Circuitos Activados Por Acetilcolina

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doi:10.1152/jn.90975.2008101:737-749, 2009. First published 19 November 2008;J NeurophysiolHernndez-Cruz, Elvira Galarraga and Jos BargasLuis Carrillo-Reid, Fatuel Tecuapetla, Osvaldo Ibez-Sandoval, ArturoCompositional Properties to Striatal Cell AssembliesActivation of the Cholinergic System Endows

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Activation of the Cholinergic System Endows Compositional Properties

to Striatal Cell Assemblies

Luis Carrillo-Reid, Fatuel Tecuapetla, Osvaldo Ibanez-Sandoval, Arturo Hernandez-Cruz, Elvira Galarraga,

and Jose BargasDepartamento de Biofsica, Instituto de Fisiologa Celular, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico

Submitted 28 August 2008; accepted in final form 13 November 2008

Carillo-Reid L, Tecuapetla F, Ibanez-Sandoval O, Hernandez-Cruz A, Galarraga E, Bargas J. Activation of the cholinergic systemendows compositional properties to striatal cell assemblies. J Neuro-

physiol 101: 737749, 2009. First published November 19, 2008;doi:10.1152/jn.90975.2008. Striatal cell assemblies are thought toencode network states related to associative learning, proceduralmemory, and the sequential organization of behavior. Cholinergicneurotransmission modulates memory processes in the striatum andother brain structures. This work asks if the activity of striatal

microcircuits observed in living nervous tissue, with attributes similarto cell assemblies, exhibit some of the properties proposed to benecessary to compose memory traces. Accordingly, we used wholecell and calcium-imaging techniques to investigate the cholinergicmodulation of striatal neuron pools that have been reported to exhibitseveral properties expected from cell assemblies such as synchronousstates of activity and the alternation of this activity among differentneuron pools. We analyzed the cholinergic modulation of the activityof neuron pools with multidimensional reduction techniques andvectorization of network dynamics. It was found that the activation ofthe cholinergic system enables striatal cell assemblies with propertiesthat have been posited for recurrent neural artificial networks withmemory storage capabilities. Graph theory techniques applied tostriatal network states revealed sequences of vectors with a recursivedynamics similar to closed reverberating cycles. The cycles exhibited

a modular architecture and a hierarchical organization. It is thenconcluded that, under certain conditions, the cholinergic system en-ables the striatal microcircuit with the ability to compose complexsequences of activity. Neuronal recurrent networks with the charac-teristics encountered in the present experiments are proposed to allowrepeated sequences of activity to become memories and repeatedmemories to compose learned motor procedures.

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

The basal ganglia participate in the acquisition, storage, andexecution of motor skills that commonly involve sequences ofmovements (Aosaki et al. 1994; Cools 1980; DeLong 1981).The main stage of the basal ganglia is the striatum, which

displays a dense staining for cholinergic markers (Holt et al.1997). At the cellular level, cholinergic interneurons enhancethe excitability of neostriatal projection neurons through theactivation of muscarinic receptors that regulate several voltage-dependent ion currents (e.g., Calabresi et al. 1998; Figueroaet al. 2002; Galarraga et al. 1999; Perez-Burgos et al. 2008;Shen et al. 2005). At the same time, presynaptic muscarinicreceptors modulate the strength of the connections that theseneurons exert on their targets (Perez-Rosello et al. 2005). In

addition, acetylcholine enhances the activity of striatal inter-neurons (Koos and Tepper 2002) that play a prominent role incell assembly orchestration (Berke et al. 2004; Carrillo-Reidet al. 2008). Therefore it is difficult to visualize a priori how allthese cholinergic actions combine at the microcircuit level.Accordingly, this study is aimed at describing cholinergicactions in the striatal microcircuit as a first step toward atop-down approach to link the activity in small networks with

cellular physiology.The cholinergic system has long been associated with cognitivefunctions and memory processes in the striatum (Calabresi et al.1998; Diaz del Guante et al. 1991) and other brain regions(Hasselmo and Giocomo 2006). Cholinergic modulationchanges the ability of animals to switch among differentbehaviors (Cools 1980), and the activity of the striatal cholin-ergic system enables temporal windows for procedural learningto take place (Morris et al. 2004).

Nevertheless, possible neuronal microcircuits that may func-tion as correlates for cognitive abilities and their cholinergicmodulation are hard to demonstrate in living brain tissue. Forexample, to manage memory storage during cognitive pro-cesses several types of neuronal circuitries have been posited,

such as Hebbian cell assemblies, synfire chains or closedreverberating circuits whose neurons should be capable ofsynchronization and alternation of their correlated activityamong diverse neuronal pools, thus conforming functionalcycles of recurrent activity (Abeles 2003; Hammer 2003; Hebb1949; Lorente de No 1938). Neuronal microcircuits with thepotential to behave in this way have rarely been observeddirectly in the mammalian brain tissue (e.g., Carrillo-Reid et al.2008; Ikegaya et al. 2004; Reyes 2003) or even in artificialnetworks (Hammer 2003). Therefore their functioning andcapabilities have not been fully described. As an approxima-tion, however, multi-unitary electrophysiological recordings,associated with field recordings, have inferred the workings ofrecursive cell assemblies based on their correlated firing (Abe-les 2003; Beiser et al. 1997; Berke et al. 2004; Dragoi andBuzsaki 2005).

In the present study, we show, in the neostriatal brain slicepreparation, neuronal pools with several attributes expectedfrom Hebbian-like cell assemblies capable of memorization(Abeles 2003). First, we observed these neuronal pools directlyby combining whole cell electrophysiological recordings anddynamic multicellular calcium imaging. Second, we used mul-tidimensional reduction of network dynamics to rigorously

Address for reprint requests and other correspondence: J. Bargas, Dept. deBiofsica, Instituto de Fisiologa Celular, Universidad Nacional Autonoma deMexico (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 101: 737749, 2009.First published November 19, 2008; doi:10.1152/jn.90975.2008.

7370022-3077/09 $8.00 Copyright 2009 The American Physiological Societywww.jn.org


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compare the activity of neuronal pools over time. Third, weused hard and fuzzy clustering algorithms, taking the Dunnsindex as a validity function, to depict the different states of thenetwork in a plane (Bezdek et al. 1997; Carrillo-Reid et al.2008; Sasaki et al. 2007). Finally, we used graph theory,applied to depicted network states, to show that there areclosed reverberating cycles that exhibit a modular structure and

a hierarchical organization as assessed by graph-based hierar-chical conceptual clustering (Jonyer et al. 2001). Interestingly,the analysis demonstrate that striatal microcircuits present thecardinal property of compositionality, which refers to theability of a given system to build complex hierarchical repre-sentations, in terms of simpler parts and their relationships(Bienenstock and Geman 1995; Hammer 2003).

M E T H O D S

Slice preparation

Transverse corticostriatal slices (300 m thickness), were obtainedfrom PD14-18 Wistar rats as described in previous work from our

laboratory (Carrillo-Reid et al. 2008; Vergara et al. 2003). All proce-dures conformed to the guidelines of the Universidad Nacional Au-tonoma de Mexicos Animals Scientific Procedures Committee. Sliceswere 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.

Calcium imaging

Slices were loaded for calcium imaging by incubation at roomtemperature in the dark for 20 min with 10 M fluo 4-AM (Tef Labs,Austin, TX) in 0.1% dimethylsulphoxide (35C), equilibrated with95% O

2-5% CO2. Slices were bathed with control saline (see preced-

ing text) in a perfusion chamber placed on the stage of an uprightmicroscope equipped with a 20, 0.95 NA water-immersion objec-tive (Olympus BX51WI; Olympus). Illumination pulses at 488 nm(50- to 100-ms exposure) were delivered to the preparation with aLambda LS illuminator (Sutter instruments, Novato, CA), connectedto the microscope via fiber optics. Experiments were performed atroom temperature.

Images were acquired with a cooled digital camera (CoolSNAPES2, Photometrics, Roper Scientific, Tucson, AZ) at 250500 msusing RS Image (Photometrics; Roper Scientific). The imaged fieldwas 800 600 m in size. Short movies (250 s) were taken at timeintervals of 520 min during 1 h.

The number of fluo-4-loaded neurons in the field was determined atthe end of the experiment with a brief exposure (5 s) to salinecontaining 50 mM KCl. This maneuver disclosed all fluo-4-labeled

neurons (either active or silent during the experiment). Cells activeduring the experiment were identified, and the ratio of active versussilent cells was obtained. Spontaneous or evoked calcium transients,together with voltage responses were recorded electrophysiologicallyin some cells, both in control saline and during the application ofdifferent drugs (see RESULTS). This allowed us to study changes ofnetwork activity under different pharmacological conditions.

Drugs

Stock solutions were prepared before each experiment and added tothe perfusion solution in the final concentration indicated. N-methyl-D-aspartate (NMDA), muscarine chloride, eserine and atropine sulfatewere obtained from Sigma (St. Louis, MO).

Electrophysiology

An Axoclamp 2B amplifier (Axon Instruments, Foster City, CA)was used to perform whole cell current- and voltage-clamp record-ings. Signals were filtered at 13 kHz and digitized at 39 kHz withan AT-MIO-16E4 board (National Instruments, Austin, TX) in a PCcomputer. Data acquisition used software designed in the LabViewenvironment. Patch pipettes (36 M) were filled with (in mM): 115

KH2PO4, 2 MgCl2, 10 HEPES, 0.5 EGTA, 0.2 Na2ATP, and 0.2Na

3GTP. In some experiments, biocytin 0.5%, and fluo-4 salt (1020

M) were added to the recording pipettes.

Image analysis

Image processing was done as previously described (Carrillo-Reidet al. 2008) using Image J (v.1.36, National Institutes of Health) andcustom-made programs written in IDL (Cossart et al. 2003; Mao et al.2001) and MATLAB (The Math-Works, Natick, MA). Briefly, activeneurons were semi-automatically identified, and their mean fluores-cence was measured as a function of time. Calcium-dependent fluo-rescence signals were computed as (F

i Fo)/Fo, where Fi: fluores-cence intensity at any frame, and F

o: resting fluorescence, i.e., average

fluorescence of the first 4 frames of the movie.Calcium signals elicited by action potentials were detected based on

a threshold value given by their first time derivative (2.5 times the SDof the noise value). Recordings were inspected manually to removeartifacts (Carrillo-Reid et al. 2008; Ikegaya et al. 2004; Sasaki et al.2007).

Statistical methods

IDENTIFYING NETWORK STATES. The method employed to detectstriatal network states has been published (Carrillo-Reid et al. 2008).Briefly, to determine if neuronal activity as observed by calciumtransients and recorded from different cells simultaneously was cor-related, the numbers of simultaneous activations per trial were de-tected. To determine the P value of simultaneous calcium transients

occurring by chance, the distribution under the null hypothesis ofindependent transients using Monte Carlo simulations with 1,000replications were computed (Mao et al. 2001).

The degree of firing correlation between active cells was calculatedwith the Jaccard correlation coefficient. Correlation maps were con-structed where line thickness was proportional to the correlationcoefficient. We also constructed pseudocolored cross-correlationmaps to directly inspect all correlated cell pairs. To identify peaks ofsynchronous activity (i.e., including more cells than those expected bychance), Monte Carlo simulations were also used to estimate thesignificance of concurrent firing. The chosen threshold correspondedto a significance level ofP 0.01. Peaks of synchronous activity thatremained significant during the experiment were selected for furtheranalysis.

To identify the neurons underlying each network state and their

dynamics, 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-inducedup-states last between 0.5 and 5 s) (Carrillo-Reid et al. 2008). In thismanner, peaks of synchronous activity were vectorized so that burst-ing over time of all active cells with correlated firing during the timebin represent the elements of each vector, corresponding to a networkstate. Neuron pools represent the cells from which specific networkstates arise. We previously showed that first time derivatives of thecalcium transients correspond to the duration of electrophysiologi-cally recorded bursts or up-states in single neurons (Carrillo-Reidet al. 2008). Therefore the set and sequential activation of thesevectors can be used to describe network activity as a function of time(Brown et al. 2005; Carrillo-Reid et al. 2008; Sasaki et al. 2007).

738 CARILLO-REID ET AL.

J Neurophysiol VOL 101 FEBRUARY 2009 www.jn.org


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To identify if a given vector is formed by similar or differentsubsets of firing neurons, we measured a similarity index between allnetwork vectors appearing during an experiment by computing theinner product (similarity index) of all possible vector pairs (Carrillo-Reid et al. 2008; Sasaki et al. 2007; Schreiber et al. 2003). Then weplotted the similarity indexes as a pseudocolored matrix in whichfunctional states sustained by significantly correlated or synchronizedneural vectors appear as cluster-like structures (Carrillo-Reid et al.

2008; Sasaki et al. 2007).To follow network dynamics within the neuronal microcircuits, we

reduced the dimensionality of the neural vectors with locally linearembedding (LLE), a technique for nonlinear dimensionality reduction(Brown et al. 2005; Roweis and Saul 2000; Stopfer et al. 2003). It waspreviously found that trajectories of high dimensional data are notwell described with simple linear dimensionality reduction methods(Brown et al. 2005; Carrillo-Reid et al. 2008). In LLE, the vectorsrepresenting network states were projected into a vectorial space madeup by the first two dimensions with data points representing vectors ata given time. A cluster of data points represent vectors with similardynamics. Arrows joining clusters of data points indicate the trajec-tories followed by the synchronous activity involving different net-work states. To choose the optimal number of clusters appearing in anexperiment, we used hard and fuzzy clustering algorithms taking the

Dunns index as a validity function (Bezdek et al. 1997; Carrillo-Reidet al. 2008; Sasaki et al. 2007). To determine the number and identityof the neurons in each neuron pool belonging to each network state,hierarchical cluster analysis was computed using Euclidean distancesand the nearest neighbor single linkage method (Systat, Richmond, CA).

IDENTIFYING CLOSED CYCLES OF ACTIVITY BETWEEN NETWORK

STATES. We asked whether activity trajectories of neural vectorsinclude reverberating loops characteristic of Hebbian-like cell assem-blies and special classes of synfire chains (Abeles 2003; Harris 2005;Hebb 1949). Thus to visualize the trajectories of the synchronizedactivity between network states, we constructed isomorphic directedgraphs (or digraphs) of network state transitions. A digraph is a graphformed by vertices and directed edges (arrows pointing to 1 endpoint)(Diestel 2005; Gerber 1990). Peaks of synchronous activity, belong-

ing to specific network states, denote the vertices of the digraphs.Arrows denote the trajectories from one state to the other. Closedcycles were identified when they exhibit the properties of Hamiltonianor Eulerian cycles. A Hamiltonian cycle of a digraph (directed graph)G is a closed walk that contains every vertex of G exactly once. AnEuler tour is a closed walk traversing every edge exactly once (Diestel2005). As previously suggested (Grillner 2006; Hebb 1949), a closedcycle in a network is truly reverberating and capable of self-sustainedactivity in the absence of external stimuli when the traveling activitybegins and ends at the same point (Hebbian closed cycle or phasesequence). Thus to analyze these cycles in the striatal microcircuitbetter, we constructed sentences that reflected network dynamics overtime, such as abcabec, which represents the state transitions fromstates a to b, b to c, c to a, a to b, and so on, where each letterrepresents a given network state (vertex of the digraph). Once a

sentence was identified for a given experiment, we searched for theexistence of closed cycles that form words. For example abca is aword representing a closed cycle within the cited sentence in thepreceding text. For each closed cycle found in the sentence, weobtained the adjacency matrix of the given digraph (Diestel 2005).The adjacency matrix of a Hamiltonian digraph has the followingproperty: Hn I, where n is the number of vertices of the digraph and

I is the identity matrix. Hamiltonian cycles also have the properties ofcyclic groups. The adjacency matrix of an Eulerian digraph E has the

following property: j1n aij j1

n aji that is, the sum of the elementsof the row i is equal to the sum of the elements of the column i.

We used graph-based hierarchical conceptual clustering (Jonyeret al. 2001) to search for smaller units or semantic atoms (Abeles2003; Bienenstock and Geman 1995; Hebb 1949) that could compose

the complete representation of the striatal cycles (Bienenstock andGeman 1995).

Finally, we looked for cycles that could be performing low level orparallel processing. That is, cycles appearing in the activity of indi-vidual cells before they emerge as peaks of statistically significantsynchronous activity (within a neural vector). The identification ofsuch matching sequences in some cells revealed the same cycles

found with the peaks of synchronous activity. Thus the same words ofthe sentences formed by the transitions between network states couldbe observed as running in low level (Lee and Wilson 2002), a propertyknown as priming (Abeles 2003). Priming refers to the activation ofindividual cells forming specific sequences before enough neurons ina pool get synchronized to generate a significant peak of synchrony(neural vector). To find priming sequences in individual cells, weexamined the onsets of their calcium transients. Thus priming se-quences are the same sequences found in the cell assembly amongneural vectors but running in low level.

R E S U L T S

Optical and electrophysiological recordings of

striatal neurons

Figure 1A illustrates loaded cells within an observationalfield. Automatic contour detection allowed us to reconstruct aspatial map of the neurons (Fig. 1B; 3, 1 cell targeted forelectrophysiological recordings). Most loaded cells were me-dium spiny projection neurons (see Carrillo-Reid et al. 2008)(Fig. 1C). We previously showed that the firing of burstscomprising two or more action potentials produces discernibleCa2 transients in projection striatal neurons, thus making itpossible to monitor electrical activity of many cells by dynamiccalcium imaging (Fig. 1D). Simultaneous electrophysiologicaland calcium-imaging recordings confirmed that the first deriv-

ative of the calcium signals can be used to reconstruct the timecourse of spontaneous bursts. Spontaneous Ca2 transients arethen transformed into raster plots (Carrillo-Reid et al. 2008)that can be used to follow simultaneously the activity of dozensof neurons with single-cell resolution. Under control condi-tions, medium spiny neurons are usually silent. Nevertheless,and as it has been exhaustively shown in several circuits of thenervous system (e.g., Grillner 2006; Guertin and Hounsgaard1998; Hsiao et al. 1998; Ibanez-Sandoval et al. 2007; Tsengand ODonnell 2005; Zhu et al. 2004), the tonic excitatorydrive conveyed by NMDA application induces recurrent pat-terned bursting in striatal neurons (Carrillo-Reid et al. 2008;Vergara et al. 2003) similar to that found in vivo (Herrling

et al. 1983) and correlated with movement execution (Grillner2006; Ossowska and Wolfarth 1995; Vautrelle et al. 2008),demonstrating that patterns of activity (motor behaviors) oc-curring in vivo could arise from the striatal processing of atonic excitatory drive such as NMDA.

Given that cholinergic modulation in neostriatal neuronsincreases postsynaptic excitability, while at the same timedecreases the strength of their GABAergic collateral synapticconnections (Figueroa et al. 2002; Galarraga et al. 1999;Perez-Rosello et al. 2005; Shen et al. 2005), we hypothesizedthat cholinergic modulation should increase the ability of cellassemblies to alternate their synchronized activity betweenneuronal pools within the striatum (Abeles 2003).

739COMPOSITIONALITY OF STRIATAL NETWORK DYNAMICS



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Cholinergic modulation of correlated firing in thestriatal microcircuit

NMDA induced network dynamics in the striatum, that is,neuron pools with synchronized firing alternate their activitywith other pools, to produce cycles of activity formed bynetwork states evolving over time (Carrillo-Reid et al. 2008).We investigated the effects of increasing the cholinergic toneon these dynamics. Accordingly, we first studied the cholin-ergic modulation of firing patterns at the single-cell level withvoltage recordings and, simultaneously, followed the activityof dozens of neurons with multicellular calcium imaging.Figure 2 shows that after bath addition of muscarine (5 M)there was a change in the firing pattern induced by NMDA(Fig. 2A). The frequency and level of membrane potentialdepolarization of spontaneous voltage transitions increased

(n 26 cells). The ionic basis that underlie this firing patterndeserves further analysis and perhaps includes the blockage ofKCNQ-channels (Shen et al. 2005), the activation of cationiccurrents (Figueroa et al. 2002), and the blockage of the spikeafterhyperpolarization (Perez-Burgos et al. 2008; Perez-Rosello et al. 2005). In this work, we show that the change infiring pattern is associated with an increase in the negativeslope conductance region (NSCR) and a reduction in inwardrectification (Figueroa et al. 2002; Galarraga et al. 1999) as isevident in the current-voltage relationship (Carrillo-Reid et al.2008; Vergara et al. 2003) (Fig. 2B). The NSCR endowsneurons with nonlinear capabilities (Izhikevich 2007). In-creased spontaneous voltage transitions and calcium transientscould be abolished by addition of the NMDA receptor antag-onist AP5 (50 M; data not shown), suggesting that the genera-

D

BA

100 pA

100 ms

25 mV

Current (pA)

Voltage(mV)

10 s

F/F10%

-80 mV

30 mV

d(F/F)/dt

10%/s

-100 0 100

-100

-80

-60

-40

2 54410

500 ms

2.5 SD

C

FIG. 1. Visualizing striatal network activity. A: neurons in a cortico-striatal slice loaded with Fluo-4 AM. In some experiments, simultaneous whole cellrecordings were made (notice patch pipette filled with Fluo-4 salt from bottom right corner to the middle). B: automatic contour detection from image in A. 3,the cell chosen for electrophysiological recordings. Scale bar: 100 m. C: voltage recordings (top) of a medium spiny neuron in response to current steps ( middle).Current-voltage relationships measured from these traces (bottom) identify electrophysiologically this cell as a medium spiny neuron. D: 2nd trace from top tobottom shows simultaneous recordings of membrane depolarizations (expanded on top) while their corresponding calcium transients are shown in the middletrace. Intracellular current steps (stimulus) are not shown. Bottom trace: the 1st time derivative of the calcium transient (dashed line indicates 2.5 times the SDof the noise). F, events where d(F/F)/dt 2.5 times SD. These F are used to build raster plots (see following text). Calcium transients evoked by 2 actionpotentials were considered for further analysis.




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tion of the NSCR in striatal neurons is determinant for thisactivity (Carrillo-Reid et al. 2008; Vergara et al. 2003). More-over, the muscarinic-dependent increase of the NSCR is onlyseen in the presence of NMDA (data not shown), the activationof muscarinic receptors in medium spiny neurons at rest

(without activity induced by NMDA) only reduces the amountof inward rectification (Figueroa et al. 2002; Galarraga et al.1999).

To study the actions of muscarine at the network level, weused multicellular calcium imaging to obtain brief movies (180

A

5 sec

80 mV

-75 mV

F/F20%

D

E

F

jaccardcor

relation

0

0.25

neur

on

i

neuron i

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%c

oactive

cells

Activecell#

1 min

5 10 15 20 25 30 35

5

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20

25

30

35

5 10 15 20 25 30

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30

jaccardcor

relation

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neur

on

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10

5

P


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s) under different conditions, and by means of raster plotanalysis followed the activity of multiple cells simultaneously(Fig. 2C, top: each row in the raster plot represents an activeneuron). The activation of muscarinic receptors increased con-siderably both the correlated firing among neurons (Fig. 2E)and the number of peaks of spontaneous synchronous activity(Carrillo-Reid et al. 2008; Cossart et al. 2003) (Fig. 2C bottom;

n 20 slices; 2.8 peak/epoch in NMDA vs. 12.2 peak/epochin muscarine; P 0.001). The timing of peaks of synchronousactivity correlated with the timing of voltage-state transitionsas measured electrophysiologically (Carrillo-Reid et al. 2008).

Active neurons are marked with filled circles in the maps ofFig. 2D. Blue labels indicate cells that were active before andduring muscarine exposure. Red-filled circles indicate cellsthat were active during the peaks of synchrony. Lines connectpairs of neurons that had nonrandom correlated activity (P 0.01: the thickness of these lines is proportional to the strengthof the correlation). A cross-correlation matrix of all activeneurons was constructed using the Jaccard correlation coeffi-cient (Fig. 2F) calculated before and during muscarine exposure.

Remarkably, cholinergic modulation enhanced correlatedfiring (Fig. 2, CF; 35 lines in NMDA vs. 64 lines in musca-rine) without significantly increasing the number of active cells(34 cells in NMDA vs. 39 cells in muscarine). However, not allneurons with correlated firing participated in the peaks ofsynchronous activity (black vs. red circles), suggesting thatcorrelated firing is not a sufficient condition to affirm that aneuron belongs to a cell assembly (Carrillo-Reid et al. 2008).To compare the actions of the exogenous agonist (muscarine)with that of endogenously released acetylcholine, the acetyl-cholinesterase inhibitor eserine (10 M) was used to enhancethe basal tone of striatal acetylcholine (see Supplementary Fig. S11).Eserine increased the occurrence of peaks of synchrony andcorrelated firing in all experiments (n 12 slices). The

increase in neuronal synchronization was mediated by musca-rinic receptors because bath addition of atropine (5 M)decreased the synchronization and returned network dynamicsto the control level (NMDA, preeserine; NMDA: 2.3 peak/epoch, 37 cells, 36 correlation lines; eserine: 12.5 peak/epoch,39 cells, 58 correlation lines; atropine: 2.8 peak/epoch, 36cells, 33 correlation lines; Supplementary Fig. S1). In sum-mary, the experiments demonstrate that striatal activation of thecholinergic system induces the recruitment of more neurons intocorrelated firing without increasing the number of active neurons.

A cardinal signature of cell assembly dynamics is that anexcitatory tonic drive can be transformed into patterned activ-ity. Patterned activity is characterized by the emergence ofvarious neuronal pools with synchronous, alternating and re-

current activity (Carrillo-Reid et al. 2008; Grillner 2006; Hammer

2003; Harris 2005; Hebb 1949). Based on the results describedin the preceding text, we hypothesized that the cholinergicsystem may endow network dynamics with more complexproperties than those conferred by NMDA alone (Carrillo-Reidet al. 2008).

Cholinergic modulation of striatal network dynamics

To study the actions of the cholinergic system on networkbehavior, movies were obtained at different time intervalsduring long time periods (1 h; n 15 slices), and neuronalactivity was vectorized (see METHODS) by binning raster plotsfrom all recorded neurons (Carrillo-Reid et al. 2008). The sameresults were obtained with different bin widths (see METHODS).The vectorization method allowed a rigorous comparison ofsynchronous peaks before and during muscarine application(Brown et al. 2005; Sasaki et al. 2007). The inner product of allvector pairs, plotted as a matrix, reflected the similarity be-tween all network states appearing in one experiment as afunction of time and pharmacological manipulations (Fig. 3A).The appearance of cluster-like structures distributed along time in

the similarity index matrix (Fig. 3A) readily suggested radicalchanges in network dynamics after muscarine addition (Carrillo-Reid et al. 2008; Sasaki et al. 2007; Stopfer et al. 2003).

In confirmation of that assertion, LLE disclosed the presenceof various neural vectors representing the different networkstates (Brown et al. 2005; Carrillo-Reid et al. 2008; Roweis andSaul 2000; Stopfer et al. 2003) (Fig. 3B, see METHODS). Hardand fuzzy clustering algorithms taking the Dunns index as avalidity function made it possible to depict the different statesof the network and the alternation of activity among them overtime (Bezdek et al. 1997; Carrillo-Reid et al. 2008; Sasaki et al.2007). Here data points represent vectorized synchronouspeaks and clusters of data points represent the vectors withsimilar dynamics appearing during recording time. Arrowsconnecting clusters of data points indicate sequences of acti-vation among pools with synchronously active neurons. It isseen that activity travels among the different network statespresent within the field of observation. Neuron pools could beseen alternating their activity following recurrent closed path-ways and subpathways (Hammer 2003). These dynamics differgreatly from that induced by NMDA alone, both in the numberof synchrony peaks and the predisposition of the network toleave one state and readily switch from one to the other (cf.,Carrillo-Reid et al. 2008). Figure 3C shows the time course ofsynchronous peaks occurrences (columns with dots of the samecolor, each number on the top represents a different 3-minsample movie separated by vertical lines; see METHODS). A

representative histogram of overall activity (Fig. 3C, bottom)shows the percentage of coactive cells as a function of time andexperimental condition.1 The online version of this article contains supplemental data.

FIG. 3. Generation of alternate pathways between network states. A: similarity indices of all vectors representing network states as a function of time.B: multidimensional reduction of vectors representing network states using locally linear embedding (LLE). Each data point represents one vector at a given timeprojected in the vectorial space delimited by the 1st 2 dimensions. Clusters of vectors represent a given network state. Arrows represent transitions of activityfrom 1 state to another. Percentages indicate the probability of leaving a given network state. C: raster plots and time histograms of network dynamics. Rowsrepresent the activity of individual cells. Recurring peaks of synchronous activity are marked with asterisks. Vertical lines separate different series of images.First series corresponds to network activity induced by NMDA alone, while the next 2 series correspond to the NMDA-induced activity in the presence ofmuscarine in the bath. Colors denote different peaks of synchrony corresponding to different network states. Sequential, alternating and recurrent activation ofnetwork states can also be noticed in the raster plot. D: spatial maps of the neuron pools from which network states arise (filled circles). Empty contours representthe neurons without activity in the epoch. E: plot of the percentage of coactive cells during different network states. F: hierarchical cluster analysis of cellsbelonging to the neuron pools underlying the network states ( n 71 cells). Colored boxes indicate cells participating in the different states. A core of neuronsshared by all network states is indicated (n 11 cells).




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Figure 3D shows the spatial distribution of each neuron poolrepresenting the different network states. The percentage ofcoactive cells during the different states is shown in Fig. 3E.

Hierarchical cluster analysis of these data revealed the exis-tence of identifiable cells belonging to different and intermin-gled neuronal pools participating once and again in the network

A B

C

D E

F

Activecell#

*

P


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activity (Fig. 3F). Interestingly, following the activation ofmuscarinic receptors, neuron pools were divided into sub-groups generating more pathways for the traveling activity,suggesting the property of distributed synchrony (Levy et al.2001). Enhancement of the cholinergic tone also producedmore elements that were shared by the different network states(Fig. 3E; 22 2% of shared neurons), as compared with

NMDA alone (12 2% of shared neurons; see Fig. 7E inCarrillo-Reid et al. 2008).

These experiments demonstrate that under the present con-ditions, muscarinic receptor activation led to a fundamentalchange in the network dynamics of the striatal circuitry: boththe level of synchrony and the traveling of the synchronousactivity between neuron pools were greatly enhanced. Thesechanges in network dynamics could be theoretically interpretedon a simple basis: they could represent a combinatorial in-crease in storage capacity during memory processes (Hammer2003; Luczak et al. 2007; Roudi and Latham 2007), or theincreased capability to recruit neurons and to switch betweennetwork states during possible related tasks. We next asked ifthe traveling of synchronous activity from one state to anotherfollowed fixed trajectories, that is, if cell assemblies could fire,as expected for Hebbian-like cell assemblies, in a cyclicalmanner, as do some recurrent neural networks (Abeles 2003;Hammer 2003; Harris 2005; Hebb 1949; Levy et al. 2001).

Enhancement of cholinergic activity promotes theorganization of striatal assemblies into closed cycles

Figures 4A, 13, shows the activity patterns during differentpharmacological conditions. Eserine and muscarine treatmentproduced alternating activation of vectors of network dynamics(n 11/15 slices; see also Supplementary Fig. S2). Also LLEshowed an increase in the generation of alternate pathways orclosed cycles during cholinergic system activity as compared

with the NMDA-alone condition (cf., Fig. 4B, 13). Becausethe time course of activity trajectories during network statesappeared to exhibit patterns repeated in time, we identifiedsentences to follow the changes of the network states over timebetter (Lee and Wilson 2002) (see black numbers, Fig. 4C,13). Each sentence was constructed as a set of the changes ofthe network states over time. Sentences were analyzed lookingfor fixed trajectories or closed cycles. Each closed cycle, whichbegan and ended in the same state, denotes a word (sequenceof numbers, e.g., 14321), belonging to a given sentence.Different sequences could be associated with memory traces(Lee and Wilson 2002), but the same sequences may occur ondifferent time scales (Ikegaya et al. 2004; Lee and Wilson

2002). Different words are represented as colored numbersequences. The same color denotes the same sequence butbeginning at a different point. Fixed sequences were identifiedwith some of them returning several minutes after a previous

activation (e.g., Fig. 4C2: 13241 and 24132; C3: 2412and 1241).

We used graph theory to analyze the pathways formed by thenetwork transitions. Each state could be represented as a vertexof a digraph (isomorphic directed graph; see METHODS), andeach trajectory could be represented as a directed edge of thedigraph (Fig. 4D) (Gerber 1990). Accordingly, two digraphs, G

and H, are isomorphic if a function : VG3VH exists such thatall vertices of digraph G have a unique correspondence with allvertices of digraph H(Fig. 4D). Isomorphic graphs possess thesame theoretical properties and generate the same plane figures(Diestel 2005). We represented segments of state transitions asdigraphs labeled with their corresponding words. Digraphshave adjacency matrices (see METHODS, and Supplementary Fig.S3). The representation of digraphs as matrices allows theirstudy under the linear algebra workspace (Gerber 1990).Closed cycles have been proposed as a network mechanism tostore memory traces, and these sequences should represent themost efficient memory pathways (Hebb 1949). Thus we chosedigraphs that satisfied the conditions of Hamilton or Eulercycles, that is, the best pathways among a fixed set of points(see METHODS, and Supplementary Fig. S3). Figure 4D, 13,shows digraphs (directed graphs; see METHODS) composed afterthe enhancement of cholinergic activation that met this crite-rion (either eserine or muscarine treatment). Notice that duringeserine or muscarine treatment, there is an overlapping ofdifferent cycles. It was observed that the conformation ofcomplex cycles from basic ones followed specific rules (n 35/50 videos; Fig. 4C, 2 and 3; see also Supplementary Fig.S2), demonstrating compositional properties (Bienenstock andGeman 1995; Hammer 2003) of striatal cell assemblies (seefollowing text).

Enhancement of cholinergic tone induces compositionality of

striatal cell assembliesCompositionality is a central issue to cognition; it refers to

the ability of a given system to build complex hierarchicalrepresentations in terms of simpler parts and their relations(Bienenstock and Geman 1995). There are several systems thathave compositional properties, such as language or specificmotor actions (Bienenstock and Geman 1995; Grillner 2006;Hammer 2003). Organization of cell assemblies into hierarchi-cal lattices may be an important feature in storing and retriev-ing any complex knowledge (Hammer 2003), allowing therepresentation of composite objects by binding simpler com-ponents (Abeles 2003; Bienenstock and Geman 1995).

Using graph-based hierarchical conceptual clustering

(Jonyer et al. 2001), we showed that striatal cycles can bedecomposed into smaller units that can be taken as semanticatoms (Abeles 2003; Hebb 1949). Figure 5 illustrates a latticeof different cycles and their composition rules (colored graphs

FIG. 4. Organization of striatal closed cycles of activity. A: similarity indices of all vectors representing network dynamics as a time function under differentpharmacological conditions (A1: NMDA; A2: NMDA eserine; A3: NMDA muscarine). Note the appearance of cluster-like structures in all the cases.

B: vectors representing network states under the same conditions as in A. Note that eserine and muscarine generate alternate pathways within the closed cycles.Percentages over trajectories signal probability of leaving a given state. C: state transitions over time under the same conditions as in the preceding text. Blacknumbers indicate sequences of vectors activation at each epoch where each number corresponds to a different state. Vertical lines separate different epochs.Closed cycles (indicated with colors) beginning and ending at the same point can be observed. The same colors represent the same cycles. Note an increase inthe number of cycles and compositionality of cycles within larger ones during eserine and muscarine application. D: isomorphic digraphs (directed graphs) ofactivity cycles for the same experimental conditions as above (D, 13). Some cycles may initiate their activity starting at different points but then the trajectoryfollows the same steps. Asterisks signal points of initiation. The conditional probability of a given cycle being active is in black. Words represent closedtrajectories.




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from Fig. 4D2), that we termed cyclic folds. Cyclic foldsdenote a coordinated neuronal activity that is organized hier-archically. It is formed by activity cycles that fold and unfold(transient events) to compose more complex cycles that may be

slightly different each time. Therefore co-activation of cyclicfolds represents a complex semantic entity. The existence ofsmaller units allows the composition of various complex cyclesthat could be composed following different pathways each time

10 20 30 40

1

2

3

4

124131324143

13241 24132

31241323

state#

peak i(t)

20 40 60 80 100

20

40

60

80

100

similarityindex

0

1

vector

i

(t)

vectori(t)

NMDA + eserine

vector

i

(t)

vectori(t)

similarityindex

0

1

20 40 60 80 100

20

40

60

80

100

NMDA + muscarine

-2 -1 0 1 2

state 4 state 3

state 2

state 1

44% 28%

22%6%

38%

12%

25%

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33%

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-1

0

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LLE 1

LLE2

13241 24132

1324124132

*

*

0.33%

1241

*

2.31%

=~:VG VH

=~

31324143 31241323*

0.004% 0.008%

state#

peak i(t)10 20 30 40 50

1

2

3

4232412

241212412142

14211421241

LLE 1

LLE2

-4 -3 -2 -1 0 1 2 3

-2

-1

0

1

2

3

state 4state 3

state 2

state 1

66%

24%

5%5%

20%

20%

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44%

25%

33%

20%

40%

50%17%

232412

*

*

1241

*

*

14212412

*

2142

*

1421241

0.02%

1.98% 0.63% 0.01%

NMDAA1

B1

C1

D1

A2

B2

C2

D2

A3

B3

C3

D3

similarityindex

0

1

vector

i

(t)

vectori(t)10 20 30 40 50

10

20

30

40

50

-2 -1 0 1 2

-2

-1

0

1

2

state 3

state 2

state 1

25%

25%

50%

50%33%

33%

17%

67%

LLE 1

LLE2

2 4 6 8 10 12 14

1

2

3

1231

state#

peak i(t)

:VG VH

=~

r r

b

g

12315.7%

1

2

3*

V1V2V3

VrVbVg

1231_31_323 121_34343131324143_3121212_31241323 1_121343_12424232412_121421241

:VG VH

=~

34313

34313*

0.71%

*

4232424232

*

*

4232424232

0.15%




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(dotted lines). Coexistence of several cyclic folds suggests thatstriatal cell assemblies can support parallel processing or back-ground activation of activity patterns. Activity in the lowerlevels of the system could initiate activity at higher levels(Abeles 2003; Hebb 1949) endowing striatal cell assemblieswith the property of priming.

Priming of striatal closed cycles

Because sequential transitions between states could fol-low closed cycles (such as 14231), the present activitystrongly suggests the existence of mechanisms that deter-mine specific time relations among the firing of cells be-longing to different states (Dragoi and Buzsaki 2005; Harris2005). This form of organization would tend to activate thesame combination of cells selectively over time (Hebb1949), a property called priming in the psychological liter-ature (Abeles 2003). To test if striatal cell assemblies can beprimed, we asked whether background activity arising fromindividual cells exhibits closed cycles outside the peaks ofsynchrony but similar to those being exhibited among neural

vectors. If these background closed cycles were encoun-tered, in much smaller segments of the circuit (before theyappear within the synchrony peaks; Fig. 6), then, priming ofcell assemblies by parallel low-level processing should bepossible. In fact, low-level cycles of activity were easilyidentified as nested inside segments of different epochs (Fig.6, A and B) and at different time scales (Lee and Wilson2002). These specific patterns could be found to run simul-

taneously in small sets of individual neurons before theyappeared within a cycle among synchrony peaks, demon-strating the possibility of low-level parallel processing.Indeed cycles shown in Fig. 6B did not participate in thepeaks of synchronous activity (dashed box from Fig. 6A)until 10 min later. This implies that reverberating activitymay synchronize and desynchronize different cell assem-blies over time, allowing the appearance of the same wordsduring state transitions depending on the past states (internalstates) of the network. It has been proposed that multipleparallel or alternate processing of information could be auseful mechanism to provide resilience in the face of braindamage (Hebb 1949).

Cyclic folds

FIG. 5. Hierarchical conceptual clustering of striatal cycles. Figure shows the classification lattice generated by graph-based hierarchical conceptualclustering. Cyclic folds refer to coordinated neuronal activity that is organized hierarchically and is formed by cycles that fold and unfold to compose complexones. Smaller units compose the first level of the lattice. These are the most general clusters which can be classified as Hamiltonian cycles. Subsequently cyclicfolds are composed by these semantic atoms that are either combined with elements of the same level or with other levels to form new cycles (black dotted circle).Note that some cyclic folds could be composed by different pathways (dotted lines), and that there are specific rules of composition to form a closed cycle. Thismeans that not all combinations of the elements compose a valid cycle.




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D I S C U S S I O N

We used dynamic multi-cell calcium imaging of neuronalpopulations and whole cell voltage recordings from neostriatalspiny neurons, in combination with analytical approaches, todemonstrate that: 1) muscarinic receptor activation facilitatesboth the negative slope conductance region of the current-voltage relationship induced by NMDA and the generation ofvoltage transitions. These effects are translated into a signifi-cant increase in the correlated activity of the network. 2) In-creased correlated firing was accompanied by a more efficienttraveling of synchronized activity among the neuron pools

giving rise to network states. In fact, an increased number ofboth spontaneous peaks of synchrony and trajectories in theflow of activity were evident. These results demonstrate thatneurons can enter and depart from the synchronized statesmodulated by cholinergic tone. 3) A closer analysis of activitytrajectories revealed recurrent sequences of activation of neu-ron pools. Alternative trajectories for the activity following aset of pathways and subpathways revealed that the cholin-ergic system induces cell assemblies with compositionalcapabilities (Bienenstock and Geman 1995; Hammer 2003).These findings suggest that the interface between neuronalactivity and circuitry function can be approached (Graybiel1995).

From cell assemblies to memory processes

A central issue in neuroscience is how neural networks storememories. Hebb (1949) proposed reverberant cell assembliesas the substrate for memory storage. However, to store anykind of composed knowledge, cell assemblies must have theability to form hierarchies: subcircuits composing larger cir-

cuits (Hammer 2003). Composition refers to hierarchical con-struction of representations made up of simpler parts and theirrelationships (Bienenstock and Geman 1995). A realistic mem-ory system must be composed of interconnected modules; sucha modular structure can be the basis for a combinatorialincrease in storage capacity (Huyck 2001).

Among the two Hebbian hypotheses concerning memoryprocesses, the one concerning long-term synaptic plasticity hasreceived exhaustive experimental support following the workof Bliss and Lomo (1973), while the one concerning theexistence of cyclic reverberant cell assemblies or recurrentneuronal networks (Hammer 2003) has proved more elusive.Here we recorded neuronal microcircuits that clearly exhibitrecursive cell assembly properties in the rat neostriatum in

vitro. The methods used allowed us to follow multiple cellssimultaneously in a small segment of the network. The activityof similar neuron pools has been recorded in the cortex andother brain structures (Cossart et al. 2003; Ikegaya et al. 2004),but previous analysis stopped short of demonstrating the re-current and reverberating nature of neural activity.

Building composed microcircuits

It is thought that complex motor actions can be recursivelydecomposed into simpler motor actions (Bienenstock andGeman 1995; Grillner 2006). These basic units can be recom-bined to generate a diverse and flexible output (Grillner 2006).

Basic modules or semantic atoms (Abeles 2003; Hebb 1949)could be thought as unit central pattern generators (CPGs),that is, a group of neurons generating recurrent activity (Grillner2006). The nervous system is endowed with a number ofinborn or preassembled unit CPGs that can be recruited togenerate a rich variety of behaviors (Carrillo-Reid et al. 2008;Grillner 2006). However, while NMDA only induces a feed-back activity more similar to that encountered in unit CPGs(Carrillo-Reid et al. 2008; Grillner 2006), cholinergic modula-tion endows striatal cell assemblies with a more interestingproperty: compositionality (Bienenstock and Geman 1995;Hammer 2003).

Actually, the ability of neuronal pools to compose complexand synchronized sequences of activity is the main difference

between our findings and those from other groups working inthe cortex. Thus synfire chains are based on the sequentialactivation of individual cells (Ikegaya et al. 2004), while ourresults are based on the synchronous and sequential firing ofneural vectors over time. These neural vectors form cycles ofactivity that are organized hierarchically with a modular archi-tecture: larger cycles are composed from simpler ones. More-over, activity of closed cycles can be repeated exactly in thesame way at different times or repeated with some variationusing smaller subpathways at another time. This behaviorstrongly suggests that the activation of the cholinergic systemendows the microcircuit with multiple semistable states, the setof which could represent memory traces. This also differs from

A

B

state

#

peak i(t)

25 35 45

1

2

3

4

2 3 2 4 1 22 4 1 2 1 2 4 1

2 1 4 21 4 2 11 4 2 1 2 4 1

1 2 4 2 4 2 3 2 4 1 2_1 2 1 4 2 1 2 4 1

4 2 3 2 42 4 2 3 2

Cell 6

Cell 12

Cell 14

Cell 70

Cell 82

4 44442 2 2 21 1 1 1 1

1 4 2 1 1 2 4 121 4 2

30 sec

F/F15%4

4

4

4

4

2

2 2

2

2

2

2

2

2

1 1 1 1 1

2

FIG. 6. Low-level processing by striatal cell assemblies. A: state transitionsillustrated in Fig. 4C3 (2 epochs). The dashed box represents the segment usedto look for the priming of closed cycles. B: activity of individual cellsbelonging to cycles found in the dashed box from A. Colors denote cellsbelonging to different network states: red: state 1; blue: state 2; orange state 4.Note the anticipated presence of sequences that will appear in the next videoexactly in the same order. To look for priming sequences in individual cells,we considered the onset of the calcium signals (arrows); this indicates the stateof a given cell to be considered. The sequences 2142, 1421, and 1241appeared as in state transitions from peaks 37 to 49. Those words were foundbefore they appeared in the peaks of synchronous activity. Note synchronousactivity of cells 12 and 14.




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metastability found in hippocampal CA3 networks (Sasakiet al. 2007).

Organization of recurrent closed cycles of activity

The demonstration of the sequential and recurrent travelingof synchronous activity among network states reveals a robust

and coordinated activity of specific, identified neuron pools.These pools are intermingled within (Cossart et al. 2003; Hebb1949; Ikegaya et al. 2004) a small area of an in vitro slicepreparation. The recursive activation of spatiotemporal pat-terns following closed cycles of recurrent activity was hypoth-esized when looking at histological preparations of the samesize scale (Hebb 1949; Lorente de No 1938). It has also beeninferred from electrophysiological recordings of patterned ac-tivity of small numbers of neurons during simultaneous fieldrecordings (Abeles 2003; Beiser et al. 1997; Berke et al. 2004;Costa et al. 2006; Dragoi and Buzsaki 2005; Luczak et al.2007). This activity has been posited as a plausible mechanismto store memories, including procedural memories (Abeles2003; Beiser et al. 1997; Hammer 2003). Because the ability torecord from dozens of cells simultaneously has only recentlybeen available (Cossart et al. 2003; Ikegaya et al. 2004; Maoet al. 2001), it perhaps should not come as a surprise that thefinding of these capabilities in small segments of tissue in vitrohas appeared only after a relatively long time since the adventof the brain slice preparation.

Mechanisms of striatal activity cycles

We demonstrated previously that the NMDA-induced net-work dynamics reside in both the synaptic and intrinsic prop-erties of striatal neurons. Blockade of GABAergic transmissiondisrupts alternation and selection among different neuron pools

demonstrating that inhibitory connections in the striatum areessential for the recursive activity of striatal cell assemblies(Carrillo-Reid et al. 2008; Ossowska and Wolfarth 1995).Furthermore, a tonic excitatory drive elicited by NMDA ap-plication to neoestriatial microcircuits generates both burstingactivity (Carrillo-Reid et al. 2008; Vergara et al. 2003) andmotor behavior (Ossowska and Wolfarth 1995), demonstratingthat a physiological significant mechanism is preserved invitro. The striatum has been posited as the store of some inbornmotor circuits (CPGs) (Grillner 2006) and also of learned oracquired ones (Aosaki et al. 1994; Diaz del Guante et al. 1991;DeLong et al. 1986; Graybiel 1995). In fact, network dynamicshas been shown in both young (PD13-22) and adult (PD 40)animals (Carrillo-Reid et al. 2008; Ikegaya et al. 2004; Lee and

Wilson 2002) demonstrating that network activity can beobserved over a wide range of ages.

However, not every increase in the excitability of the net-work results in the same behavior. Thus blockade of GABAergictransmission increases the number of synchrony peaks butabolishes network dynamics. On the other hand, increases inNMDA concentration do not change the dynamics produced bythis transmitter alone (Carrillo-Reid et al. 2008). And finally,selective cholinergic antagonists reverse the dynamics inducedby muscarine to that found just with NMDA.

In summary, activation of the cholinergic system engen-dered synchronized activity that traveled in closed cycles witha modular structure and a hierarchical organization (Abeles

2003; Beiser et al. 1997; Grillner 2006; Harris 2005; Hebb1949; Lewis et al. 2005; Roudi and Latham 2007). It isintriguing, that this activity involves circuitry composedmainly of inhibitory neurons.

Functional implications

While the present work aimed to extend our understandingof normal striatal physiology, it may also have significantimplications for the elucidation of pathological conditions. It isknown that acetylcholine levels are increased in Parkinsondisease and that anticholinergic drugs are frequently adminis-tered as adjuvant therapy with inconsistent results (Pisani et al.2007). Some theories about Parkinsons disease suggest theappearance of excessive synchrony in the basal ganglia (Beiseret al. 1997) brought about in part by a chronic elevation ofcholinergic tone due to dopamine loss (Pisani et al. 2007). Thechronic elevation of synchronous activity observed could en-gage fixed neuronal assemblies, thus discontinuing the associ-ation of different modules and restricting compositional capa-bilities (Levy et al. 2001). Thus network dynamics and the role

of intrinsic and synaptic mechanisms involved in the abnormalsynchronization of firing patterns in pathological states deservefurther study with the approaches we have used.

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

We thank J. Perez-Ortega, for programming the code used to find sequences.We also thank A. Laville and D. Tapia for technical support.

G R A N T S

This work was supported by Investigacion Multidisciplinaria De ProyectosUniversitarious De Lide Razgo y Superacion Academica-Universidad Nacio-nal Autonoma de Mexico (UNAM) 03 and Direccion General de Asuntos delPersonal Academico-UNAM Grants IN-226403 to A. Hernandez-Cruz, IN-201603 to J. Bargas, and IN-201507 to E. Galarraga and Consejo Nacional de

Cienca Y Tecnologia-Mexico Grants 49484 to J. Bargas and 42662 to A.Hernandez-Cruz. L. Carrillo-Reid, F. Tecuapetla, and O. Ibanez-Sandoval hadCONACyT-Mexico doctoral fellowships.

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