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doi:10.1152/jn.01131.200799:1435-1450, 2008. First published 9 January 2008;J NeurophysiolHernndez-Cruz, Elvira Galarraga, Ren Drucker-Colin and Jos BargasLuis Carrillo-Reid, Fatuel Tecuapetla, Dagoberto Tapia, ArturoEncoding Network States by Striatal Cell Assemblies

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Encoding Network States by Striatal Cell Assemblies

Luis Carrillo-Reid,1 Fatuel Tecuapetla,1 Dagoberto Tapia,1 Arturo Hernandez-Cruz,1 Elvira Galarraga,1

Rene Drucker-Colin,2 and Jose Bargas1

1Departamentos de Biofsica and 2Neurociencias, Instituto de Fisiologa Celular, Universidad Nacional Autonoma de Mexico,

Mexico City, Mexico

Submitted 12 October 2007; accepted in final form 8 January 2008

Carrillo-Reid L, Tecuapetla F, Tapia D, Hernandez-Cruz A,Galarraga E, Drucker-Colin R, Bargas J. Encoding network statesby striatal cell assemblies. J Neurophysiol 99: 14351450, 2008. Firstpublished January 9, 2008; doi:10.1152/jn.01131.2007. Correlatedactivity in cortico-basal ganglia circuits plays a key role in theencoding of movement, associative learning and procedural memory.How correlated activity is assembled by striatal microcircuits is notunderstood. Calcium imaging of striatal neuronal populations, withsingle-cell resolution, reveals sporadic and asynchronous activityunder control conditions. However, N-methyl-D-aspartate (NMDA)

application induces bistability and correlated activity in striatal neu-rons. Widespread neurons within the field of observation present burstfiring. Sets of neurons exhibit episodes of recurrent and synchronizedbursting. Dimensionality reduction of network dynamics reveals func-tional states defined by cell assemblies that alternate their activity anddisplay spatiotemporal pattern generation. Recurrent synchronousactivity travels from one cell assembly to the other often returning tothe original assembly; suggesting a robust structure. An initial searchinto the factors that sustain correlated activity of neuronal assembliesshowed a critical dependence on both intrinsic and synaptic mecha-nisms: blockage of fast glutamatergic transmission annihilates allcorrelated firing, whereas blockage of GABAergic transmissionlocked the network into a single dominant state that eliminatesassembly diversity. Reduction of L-type Ca2-current restrains syn-chronization. Each cell assembly comprised different cells, but a small

set of neurons was shared by different assemblies. A great proportionof the shared neurons was local interneurons with pacemaking prop-erties. The network dynamics set into action by NMDA in the striatalnetwork may reveal important properties of striatal microcircuitsunder normal and pathological conditions.

I N T R O D U C T I O N

A central pattern generator (CPG) produces specific activitypatterns in the absence of sensory inputs (Grillner et al. 2005b)and can transform afferent inputs into detailed spatiotemporaloutputs (Grillner 2006; Yuste et al. 2005). In vitro experimentswith circuits containing CPGs demonstrate that tonic excitationproduced by the glutamate agonist N-methyl-D-aspartate

(NMDA) can activate the stereotyped electrical behavior thatneuronal networks exhibit in more intact preparations (such asfictive locomotion). This is observed as recurrent burstingactivity in single neurons, while simultaneous unitary andpopulation recordings demonstrate synchronicity and alterna-tion of cell assemblies activity during bursting (e.g., Gordonand Whelan 2006; Grillner et al. 1981; Guertin and Hounsgaard1998; Hsiao et al. 1998; Kiehn 2006; Takakusaki et al. 2004b).

Basal ganglia (BG) contain CPGs that activate innate be-havioral routines, procedural memories, and learned motor

programs (Barnes et al. 2005; Graybiel 1995; Grillner et al.2005a,b; Takakusaki et al. 2004a). A major component of theBG is the striatum, which receives a widespread input from thecerebral cortex and thalamus. Striatal circuits process cortico-thalamic inputs to produce specific outputs consisting of burstsof action potentials during the execution of motor tasks, prob-ably following voltage transitions to depolarized up-states(Hikosaka et al. 2000; Kasanetz et al. 2006; Mahon et al. 2006;Romo et al. 1992; Schultz et al. 1993; Wilson 1993). As in

other isolated nervous tissue preparations known to containCPGs (e.g., Guertin and Hounsgaard 1998), addition of NMDAto neostriatal circuits in vitro (Vergara et al. 2003) and in vivo(Herrling et al. 1983) induces recurrent bursting and patterngeneration in single neurons. Moreover, intrastriatal applica-tion of NMDA in vivo generates turning behavior when ad-ministered unilaterally in freely moving animals (Ossowska andWolfarth 1995); demonstrating that motor behaviors arise fromthe striatal processing of enhanced excitatory drives. This evi-dence suggests that the striatum posses the connectivity andintrinsic mechanisms to orchestrate pattern generation (Grillner2006).

To test this hypothesis, we used calcium imaging of neuro-

nal populations in a corticostriatal slice preparation to monitor,with single-cell resolution, dozens of cells simultaneously andthen discern pattern generation produced at the microcircuitlevel by the activity of cell assemblies. Simultaneous electro-physiological recordings from striatal neurons demonstratedthat calcium transients result from neurons bursting on top ofsuprathreshold up-states (Kerr and Plenz 2002).

M E T H O D S

Slice preparation

Transverse corticostriatal slices (300 m thickness) were obtainedfrom PD14-29 Wistar rats as previously described (Kawaguchi et al.

1989; Vergara et al. 2003). All procedures conformed to the guide-lines of the UNAMs Animals Scientific Procedures Committee.Slices were obtained with ice-cold saline (4C) containing in mM: 123NaCl, 3.5 KCl, 1 MgCl

2, 1 CaCl

2, 26 NaHCO

3, and 11 glucose (25C;

saturated with 95% O2

-5% CO2

; pH 7.4; 298 mosM/l). Slices werethen transferred to saline at room temperature (2125C) where theyremained for 1 h before recording. The cationic concentration of thissaline favors the appearance of up states in vitro (Vergara et al. 2003).Although the present results were performed mainly in young animals(PD14-21), basically the same network behavior was observed in olderanimals (PD25-29) (J. Bargas, unpublished data and see RESULTS).

Address for reprint requests and other correspondence: J. Bargas, Institutode Fisiologa Celular UNAM, PO Box 70-253, Mexico City, DF 04510 Mexico(E-mail: [email protected]).

The costs of publication of this article were defrayed in part by the paymentof page charges. The article must therefore be hereby marked advertisementin accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

J Neurophysiol 99: 14351450, 2008.First published January 9, 2008; doi:10.1152/jn.01131.2007.

14350022-3077/08 $8.00 Copyright 2008 The American Physiological Societywww.jn.org


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Calcium imaging

Slices were incubated at room temperature in the dark for 2030min in the presence of 1020 M fluo 4-AM (Tef Labs, Austin, TX)in 0.1% dimethylsulphoxide (35C), equilibrated with 95% O

2-

5% CO2

. Slices were perfused with control saline (see preceding text)in a perfusion chamber on the stage of an upright microscopeequipped with a 10 water-immersion objective (Eclipse E600FN;

Nikon, Melville, NY). Excitation at 488 nm was performed with aLambda LS illuminator (Sutter instruments, Novato CA). Experi-ments were performed at room temperature.

Images were acquired with a cooled digital camera (SenSys 1401E,Roper Scientific, Tucson, AZ) at 250500 ms/frame. Imaging soft-ware used was RS Image (Photometrics; Roper Scientific). Theimaged field was 800 600 m in size. Short movies (100 250 s, 50-to 100-ms exposure, 24 image/s) were taken at time intervals of 520min during 1 h.

The number of fluo 4-loaded neurons in the field was determined atthe end of the experiment with a 5-s puff of 50 mM KCl. Thismaneuver disclosed all fluo-4-labeled neurons (either active or silentduring the experiment). Cells active during the experiment were ana-lyzed, and the ratio of active/silent cells was obtained. Spontaneous orevoked calcium transients together with voltage responses were re-

corded electrophysiologically in some cells, both in control saline andduring the application of 512 M NMDA (Sigma-Aldrich-RBI, St.Louis, MO). In some experiments, cortical sensory motor areas werestimulated with a concentric bipolar electrode (12 m; FHC, Bow-doinham, ME). Stimuli consisted of different trains of 510 stimuli at20 Hz. Each stimulus was 100200 s and 50120 A. In someexperiments, we used the minimal stimulus intensity necessary toevoke peaks of synchrony with amplitudes above chance (P 0.05).This allowed us to study changes of electrical evoked synchronyunder different pharmacological conditions.

Immunohistochemistry

Sections were processed to fix fluo-4 fluorescenceor cells activeduring experimentswith N-(3-dimethylaminopropyl)-N-ethylcarbodi-

imide hydrochloride (EDAC), and to perform conventional immunocy-tochemistry in fluorescent cells to demonstrate either substance P (SP) orenkephalin (ENK) on fluo-4-labeled cells using commercially availableantisera (Peninsula Labs, San Carlos, CA) conjugated to CY3 or CY5.Slices were not processed for both antisera, but one was chosen in eachcase. Thus in each trial, either SP or ENK positive and negative neuronscould be observed. Briefly, sections were rinsed in PBS and incubated for1824 h at 4C with primary rabbit antibody against ENK or SP (diluted1:200). Sections were mounted in an anti-quenching media (Vectashield,Vector Laboratories) and examined under a confocal microscope (MRC-1024; Bio-Rad, Natford, UK) equipped with a kryptonargon mixed-gaslaser. Immunostained cells were studied in either single confocal imagesor reconstructed sections made by projecting z-series of 1040 consec-utive confocal images 1 m apart collected throughout the thickness ofthe section. The background noise was reduced averaging three to six

images. Digitized images were transferred to a personal computer (Con-focal Assistant, T. C. Brelje). More than 80% of fluo-4-loaded cells weremedium spiny neurons.

Drugs

Stock solutions were prepared before each experiment and added tothe perfusion solution in the final concentration indicated. NMDA,APV, nicardipine, CNQX, biocytin, and bicuculline methiodide orhydrochloride were obtained from Sigma (St. Louis, MO).

Electrophysiology

Calcium imaging and simultaneous electrophysiological recordingswere obtained from areas of the dorsal striatum previously shown asreceiving numerous cortical fibers (Vergara et al. 2003).

An Axoclamp 2B amplifier (Axon Instruments, Foster City, CA) wasused to perform whole cell current- and voltage-clamp recordings. Sig-nals were filtered at 13 kHz and digitized at 39 kHz with an AT-MIO-16E4 board (National Instruments, Austin, TX) in a PC computer. Dataacquisition used a software designed in the LabView environment(Lemus-Aguilar et al. 2006). Patch pipettes (36 M) were filled with (inmM) 115 KH

2PO

4, 2 MgCl

2, 10 HEPES, 0.5 EGTA, 0.2 Na

2ATP, and

0.2 Na3

GTP. In some experiments, biocytin 0.5%, and fluo-4 salt (2030M) were added to the recording pipettes.

Image analysis

Image processing was carried out with Image J (v.1.36, NationalInstitutes of Health), Multicell 2.0 (kindly supplied by Robert Froemke),and custom-made programs written in IDL (Cossart et al. 2003; Maoet al. 2001; Schwartz et al. 1998) or MATLAB (The Math-Works,Natick, MA).

All active neurons in a field were semi-automatically identified, andtheir mean fluorescence was measured as a function of time. Single-pixel noise was discarded using a 5-pixel ratio mean filter. Calcium-dependent fluorescence signals were computed as (F

i F

o)/F

o, where

Fi: fluorescence intensity at any frame and F

o: resting fluorescence,

i.e., average fluorescence of the first four frames of the movie.Calcium signals elicited by action potentials were detected based ona threshold value given by their first time derivative (2.5 times the SDof the noise value). Thus we obtained a C F binary matrix; were Crepresents the number of active cells and F the number of frames foreach movie. Spike onsets were signaled by ones in the matrix represent-ing transitions to the up states. Recordings were inspected manually toremove artifacts and slow calcium transients which are likely to corre-spond to glial cells (Ikegaya et al. 2005; Sasaki et al. 2007).

Statistical methods

To determine if calcium transients recorded from different cellswere correlated, the numbers of simultaneous activations per trial(onset of signals occurring within 3 frame windows) were detected.

To determine the P value of simultaneous transients occurring bychance, the distribution under the null hypothesis of independenttransients using Monte Carlo simulations with 1,000 replications werecomputed (Mao et al. 2001).

The degree of correlation between active cells was calculated withthe Jaccard correlation coefficient. Nevertheless the magnitude of thecorrelations is difficult to discern when many lines are superimposed.Thus we constructed cross-correlation maps of Jaccard correlationcoefficients to show the magnitude of the correlations between allcells pairs.

In addition, we identified the sets of cells activated simultaneouslyover time. To identify peaks of synchronous activity (i.e., that in-cluded more cells than those expected by chance), Monte Carlosimulations were also used to estimate the significance of their firingtogether. The threshold corresponded to a significance level of P

0.05. We then examined peaks that were significant at P 0.05.Peaks of synchronous and recurrent bursting activity that remainedsignificant during the experiment were selected for further analysis.

To analyze the dynamics of cell assemblies over time (networkdynamics), D N matrices were constructed, where D represents thenumber of active neurons in a set of experiments, and N denotes thefiring of cells during 250-ms to 1-s time bins (NMDA-induced upstates last between 0.5 and 5 s) (Vergara et al. 2003). Peaks ofrecurrent synchronous activity were vectorized so that bursting overtime was associated with different neurons. Each vector element isformed by the sum of the number of calcium spikes displayed by asingle neuron during the time bin, where peak derivatives denote thetime onset of electrophysiologically recorded up states (see precedingtext and RESULTS). Therefore the set of these vectors denote networkactivity as a function of time (Brown et al. 2005; Sasaki et al. 2007).

1436 CARRILLO-REID ET AL.

J Neurophysiol VOL 99 MARCH 2008 www.jn.org


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To measure the similarity index between the network vectors, wecomputed the normalized dot product of all possible vector pairs,which is equivalent to the cosine of the angle between the vectors(Sasaki et al. 2006; Schreiber et al. 2003). Then we plotted thesimilarity indexes as a pseudocolor matrix in which functionalstates sustained by significantly correlated or synchronized burst-ing cell assemblies appear as cluster-like structures (Sasaki et al.

2006).To reduce the dimensionality of the network vectors, locally linearembedding (LLE) was chosen. LLE is an unsupervised learningalgorithm that discloses nonlinear structures from multi-dimensionaldata (Roweis and Saul 2000). Multidimensional scaling (MDS) (Sys-tat, Richmond, CA) gave results quantitatively similar from thoseobtained with LLE. Nevertheless LLE depicted more effectively thetrajectories of the functional states in the network. The trajectories ofour high-dimensional data were not well described by linear dimen-sionality reduction methods (see Brown et al. 2005). To choose theoptimal number of states depicted from the LLE reduction, we usedhard and fuzzy clustering algorithms taking the Dunns index as avalidity function (Bezdek et al. 1997; Sasaki et al. 2007). To deter-mine the number of neurons in each state, hierarchical cluster analysiswas computed using Euclidean distances and the nearest neighbor

single linkage method (Systat Software, San Jose, CA).

R E S U L T S

Optical imaging from populations of striatal neurons

To study striatal microcircuits in vitro, we used Ca2 imag-ing (Fig. 1) to measure electrical activity in many cells simul-taneously with single-cell resolution. Activity was measuredindirectly, as changes in fluorescence. Fields from the dorsalstriatum were imaged in slices loaded with the Ca2 indicatorfluo-4 AM in 174 experiments performed in 86 corticostriatalslices. Figure 1A shows all dye-loaded neurons (see METHODS).Contours of cells both active and inactive during the experi-ment are depicted in Fig. 1B (filled circles and empty contours,respectively). Only neurons active during an experiment wereanalyzed. Under control conditions (with no drugs added), onlya few cells were active, and their firing was asynchronous(filled circles in Fig. 1B). Records of Ca2 transients fromthree spontaneously active neurons are shown (Fig. 1C). Theseexperiments confirm that the striatal circuitry is mostly quies-cent (n 40 slices) under control conditions.

Simultaneous voltage recordings and Ca2 imaging in me-

dium spiny neurons (Fig. 1, D and E) demonstrate the corre-

FIG. 1. Optical recording in striatal neurons. A: neurons in a striatal slice loaded with fluo-4 AM. Picture is the result of averaging 200 consecutive framesand background subtraction (see METHODS). Scale bar: 100 m. B: automatic contour detection of 376 cells from A. Dark circles indicate neurons exhibitingspontaneous calcium transients under control conditions with no drugs added (14/376 or 3.7%). Under control conditions, most striatal cells loaded with fluo-4remain silent. C: recurrent calcium transients recorded from 3 of the active cells shown in B (280 ms per frame). D: a fluo-4-loaded cell targeted forelectrophysiological recording (1). Fluo-4 salt was also administered through the recording pipette (bottom). Scale bar: 10 m. Voltage responses (top) to currentsteps (bottom) recorded from the neuron shown in D1 (2). Inward rectification and long latency to 1st spike are characteristics of medium spiny neurons.Steady-state current-voltage relationship measured in current-clamp mode from traces shown in D2 (3). Most calcium transients and electrophysiologicalrecordings shown in the next figures are from this class of neurons. E: simultaneous recordings of voltage transitions (1) and calcium transients (2) from the cellshown in D1 induced by the presence of N-methyl-D-aspartate (NMDA) in the bath. The duration of first derivatives of the calcium transients [dashed lineindicates 2.5 times the SD of the noise; 3; peaks in d(F/F)/dtgray stripes] match the duration of electrophysiological up states. Dots indicate events whered(F/F)/dt2.5 times SD; used to build raster plots (see following text). Histogram showing a bimodal distribution of membrane potential ( 4); taken fromelectrophysiological recordings in 1. Current-voltage relationship measured in voltage-clamp configuration in the presence of NMDA ( 5). Note 3 crossing pointsin the voltage axis and a negative slope conductance region.

1437STRIATAL NETWORK DYNAMICS



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spondence of suprathreshold activity and somatic calciumtransients (n 15 cells). As it has been observed in many othercentral circuits (e.g., Gordon and Whelan 2006; Grillner et al.1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998; Kiehn2006; Takakusaki et al. 2004b), we were able to generatebistability in striatal neurons after bath application of NMDA(Vergara et al. 2003), a transmitter known to induce motor

behavior when administered in the striatum (Fig. 1E) (Ossowskaand Wolfarth 1995). This treatment induces persistent burstingbehavior in neostriatal neurons (1 h) without the need ofelectrical stimulation (spontaneous). Most active cells during agiven experiment had the electrophysiological characteristicsof medium spiny neurons (Fig. 1D). Ca2 transients (F/F)corresponding to up states, had time derivatives [d(F/F)/dt]that matched bursts duration (Cossart et al. 2003; Kerr andPlenz 2002). Clearly membrane potential distribution is bi-modal in bursting cells (Fig. 1E4). The current-voltage rela-tionship (I-Vplot) measured in voltage clamp at the end of 400-to 500-ms commands shows a negative slope conductanceregion (NSCR), indicating bistability, in NMDA-treated cells

(Fig. 1E5) (e.g., Hsiao et al. 1998; Izhikevich 2007; Vergaraet al. 2003). Ca2 imaging and simultaneous electrophysiolog-ical recordings also show that only bursts with two or moreaction potentials produce detectable Ca2 transients in striatalneurons (n 10 neurons; Fig. 2). These experiments con-firmed that the striatal neurons can be activated in vitro byNMDA bath application. Therefore the next step was to ask ifthis activity was correlated and synchronous throughout thenetwork, in which case, it may correspond to a networkdynamics capable of producing pattern generation.

Cortical stimulation synchronizes widespreadstriatal neurons

In corticostriatal slices, electrical stimulation of the cortexevokes long-lasting depolarizations with overriding spikes inmedium spiny neurons (Bargas et al. 1991; Vergara et al.2003). Trains of cortical stimuli (Fig. 3A) also result in pro-

longed synaptic depolarizations with action potentials (Fig.3B). Figure 3B illustrates an experiment where whole cellcurrent-clamp recordings (Fig. 3B1) and calcium imaging (Fig.3B2) were performed simultaneously from a medium spinyneuron (stimulus frequency: 0.1 Hz; stimuli are signaled witharrows at the bottom). A calcium transient accompanies eachorthodromic response to the cortical stimulus (Fig. 3B2, arrows

at the bottom). The time derivatives of the calcium responsesare shown in the third row (Fig. 3B3); note similar duration ofderivative positive peaks and voltage responses. Accordingly,each peak from the differentiated calcium signal generates adot used to build raster plots as that illustrated in Fig. 3D. Eachrow in the raster plot represents an active neuron (filled circlesin Fig. 3C). Filled circles in Fig. 3C indicate fluo-4-loadedneurons within the field of observation that responded tocortical stimulation in one representative experiment. Emptycircles indicate loaded neurons that did not respond to corticalstimulation (see METHODS). Responsive cells are a minority ofthe total number of neurons which are scattered throughoutthe observational field among many unresponsive neurons

(Fig. 3C).Cortical stimulation activated and synchronized 1020%of fluo-4-loaded cells. Only a few neurons were active beforeand after the stimulus train, and these cells present a lack ofcorrelation (Fig. 3, D and E). At each trial, 90% of theresponsive neurons followed the cortical stimulus (Fig. 3D,bottom). Some neurons spontaneously active before the stim-ulus did not follow the electrical stimulation (blue lines).Targeted electrophysiological recordings of cells responsive tocortical stimulation revealed that most active neurons weremedium spiny neurons (Fig. 3B).

Network dynamics set into action by NMDA

Ca2

imaging allowed us to observe a population of striatalcells activated by cortical stimulation in control conditions(Fig. 3) and in the bath presence of NMDA (512 M; Fig. 4).Simultaneous voltage recordings of imaged neurons (Fig. 4A)

15 s

5% F/F

-75 mV

200 ms

20 mV

d(F/F)/dt

10%/s

A

B

C

D

FIG. 2. Action potentials necessary to producedetectable calcium transients. A: example of actionpotentials evoked by brief intracellular depolarizingcurrent steps (not shown) in a medium spiny neuron.Arrows point to the same records shown below in a

slower time base. B: actions potentials were evokedrepetitively (as in Fig. 1A) in successive series (de-limited by dashed boxes) of 14 action potentials.C: simultaneous recording of calcium transients ac-companying the action potentials shown in B. Noticethat significant calcium transients follow voltageresponses with 2 action potentials. D: 1st deriva-tive of calcium records shown in C. Detectionthreshold (dashed line) was set at 2.5 SD of thenoise. Threshold is reached when stimulus evokes2 action potentials.




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show that voltage transitions (Vergara et al. 2003) were ac-companied by Ca2 transients during NMDA activation (Kerrand Plenz 2002, 2004). Cell activity in the circuit was followedby recording their Ca2 transients and plotting them as rasterplots (Fig. 4B). Similar NMDA-induced electrical activity hasbeen observed in neurons that are part of a CPG (e.g., Grillner

et al. 1981; Guertin and Hounsgaard 1998; Hsiao et al. 1998;Takakusaki et al. 2004b). Interestingly, the raster plot (Fig. 4B,top) shows that several neurons are involved in the activity.Moreover, the time histogram (Fig. 4B, bottom; asterisks, redlines) clearly shows periods of increased synchrony and cor-related firing that occur spontaneously in different sets ofneurons (Cossart et al. 2003; Ikegaya et al. 2004). Statisticallysignificant (threshold P 0.05) spontaneous peaks of syn-chrony occurred in sets of neurons from n 38/45 slices inthese conditions. On average, there were 2.5 0.2 peaks ofsynchronous bursting per 201 10-s time epoch. Peaks ofrecurrent synchronous activity showed no apparent periodicity,and occurred with a mean interval of 46 10 s. Notably,neurons firing synchronously during NMDA treatment could

be hundreds of microns apart intermingled with silent cells(Fig. 4C, filled circles). Spatial correlation maps (Fig. 4D)show the pairs of neurons that exhibit statistically significant(P 0.05) correlated activity (line thickness is proportional tothe degree of correlation). On average, 77 4% of active cellshad correlated activity (n 38 slices) at any given moment.

Cells active during synchrony peaks represent most of thecorrelations between cells. Correlation plots of this activity(Fig. 4E; P 0.05) show that the degree of synchronizationamong active neurons is heterogeneous, thus the networkactivity is not due to chance correlation between any pair ofneurons but to the spatiotemporal dynamics of several cells.

Furthermore, application of NMDA to neostriatal circuits invitro (Vergara et al. 2003) and in vivo (Herrling et al. 1983)induces bursting activity and generates turning behavior infreely moving animals (Ossowska and Wolfarth 1995), sug-gesting a general mechanism preserved in a broad range ofages. Structured network dynamics with the same characteris-tics have been shown in the cortex of young mice in vitro(PD13-22) and adult cats in vivo, demonstrating that network

FIG. 3. Widespread distribution of striatal neurons synchronized by cortical stimulation. A: scheme of the experimental arrangement: field stimulation wasdelivered to corticostriatal afferents (electrode in cortex) while neurons from a dorsal striatum area (square) were recorded with simultaneous calcium imagingand whole cell, patch-clamp recording techniques. B: synaptic response from a medium spiny neuron to a train of synaptic potentials (10 field stimuli at 20 Hz;arrows indicate train stimuli); note firing of action potentials on top of synaptic response. A sequence of synaptic responses such as that illustrated at left (1).Calcium transients recorded from the spiny neuron (280 ms/frame) responding to cortical stimulation in 1 (2). Note that each synaptic response has a

corresponding calcium transient. First derivative obtained from calcium recordings (3). Dots indicate the onset of the responses used to build the raster plotsshown in D. C: mapping of all neurons present in the field of view (circles). Cells triggered by cortical stimulation: 33/252 (13%) are indicated with red filledcircles. Cells unresponsive to cortical stimulus but excitable, as shown after high K application, are indicated by empty circles in this and other figures. Scalebar: 50 m. D: population raster plot: each row represents an active neuron. Some cells present spontaneous activity before and after the stimulus. Notesynchronized responses from neurons several hundreds of microns apart during cortical stimulation (red). Histogram at the bottom represents the percentage ofresponding cells triggered by each cortical stimulus. E: cross-correlation map including all active cells (P 0.05; see METHODS). One spontaneously active cellnever responded to cortical stimulation (navy blue lines).




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tion allowed us to compare different states of network activity

rigorously over time (Brown et al. 2005; Sasaki et al. 2007).The normalized inner product (see METHODS) of all possiblevector pairs provides a measure of the similarity among states(Sasaki et al. 2006; Schreiber et al. 2003). To evaluate thissimilarity along network activity, we plotted all vector pairs asa matrix (Fig. 6A). Notably, abrupt transitions in the similarityindex showed the presence of cluster-like structures (Sasakiet al. 2006).

To compare network responses induced either by corticalstimulation or by NMDA bath application, we reduced thedimensionality of the vectors using LLE (see METHODS), atechnique for nonlinear dimensionality reduction (Brown et al.2005; Roweis and Saul 2000; Stopfer et al. 2003). The new

vectors were projected in two dimensions (Fig. 6B). Clusters ofpoints formed trajectories representing the responses over timeto specific stimuli. Trajectories illustrate different subgroups ofneurons coactive within each functional state (Brown et al.2005; Stopfer et al. 2003). Thus changes in the functional stateof the network can easily be followed. In Fig. 6B, state 1represents network response to cortical stimuli in control con-ditions. State 2 depicts NMDA-induced network activity with-out electrical stimulation. State 3 represents network responseto the same cortical stimuli (given in the state 1) in the bathpresence of NMDA. Figure 6C, top, shows the raster plot usedto reconstruct network states, whereas bottom shows histo-grams with the percentage of co-active neurons along time.Cortical stimulation in the presence of NMDA recruited many

more striatal neurons (Fig. 6C3, blue dots and peaks) than in

control conditions (Fig. 6C1, green dots and peaks) and pro-duced peaks of synchrony much larger than those produced byNMDA only (Fig. 6C, 2, 4, and 5, red dots and peaks).Spontaneous peaks of synchrony induced by bath NMDA were20% the size of those induced by cortical stimulation (cf. Fig.6C, 15, bottom). Nonetheless the general behavior of theactive network after a cortical stimulus (Fig. 6C, 4 and 5)remained essentially comparable to the activity before thestimulus (Fig. 6C2 and see following text). In fact, timehistograms showed similar numbers of spontaneous peaks ofsynchronous activity (Fig. 6C, bottom; P 0.05) before andafter the cortical stimulus. Moreover, cortical stimulation (Fig.6C3) elicited a trajectory looping back to the NMDA-induced

dynamics (Fig. 6B), showing recurrent activation of the sameassembly after a perturbation (Stopfer et al. 2003). Figure 6Dshows the spatial distribution of the neurons involved in thedifferent states. Hierarchical cluster analysis revealed the ex-istence of different neuronal subsets (assemblies) that underlienetwork states (Fig. 6F). Each state was basically sustained bya different neuronal assembly. A state transition implies achange of neuronal assembly (alternation). However, someelements are shared by different states (core neurons). Thususing a simple algorithm we could distinguish recursive peaksof synchronization reflecting functional states that involvedifferent sets of neurons. We conclude that spatiotemporalfiring patterns codified in multidimensional vectors are suffi-cient to reconstruct the dynamics of a given network.

BA

PD 14 PD 29

14 18 22 26 30

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edce

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FIG. 5. Age independency of network dynamics. Neurons in a striatal slice loaded with fluo-4 AM, PD 14 (A) and PD 29 (B). Pictures are the result ofaveraging 720 consecutive frames (background is not subtracted). Scale bar: 100 m. C: number of loaded cells as a function of postnatal day. Each pointrepresents one brain slice. The line represents an exponential fit. D: number of peaks per 201 10 s time epoch as a function of postnatal day. F, 1 brain slice., best linear fit. Note that although the number of loaded cells decreases with age, the network dynamics remains constant.




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Pattern generation denoted by functional states sustained bycell assemblies

The preceding analyzed states included some with imposedsynchronization, i.e.: with electrical cortical stimulation. Ifnetwork dynamics induced by NMDA are mediated by cellassemblies, then one may expect the appearance of distinguish-

able functional states evoked by the excitatory tonic drivebrought about by NMDA only (without electrical stimulation).To address this issue, we investigated the relationships be-tween the peaks of synchronous activity induced only byNMDA (similar results were obtained in n 14/15 slices thatpresented NMDA-induced peaks of synchrony).

To observe alternation between functional states, we inves-tigated the NMDA-induced network dynamics in the striatumover long periods of time (Fig. 7, n 15 slices). Brief imageseries (100250 s in duration) were obtained at differentintervals for 1 h (Figs. 7C, 1-,3 top, representative imageseries obtained at different times during the experiment areshown separated by vertical lines).

A similarity correlation matrix (similarity index, Fig. 7A)

between these vectors clearly disclosed abrupt transitions in-dicating the presence of statistically significant cluster-likestructures (Sasaki et al. 2006). We projected these vectors intotwo dimensions (Fig. 7B, colored circles) by further reducingvectors dimensionality with LLE (Brown et al. 2005; Roweisand Saul 2000; Stopfer et al. 2003). This projection allowed usto observe a variety of functional states in the network asclusters that follow a series of trajectories in sequence. Wefound a different set of neurons (cell assemblies) with corre-lated synchronous firing for each functional state (Brown et al.2005; Stopfer et al. 2003) (Fig. 7D; blue, red, green). Thereforethe method allows us to follow the evolution in time of thefunctional states within the network. Experiments similar to

that illustrated in Fig. 7B demonstrated the existence of robustnonrandom cell assemblies displaying co-active neurons alongtime with recurrent and alternating activity (Figs. 7, B and C)(Sasaki et al. 2006). States were continuously revisited with nostate having a preference or a significantly higher probability ofrecurrence (Fig. 7B; see percentages of trajectories out of eachstate including recurrences), demonstrating the existence ofvarious semi-stable network attractors. Time histograms withseveral synchrony peaks (asterisks; Fig. 7C, bottom) show thealternation of activity between different cell assemblies thusdemonstrating multistable dynamics.

Figure 7D shows the spatial distribution of neurons gener-ating the patterns and producing the different functional states.Network states share some elements but most of the neurons

are dissimilar (Fig. 7E). Finally, hierarchical cluster analysisconfirmed the recruitment of different cell assemblies during

specific states (Fig. 7F). Nevertheless there is a small core ofcells shared by all the network states (8% of neurons in-volved in the synchrony peaks).

We concluded that functional states, denoted by spontaneouspeaks of synchrony (Barnes et al. 2005), are generated by thecoordinated participation of cell assemblies, as it is the case ofunit CPGs (Grillner 2006). The transitions from one state to

the other display long-term network dynamics. The return ofthe same cell assemblies after net traveling through differenttrajectories shows that the elements of these modules arerobustly associated, allowing alternating participation. Also, acore of neurons is shared by all the states.

Mechanisms underlying network dynamicsfor pattern generation

For a preliminary investigation into the synaptic and intrin-sic requirements for multistable network dynamics, we chal-lenged network activity with both synaptic and intrinsic ionchannel antagonists. Fast synaptic inhibition within the striatal

circuit (Czubayko and Plenz 2002; Koos et al. 2004; Tepperet al. 2004; Tunstall et al. 2002) was tested with the GABA

A

receptor antagonist, bicuculline, which was applied once thenetwork became active.

Figure 8 shows that on exposure to 10 M bicuculline, thenumber of state transitions is drastically reduced in theNMDA-treated slices (n 6 slices). The network becomeslocked in a preferred state, which is recurrently revisited.Similarity indexes representing network dynamics suggest theabsence of diverse cell assemblies (Fig. 8A). Only occasionaltransitions to another state occurred (Fig. 8B). Paradoxically,the raster plot of network activity showed an increased fre-quency of recurrent synchrony peaks: from 2.7 0.2 peaks in

the NMDA condition to 8.3 2 peaks in the presence ofbicuculline (P 0.01). There was also an increase in thenumber of coactive cells supporting the preferred state (Fig.8C). Most synchrony peaks, however, correspond to the samedominant state. Neurons involved in network sates are shownin Figs. 8, D and E. Hierarchical cluster analysis did not revealcluster-like structures in spite of synchronization (Fig. 8F),meaning that all active neurons were embedded into the samestate. Cortical stimulation in the presence of bicuculline re-cruited even more striatal neurons than those obtained incontrol conditions or in the presence of NMDA alone (data notshown). Thus fast GABAergic transmission is a necessaryrequirement for the network to exhibit multistable dynamicsand different functional states.

We then tested the role of fast AMPA glutamatergic trans-mission in the orchestration of cell assemblies. Note that

FIG. 6. Visualizing network states. A: similarity indices of all vectors representing network dynamics as a time function (see METHODS). Note cluster-likestructures in the pseudocolored matrix (Sasaki et al. 2006). B: multidimensional reduction of vectors using locally linear embedding (LLE). Each point representsa vector at a given time (see METHODS). Consecutive time points form different trajectories representing specific experimental conditions, each producing adifferent network state. State 1: cortical stimulation under control conditions. State 2: NMDA-induced network activity. State 3: cortical stimulation in thepresence of NMDA in the bath. C: raster plot used to reconstruct the network states (370 ms/frame; top). Rows represent activity of individual cells. Verticallines delimit time series C1C5 (186 s/image series; C1: t 10 min, C2: t 35 min, C3: t 40 min, C4: t 45 min, C5: t 50 min). Histogram (bottom)represents percentage of coactive cells over time in the same experiment. Peaks of synchrony without electrical stimulation (asterisks) were present both beforeand after cortical stimuli (perturbation). Cortical stimulation (imposed synchrony or perturbation) apparently did not change the NMDA-induced networkdynamics (spontaneous). Note that patterns of active cells change over time. The heights of synchrony peaks during cortical stimulation appear truncated ( 1 and3). D: spatial distribution of neurons involved in the different states. Scale bar 100 m. E: percentage of coactive cells during different functional states. Notethe participation of very few cells in different states. F: hierarchical cluster analysis of cells participating in the network states (n 92 cells). colored boxesindicate the different states. Note that different cell assemblies sustain each recurrent state (imposed or spontaneous) although some cells are shared by differentstates.




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NMDA blockers cannot be used: either the withdrawal ofNMDA or the inhibition of NMDA receptors stops multistatedynamics in slices from this quiescent circuit under controlconditions.

Active networks were challenged with the AMPA receptorantagonist CNQX (10 M) (Fig. 9A). As shown in the rasterplot of Fig. 9A1, CNQX drastically reduced the number ofactive cells (cf., leftand right) and disrupted the appearance ofsynchrony peaks in all NMDA-treated slices (Fig. 9A, bottom;n 6 slices). Interestingly, a few neurons that were activeduring synchrony peaks maintained spontaneous activity in theabsence of fast AMPA transmission (Fig. 9A, 1 and 2, gray

circles) (Vergara et al. 2003). Also in the presence of CNQX,cortical stimulation was unable to recruit striatal neurons (datanot shown) and all correlated activity was suppressed (Fig. 9A).

Intrinsic conductances are known to participate in patterngeneration in single cells. In particular, in spiny neurons duringactivity set into action by a tonic excitatory drive, the activityof voltage-gated L-type calcium channels has been shown to beessential to produce bistability and I-V plots with a negativeslope conductance region (see Fig. 1E5) (see also Vergara et al.2003). L-type calcium channels promote the generation ofplateau potentials capable of sustaining repetitive firing thatoutlasts input duration. We therefore tested the effects ofblocking L-type calcium channels with the antagonist nicardi-pine (5 M). As shown in the raster plot of Fig. 9B1, nicardi-

pine only slightly reduces the number of active neurons (n 5 slices; cf., left and right panels; n 41 vs. 35 neurons), itnevertheless reduces the number of peaks with spontaneoussynchronous activity. Many cells defining previous functionalstates continued spontaneously active in the presence of nicar-dipine (Fig. 9B, 1 and 2, gray circles), but they were rarelysynchronized to produce functional states. We conclude thatplateau potentials resulting from the activity of voltage-gatedL-type calcium channels are required for network synchroni-zation (Yuste et al. 2005) and pattern generation produced bycell assemblies in NMDA-treated slices.

Shared core of neurons

In most slices presenting multistable network dynamics (n20/25 slices), we found a core of neurons, which were sharedby all states. So we aimed at recording electrophysiologicallysome of these neurons after their identification as a part of acore.

These neurons were 8 2% of all active cells in the networkstates (n 20 slices). Some of these core neurons exhibitedperiodic calcium transients during NMDA treatment or CNQX.Bicuculline disrupted the periodic firing of these cells. Cell-attached (Fig. 10A1) or whole cell current-clamp recordings(Fig. 10, C1 and D1) were performed on some of these cells,and we found that 40% of the core assembly (n 4/10 cells)showed firing properties characteristic of GABAergic inter-

A B

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neurons (Plenz and Aertsen 1996; Tepper et al. 2004), suchas high-frequency bursts (Fig. 10A) and periodic pacemak-ing activity (Fig. 10D, 13). However, by fixing fluorescenceof cells active during a given experiment (see METHODS), Fig. 11shows that most active cells in the different assemblies wereimmunoreactive to either substance P or enkephalines.

D I S C U S S I O N

We demonstrate that an isolated striatal slice has the neces-sary circuitry to transform a tonic excitatory drive into sequen-tial correlated activity of several cell assemblies that exhibitrecurrent, alternating, and synchronized spatiotemporal activ-

ity patterns. Although most striatal cells are silent undercontrol conditions, either cortical stimulation or bath applica-tion of NMDA, induce synchronized burst firing among as-semblies of striatal neurons. Cell assemblies defined functionalstates during a given experiment and alternate their activity asthough belonging to unit CPGs (Grillner 2006). Alternation ofactivity and frequent recurrences of the same states suggestattractor network dynamics.

Ca2 imaging of neuronal populations (Mao et al. 2001;Sasaki et al. 2006; Schwartz et al. 1998; Stopfer et al. 2003)and simultaneous whole cell recordings show that calciumtransients correspond to burst firing in single cells, thesynchronization and recurrence of which generates the ac-

FIG. 10. GABAergic interneurons are part of the core assembly. A: cell-attached (top) and calcium imaging recordings (bottom) from a neuron with regularbursting activity in the presence of 8 M NMDA that belongs to a core of shared neurons (1). Records show high-frequency regular bursts with theircorrespondent calcium transients. Interspike interval histogram of intraburst activity. Note a peak value around 11 ms. B: fluorescence image of the cell recordedin A. Scale bar 15 m. C: voltage responses (top) to current steps (bottom) recorded from another neuron showing regular (pacemaker) bursting activity (1). Thesame neuron filled with biocytin, showing that it corresponds to a cell with aspiny varicose dendrites. Scale bar 15 m (2). D: cell in Cexhibits regular burstingduring NMDA (8 M). Note the periodicity of the up states (1). Histogram (from 1) shows the bimodal distribution over time of membrane potential (2).

D3: power spectrum from D1. Note activity with a period of2.18 s.




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tivity patterns. Pattern generation during network statesdepend on synaptic and intrinsic properties as well as theactivity of a core of active neurons (Berke et al. 2004). Insummary, this work shows how pattern generation andcorrelated activity is generated in the striatal microcircuitryin vitro identifying the neuronal elements involved. Theconnections and modulation of these elements deserve fur-ther study because they may reveal basic general propertiesof striatal microcircuitry and may further suggest in vivoexperiments.

Voltage transitions in striatal neurons

Striatal neurons, both in vivo and in vitro, exhibit spontane-ous voltage transitions that sustain burst firing. Transitionsoccur between a quiescent down state and a recurrent burstingup state (Herrling et al. 1983; Kerr and Plenz 2002, 2004;Mahon et al. 2006; Vergara et al. 2003; Wilson 1993). It islargely unknown how these transitions reflect network activityand how they propagate through the network. It is probable thatdifferent classes of bursts exist, for example, during differentfunctional states (e.g., sleep vs. movement) (N. Vautrelle,personal communication). Here we took advantage of the tonicdrive provided by NMDA to induce the transitions because inthe striatum (Ossowska 1995), as in other circuits (Gordon andWhelan 2006; Grillner et al. 1981; Guertin and Hounsgaard1998; Hsiao et al. 1998; Kiehn 2006; Takakusaki et al. 2004b),

NMDA sets into action patterned activity that generates move-ment. Accordingly, it was demonstrated that NMDA inducescorrelated activity that evolves in time. In contrast to controlconditions in which the striatum has only a few active cellswith no correlated activity, both cortical stimulation and thetonic excitatory drive provided by NMDA exposure (Vergaraet al. 2003) recruit neurons spread over a wide area; which, inthe case of NMDA, spontaneously express particular spatio-temporal dynamics, indicating that the striatal microcircuit invitro preserves a set of unit CPGs (Grillner 2006), that is,even if most connections present in more intact preparationsare severed, remaining sets of neurons, probably belonging tolarger CPGs or modules in vivo, preserve some of the proper-

ties of these larger modules. In support of this inference,independent evidence in the cortex suggests that cell assem-blies activity is preserved along different spatial scales (Plenzand Thiagarajan 2007).

Mechanisms of network dynamics

The conditions that generate circuit dynamics reside in boththe synaptic and intrinsic properties of striatal neurons. NMDAinduces synchronous peaks of neuronal activity emerging fromthe co-activation of robust and recurrent cell assemblies thatalternate in their patterns displaying a sequence of trajectories.These results suggest the presence of robust mechanisms thatmaintain and stabilize the cells participating in the assemblies.

A B C

D E F

Fluo4 ENK

Fluo4 SP

Merged

Merged

FIG. 11. Most active cells in cell assemblies are medium spiny projection neurons. A: confocal image of a field of view of the dorsal striatum showing cellsactive during the experiment: loaded with fluo-4 AM. Fluorescence with EDAC, Scale bar 10 m. B: confocal image of the same field showing that several ofthese neurons were immunoreactive for ENK, a specific marker of spiny neurons. C: superimposition ofA and B. D: confocal image of a field of view with somecells loaded with fluo-4 AM in the dorsal striatum. Fluorescence with EDAC, scale bar 10 m. E: confocal image of the same field showing that several of theseneurons were immunoreactive for SP, another specific marker of spiny neurons. F: superimposition of D and E.




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In particular, AMPA transmission appears to be necessary forturning on network dynamics. In fact, the number of activeneurons drastically falls when AMPA transmission is blocked.

Because all glutamatergic synapses in the striatum originatefrom cortical or thalamic afferents, cortico-thalamic afferentsare likely to be the source of tonic excitatory drive underlyingsynchronous activity. NMDA presumably generates an in-crease in the tonic excitatory drive conveyed by cortico-thalamic afferents (Grillner 2006; Grillner et al. 1981), andalthough some neurons maintain spontaneous activity in thepresence of CNQXor after dissecting away the cerebralcortex (Vergara et al. 2003)the analysis shows that thisbehavior is asynchronous so that the emergence of striatal cellassemblies appears to require fast AMPA glutamatergic trans-mission, presumably originating from cortical and thalamicneurons (Kasanetz et al. 2006; Yuste et al. 2005). The mostparsimonious interpretation of these results is that cortico-thalamic afferents are essential for the appearance of multistatenetwork dynamics. That is, network dynamics are commanded

by the cortex and/or the thalamus (Kasanetz et al. 2006; Magillet al. 2001). However, the actual relationship between corticalor thalamic activation and cell assembly activity in the striatumneeds further investigation. It is known that activation ofNMDA receptors is enough to induce bistability in striatalneurons, amplifying the excitatory drive conveyed by corticalor thalamic inputs (Grillner 2006; Grillner et al. 1981) so thatactivity (e.g., from the cortex or thalamus) can be relied on.And although cell assembly activity has been recorded in thecortex using NMDA plus dopamine, it has not been recordedafter NMDA only (Tseng and ODonnell 2005). Finally, stri-atal assemblies receive the convergence of widespread corti-cal areas, and slices cannot contain all these areas (Parthasar-athy and Graybiel 1997). However, cortical cell assemblies

driving striatal cell assemblies are not excluded and may bepossible in several conditions (e.g., with added dopaminereceptor agonists). Therefore striatal assemblies may representthe coordinated activity between cortical areas, that onceformed by development and learning, only need a corticaltrigger or a subset of the original stimuli for activation.

In addition, our data show that the striatum does not simplyfollow cortical commands. The role of the striatal circuitproper in the management of cortical drives is revealed by theresult of inhibiting GABA

Areceptors. Blockade of GABA

A

inhibition not only increases the peaks of synchronous activityand recruits more cells during synchrony peaks but also shiftsthe network toward a preferred state that recruits most active

neurons. Without inhibition, the striatal circuit gets tied up ina preferred state, from which alternation and selection amongdifferent cell assemblies becomes unlikely. Therefore we con-clude that inhibitory circuits in the striatum are responsible fortransforming cortical commands into a sequential activity ofstriatal cell assemblies (Ossowska 1995; Wickens and Oorschot2000). In addition, the blockade of L-type calcium channelsdisrupts the peaks of synchrony. This result confirms thatintrinsic conductances, such as voltage-gated calcium chan-nels, by generating plateau potentials may work as synchroni-zation enablers of neuronal networks (Kiehn 2006; Yuste et al.2005). Thus NMDA-induced network dynamics in the striatumrequires the participation of both synaptic and intrinsic mech-anisms.

Finally, electrophysiological recordings of neurons exhibit-ing Ca2 transients showed that most of the active neurons incell assemblies were medium spiny projection neurons. Thiswas confirmed by immunocitochemistry (Fig. 11). Neverthe-less, a subpopulation of neurons shared by all functional states(see Parthasarathy and Graybiel 1997) exhibited periodic Ca2

transients and the firing properties of pacemaking GABAergic

interneurons (e.g., Berke et al. 2004; Tepper et al. 2004). Thefact that striatal GABAergic interneurons comprise 25% ofstriatal cells and yet make up 40% of the neurons active inmultiple assemblies suggests that striatal interneurons partici-pate in the orchestration of multistate dynamics (Berke et al.2004). Activity of pacemaker neurons is a frequent finding inCPGs (Grillner 2006; Yuste et al. 2005).

Functional implications and perspectives

Recurrent and alternating bursting is characteristic of cellassemblies included in CPGs in vivo and in vitro (Barnes et al.2005; Grillner 2006; Ikegaya et al. 2004). Synchronized activ-

ity of these modules exhibit attractor dynamics (Cossart et al.2003), providing a unified description for circuits encoding thestorage and retrieval of long-term and working memory. At-tractors are seen as memory traces retrieved through excitatorytonic driving or partial cues useful for executing motor pro-grams. The persistence of cell assemblies along time is due torecurrent connectivity (Barnes et al. 2005; Tsodyks 2005). Thealternation of their activity is under neuromodulatory control(Yuste et al. 2005). Apparently, the properties of these micro-circuits are at the interface between small networks and globalbrain functions (Plenz and Thiagarajan 2007). Their distur-bance provokes abnormal processes of synchrony, associatedwith different disorders such as schizophrenia and ParkinsonDisease (Schnitzler and Gross 2005; Uhlhaas and Singer

2006). We show that an isolated circuit set into action, in vitro,can exhibit synchronized states in specific cell assembliesemerging and returning during relatively long periods of time.

A C K N O W L E D G M E N T S

We thank Drs. Ranulfo Romo, Roman Vidaltamayo, and Nicolas Vautrellefor critically reading the present manuscript. We thank R. Velazquez forprogramming a part of the software for analysis, O. Jaidar and A. Hernandezfor some experiments in older animals, and also A. Laville, C. V. Rivera, T.Fiordelisio, and N. Jimenez, for technical support and advice.

G R A N T S

This work was supported by grants from a Project Program grant IMPULSA 03to J. Bargas, A. Hernandez-Cruz, E. Galarraga, and R. Drucker-Colin, by

Consejo Nacional de Ciencia y Tecnologa (Mexico) Grants 42636 to E.Galarraga and 49484 to J. Bargas, and by Direccion General de Asuntos delPersonal Academico, Universidad Nacional Autonoma de Mexico GrantsIN201607 to J. Bargas and IN201507 to E. Galarraga.

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