subsequent CAT planar displacements (i.e. meaning a diffusive

process), which are indicative of the propagation pattern

that always precedes the synchronized activity (reverberating

phase) in a NB.

Based on these results, spike-based and activity movies-

based CATs were recalculated up to the critical point, by

keeping a 20 ms wide integrating window, but by using a

higher time step of 4 ms. The lower sampling rate for CATs

was chosen in order to reduce the number of features and

to simplify the classification process. The resulting CATs

had variable lengths as the time-to-peak varies among NBs.

Since we were only interested in NB classification based on

9


(a) (b)

Figure 5. APDA performed on CATs and identification of the critical point separating the propagating phase from the reverberating phase.(a) APDP built on CATs calculated for 167 NBs (TW 20 ms, time step 1 ms) identified in a 28 DIV hippocampal culture recorded for10 min. The resulting double-logarithmic plot of mean square planar displacements versus time intervals is displayed as the black dottedline. The maximum time interval considered here was 246 ms, i.e. 1/3 of the longest CAT in order to have a reasonable number of planardisplacements to average upon. APDP exhibits two scaling regions: a short-term region and a long-term region with scaling exponent >0.5and <0.5 respectively (calculated through the red dashed regression lines). These values mean the coexistence of persistent behaviour whereconsecutive points in the CATs tend to distance each other and of a negatively correlated course that signifies a repetitive retracing of pastpositions. The persistent behaviour here is not pronounced (H close to 0.5) because mean square displacements are computed over the entireset of CA points, thus mixing values from the propagating phase with uncorrelated values from the reverberating phase (see section 2 onAPDA for further details). (b) RMSEs plotted against different truncating times (tcut) for CATs. CATs calculated as specified on (a) weretruncated to successive tcut (1tcut = 5 ms) with respect to the firing peak of the corresponding NBs. For each truncating time an APDP wasconstructed and RMSE on linear regression was computed. The error on residuals is initially low and then increases due to the onset of theanti-persistent scaling region. The time-switch point (t∗

cut) that marks the onset of the reverberating phase is therefore defined as thelast-before-increasing tcut.

their specific propagation pattern, with no regard to the timing

required to recruit neuronal activity (i.e. involved channels),

we chose the spline interpolation (see section 2) to equalize

trajectory lengths. This strategy produced equally long CATs

that were then classified with an automatic k-means algorithm

able to individuate the best number of classes and to remove

outliers.

The classification results are reported in table 2. Both

the adopted strategies to construct CATs resulted in the same

number of classes except for experiment 3, where the voltage-

based CATs were grouped in three classes instead of two.

Moreover, most of the experiments showed only two NB

classes.

Figure 6 visually compares CATs constructed on

identified NBs from experiments 2 and 3 (figures 6(a) and

(b) respectively). In the former, the agreement between

the two methods (i.e. spike-based and voltage-coded images)

both in terms of shape and classification is clearly evident.

Nevertheless, analysing the voltage-based CATs, a general

increased compactness of the trajectories and the potential

existence of two subgroups within class 3 are noteworthy. In

figure 6(b) the two and three classes obtained by analysing

spike data and activity movies are presented. Interestingly,

activity movies-based CATs show a larger uniformity in the

identified trajectories, resulting in more compact intra-class

shapes.

With the aim of evaluating the matching degree between

the two classification results, the likeness factor (Ŵ) reported

in section 2 was adopted. The usefulness of the new proposed

technique being based on the direct analysis of functional

activity movies, for validation we considered the results of

the spike-based CATs as a reference. As a consequence,

the CATs to test, resulting from the voltage-coded high-

resolution images, needed to be tuned. This was necessary

only for experiment 3 where the number of obtained classes

was different. In this case the clustering was repeated and

voltage-based CATs were forcibly grouped into two classes.

Afterwards, the likeness factor between the reference set and

the test set was calculated for each experiment. In order

to assess the goodness of the comparison, we additionally

included shuffled data. We randomly shuffled the CATs

belonging to the subsets of the testing trajectories set and

obtained a surrogate set Ä∗. Then, we calculated the likeness

factor comparing Ä∗ to the spike-based reference set and we

repeated the entire process independently n times (n ∼104) to

yield a distribution of the randomly generated likeness factors.

Results are summarized in figure 7. The likeness factor for

real data is always well above shuffled data (p < 0.05) and

four times out of seven is above 0.9.

These results demonstrate how the APS-MEA platform

allows identification of the diffusion patterns by directly

analysing the sequence of images that code for voltage values.

While the tracking of NBs through the activity movies can

be performed only on a high-density system, we wondered

if the spatial resolution might affect this analysis in the

spike domain. Therefore, we investigated CATs obtained by

spatially undersampling our original datasets. Low-density

datasets were constructed by choosing a 16 × 16 sub-grid

of channels spaced by 147 µm (1 channel every 4 channels

along both rows and columns; electrode density of about

40 electrodes mm−2) from the full resolution recordings

(∼580 electrodes mm−2). The intersection of the sub-grids

10


(a)

(b)

Figure 6. Visual comparison of CATs and corresponding classified results for experiments 2(a) and 3(b). (a, b) First and second rowsillustrate CATs built up on spike firing dynamics and functional electrophysiological activity movies respectively with a TW of 20 ms and atime step of 4 ms. CATs were resampled with spline functions in order to have all the same length. Then they were classified taking asfeatures ordered column and row values and using an automatic k-means algorithm based on squared Euclidean distance. CATs are colourcoded based on the side time bar. (a) The classification of 150 identified NBs according to spike-based and voltage-based CATs results inthe same number of classes. In both cases no outliers have been found. It is worth noting the increased compactness of the resulting class forthe voltage case and the differences in class 3 where the novel technique seems to reveal the existence of two subgroups. (b) 126 NBs lead totwo classes (grouping 116 NBs) for the spike-based CATs and to three classes (grouping 118 NBs) for the voltage-based CATs. Unsortedtrajectories coming from the outlier removal process are not shown.

with the set of the active channels for each experiment led

finally to obtain a variable number of active channels (from 22

to 136). CATs based on these undersampled datasets resulted

in the identification of a different number of NB classes in

five cases out of seven with respect to the results obtained

at full resolution (see table S1 in the supplementary data

available from stacks.iop.org/JNE/7/056001/mmedia). Visual

inspection of data seems to indicate that suchmissclassification

11


Figure 7. Likeness factors (Ŵ) calculated on CAT classification forthe performed experiments. Spike-based CATs were first classifiedand then voltage-based CATs were forced to be grouped in the samenumber of classes. Spike-based classified CATs were taken asreference, while voltage-based classified CATs were tested in orderto evaluate the degree of matching items across classes. Followingthe procedure described in section 2, the two sets of classes werecompared and a likeness factor was calculated (solid line). Hence,dummy sets were built by shuffling the items in the testing set andwere again compared with the reference set (grey interrupted line;corresponding to p = 0.95). In all cases the Ŵ obtained comparingreal data was significantly (p < 0.05) above the Ŵ derived fromsurrogate data.

is due to unresolved trajectories (supplementary figure S1

available from stacks.iop.org/JNE/7/056001/mmedia): at

low resolution, the majority of CATs roughly resemble

the reference trajectories but a consistent number of

CATs lose their regularity and thus are considered

to belong to other classes (figure S1(a) available

from stacks.iop.org/JNE/7/056001/mmedia) or, occasionally,

to form a new class (figure S1(b) available from

stacks.iop.org/JNE/7/056001/mmedia). From a more

quantitative point of view, we evaluated also the number of

missclassified trajectories at the low-resolution mode, finding

that the percentage of outliers (cf section 2.6.1) is significantly

higher (i.e. 2 to 14 times) than the percentage of outliers

obtained at full resolution.

We also investigated the temporal distribution of NBs

grouped into clusters for experiment 3, which was observed

to show a sort of macro-scale pattern in the activity footprint.

We chose the classification based on the activity movies as

it seemed to better classify the different activity pathways.

Figure 8 reports the results obtained both by NBs occurring

time grouped by classes (stacked vertically below the raster

plot) and by the histogram of the intervals between intra-class

NBs. While NBs from class 1 show a single modality in

the time distribution, NBs from classes 2 and 3 seem to repeat

themselves with a second interval too. Interestingly, this could

be an indication of the presence of packets of similar NBs.

Class 2 seems to repeat this sort of macro-burst every 60 s,

while class 3 every 80 s. By considering the different duration

of the packets for each class, together with the repetitive

dynamics visible in the raster plot, packets seem to reoccur

with a time constant related to the repetition of the macro-

scale pattern.

Finally, we investigated the repeatability of NB activity

pathways. Figure 9 reports raster plots of four NBs from class

1 and four NBs from class 3 taken from figure 5. Each NB is

shown both for the entire duration (1 s inspection windows)

and during its onset (first 50 ms). Comparable plots from NBs

pooled in the same class are thus arranged in the same column.

It can be noted that events coming from the same clusters share

similar onsets, although the differences in the whole NB can

be marked, especially in terms of duration, firing intensities

and electrode recruitment. Such differences are evident for the

case of the first and the second NBs from class 1 (first column,

first and second row respectively), where the latter shows an

increased duration and firing intensity. Anyhow, the increased

resolution of APS-MEAs highlights fine differences between

grouped NBs. For instance, by observing close-up raster plots

of class 3, it is possible to distinguish an anticipated activity

of the lower channels (surrounded by red dotted boxes in

figure 9) in the third and fourth bursts (marked with ‘b’ by

the central divider) with respect to the first and second bursts

(marked as ‘a’). This leads to CAT (the two plots at the bottom)

that could be further split into two subgroups since the initial

position and the directions slightly diverge.

4. Discussion

The high spatial–temporal resolution provided by the

APS-MEA together with the peculiar possibility of an

electrophysiological signal representation bymeans of activity

movies (i.e. images sequences), enables us to investigate

the network dynamics with a detailed quantification of the

propagating activity. In this work, we first focused on NB

characterization both by using standard tools based on spike

timing and a newmethod based on the voltage-amplitude of the

recorded raw signals (i.e. activity movies). Successively, we

propose a technique to separate the two phases observed during

NB evolution, namely (i) a propagating phase during burst

onset and (ii) a reverberating phase with quasi-synchronous

activations and intense spiking involving almost the whole

network. The latter phase being highly variable, for this study

we considered only the more reliable propagating phase, and

we appliedmethods from fractional Brownianmotion theory to

identify its boundary during NB events. Finally, we classified

the extracted CATs both for spiking activity and for activity

movies (i.e. voltage raw signals).

We showed that the CAT analysis allowed us to localize

NB initiation and to compare different estimated trajectories

and that the approach based on activity movies turned

out to perform slightly better than the spike-detection-

based approach for tracing the propagating phase and for

distinguishing between different NB families. In addition

to the analysis of the performances, this approach has the

advantage of a reduced computational cost (i.e. there is no

need for spike detection) and of a straightforward real-time

visualization.

Then, we applied CAT analysis to investigate in detail

NB behaviour. In three out of seven experiments we found

different modalities of the periodic fluctuations of firing rate

profiles. This resulted in a second rhythmic pattern, with a

coarser periodicity. Such behaviour has already been observed

12


Figure 8. NB classes temporal distribution. Left: raster plot of 10 min of recording from experiment 3 (see also figure 2) below theoccurring time of the identified NBs. All three identified classes plus the unsorted NBs coming from the outlier removal process arereported. Right: inter-NB interval histogram for NB belonging to each class considered separately. The interval between two NBsbelonging to the same class is calculated as the time difference between the beginning of the second NB and the end of the first NB. Themaximum inter-NB interval considered is 100 s.

for neocortical cultures (see for instance Corner et al (2002)

and van Pelt et al (2004b)) and has been related to the

oscillations recognized in the spontaneous bioelectric activity

of the central nervous system (Corner et al 2008), for instance

during slow-wave sleep. We found that different classes of

NBs participate with a seemingly periodically defined scheme

to this complex behaviour and hence such rhythm may be

the result of the interplay of different NB events. Given

the importance of rhythmic pattern generation in neuronal

ensembles, this hypothesis must be further investigated with

long-term experiments.

While a significant degree of repetitiveness led to

the identification of only a few NB classes, on the

other hand the changes in the channel recruitment may

reveal complex dynamics. Although the variability on

trajectories is notably lower than other NB features (e.g.

like firing intensity, duration and profile), we found subtle

changes or quasi-continuous deviations among trajectories

that sometimes resulted in dispersed classes (e.g. class 1

in figure 6(a)) or even in two sub-classes (see class 3

in figure 6(a)). In other studies conducted with standard

MEAs (Beggs and Plenz 2004, Ikegaya et al 2004, van Pelt

et al 2004a, Eytan and Marom 2006, Raichman

and Ben-Jacob 2008), the observed phase relationship

in channel activation (i.e. recruitment order among

channels) was stronger and led them to hypothesizethe existence in the cultured network of well-organizedstructures (Gross and Kowalski 1999, Raichman andBen-Jacob 2008) as well as sub-networks of ‘cardinalneurons’ with prominent activity and peculiarity (Lathamet al 2000, Eytan and Marom 2006). Both these assumptionsare in our opinion reasonable. However, the observationthat fine changes are intrinsic in NB events may supportthe hypothesis of fault tolerant mechanisms. In this case,neuronswould participate in a sort of ‘team’ to the perpetuationof the same information (propagation pattern), regardless ofthe precise network state, which is given from short-termactivity-dependent mechanisms, like the refractory periodsand the oscillations in the neurotransmitter’s pooling. Thisinterpretation is also supported by other results reported here.Firstly, we observed a variability in the number of participatingelectrode sites. This is reflected by the fact that there is neithera preferred distribution in the number of recruited channels,nor a single NB in which all the channels took part (see, asan example, figure 2(d)). Secondly, we found that similarpropagation patterns can result in incomparable profiles whenadopting a lower spatial resolution. This indicates that activityflow might involve slightly different neuronal pathways. Suchfindings unravel more sophisticated behaviour of networkdynamics with respect to the previously reported all-or-nonenature of NBs (Eytan and Marom 2006).

13


Figure 9. Repeatability of NBs belonging to the same class by means of raster plots and CATs. In the top panel raster plots constructed onthe 602 active pixels from experiment 2 showing both global activity and NB onset of eight NBs belonging to two different classes. A furthersegmentation into two subgroups (‘a’ and ‘b’), based on less pronounced intra-class differences, is highlighted. Below: CATs (TW= 20 ms,time step = 4 ms) calculated for each of the eight NBs up to 50 ms after the firing peak. Note how strongly typed propagating patterns areclearly distinguishable along raster plots; CATs quantitative analysis reflects the visually observed differences (cf subgroups ‘a’ and ‘b’).

Such observations were made possible by the APS-MEAplatform performances. This technology hence holds thepromise to shed new light on the mechanisms of spontaneousactivity, allowing a detailed picture of the network dynamicsboth from the temporal and from the spatial points of view.Recent publications of Ben Jacob and co-workers (Baruchiand Ben-Jacob 2007) have demonstrated the feasibility ofimprinting novel activation patterns (motifs) in culturednetworks. This is the first step towards the intriguingpossibility of neuro-memory-chips. However, two aspectsarise from our experimental findings. First, there is a lownumber of distinguishable propagating patterns (as alreadyobserved in Segev et al (2004), and Raichman and Ben-Jacob(2008)) and second, the relevance, in terms of duration andfiring intensity, of the reverberating phase. These issues opena number of possible hypotheses related to the role of the twophases and still leave open the possibility of activation patternsas amemorymechanism. More specifically, it should be asked:which is the interdependence between the two phases? Whichis the degree of variability of the reverberating phase and can

this depend on a specific propagation? More generally, further

investigations could be addressed to assess whether and how

the observed different periodicities in firing patterns, which

seem to rely on differentNBmotifs, can be linked to the general

context of sleep-wave oscillations and memory consolidation.

Acknowledgments

This work was supported by a grant from the European

Community in theNew andEmerging Science and Technology

program (IDEA project, FP6-NEST, contract no 516432).

The suggestions of Thierry Nieus on shuffling procedures are

gratefully acknowledged.

References

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Tracking burst patterns in hippocampal cultures with high-density CMOS-MEAs

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