V. Abbott and C. Vreeswijk, Asynchronous states in networks of pulsecoupled oscillators, Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, vol.48, issue.2, p.14831490, 1993.

A. K. Alijani and M. J. Richardson, Rate response of neurons subject to fast or frozen noise: From stochastic and homogeneous to deterministic and heterogeneous populations, Physical Review E, vol.84, issue.1, p.11919, 2011.
DOI : 10.1103/PhysRevE.84.011919

D. J. Amit and N. Brunel, Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex, Cerebral Cortex, vol.7, issue.3, p.237252, 1997.
DOI : 10.1093/cercor/7.3.237

D. Aronov, D. S. Reich, F. Mechler, and J. D. Victor, Neural Coding of Spatial Phase in V1 of the Macaque Monkey, Journal of Neurophysiology, vol.89, issue.6, p.330427, 2003.
DOI : 10.1152/jn.00826.2002

M. Arsiero, H. Lüscher, B. N. Lundstrom, and M. Giugliano, The impact of input uctuations on the frequency-current relationships of layer 5, 2007.

L. Badel, S. Lefort, T. K. Berger, C. C. Petersen, W. Gerstner et al., Extracting non-linear integrate-and-re models from experimental data using dynamic i-v curves, Biol Cybern, vol.99, pp.4-5361, 2008.

E. Balaguer-ballester, C. C. Lapish, J. K. Seamans, and D. Durstewitz, Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making, PLoS Computational Biology, vol.976, issue.5, p.1002057, 2011.
DOI : 10.1371/journal.pcbi.1002057.s008

T. Bekolay, M. Laubach, and C. Eliasmith, A Spiking Neural Integrator Model of the Adaptive Control of Action by the Medial Prefrontal Cortex, Journal of Neuroscience, vol.34, issue.5, p.18921902, 2014.
DOI : 10.1523/JNEUROSCI.2421-13.2014

K. Benchenane, A. Peyrache, M. Khamassi, P. L. Tierney, Y. Gioanni et al., Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal- Prefrontal Network upon Learning, Neuron, vol.66, issue.6, p.66921936, 2010.
DOI : 10.1016/j.neuron.2010.05.013
URL : https://hal.archives-ouvertes.fr/hal-00554482

J. Benda and A. V. Herz, A Universal Model for Spike-Frequency Adaptation, Neural Computation, vol.79, issue.11, p.1525232564, 2003.
DOI : 10.1038/26758

T. C. Blanchard and B. Y. Hayden, Neurons in Dorsal Anterior Cingulate Cortex Signal Postdecisional Variables in a Foraging Task, Journal of Neuroscience, vol.34, issue.2, pp.646-655, 2014.
DOI : 10.1523/JNEUROSCI.3151-13.2014

I. Bodis-wollner, S. F. Bucher, and K. C. Seelos, Cortical activation patterns during voluntary blinks and voluntary saccades, Neurology, vol.53, issue.8, p.5318001805, 1999.
DOI : 10.1212/WNL.53.8.1800

C. Boucsein, M. P. Nawrot, P. Schnepel, and A. Aertsen, Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround, Frontiers in Neuroscience, vol.5, p.32, 2011.
DOI : 10.3389/fnins.2011.00032

K. H. Britten, W. T. Newsome, M. N. Shadlen, S. Celebrini, and J. A. And-movshon, Abstract, Visual Neuroscience, vol.60, issue.01, p.87100, 1996.
DOI : 10.1017/S0952523800005423

N. Brunel, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J Comput Neurosci, vol.8, issue.3, p.183208, 2000.

N. Brunel, F. S. Chance, N. Fourcaud, A. , and L. F. , Eects of synaptic noise and ltering on the frequency response of spiking neurons, Phys Rev Lett, issue.10, p.8621862189, 2001.

N. Brunel and V. Hakim, Fast global oscillations in networks of integrateand-re neurons with low ring rates, Neural Comput, issue.7, p.1116211671, 1999.

N. Brunel and P. E. Latham, Firing rate of the noisy quadratic integrateand-re neuron, Neural Comput, issue.10, p.1522812306, 2003.

N. Brunel and X. J. Wang, Eects of neuromodulation in a cortical BIBLIOGRAPHY 231, 2001.

N. Brunel and X. Wang, What Determines the Frequency of Fast Network Oscillations With Irregular Neural Discharges? I. Synaptic Dynamics and Excitation-Inhibition Balance, Journal of Neurophysiology, vol.90, issue.1, p.415430, 2003.
DOI : 10.1152/jn.01095.2002

D. V. Buonomano and M. M. Merzenich, Temporal information transformed into a spatial code by a neural network with realistic properties, Science, vol.267, issue.5200, p.26710281030, 1995.
DOI : 10.1126/science.7863330

T. J. Buschman, E. L. Denovellis, C. Diogo, D. Bullock, and E. K. Miller, Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex, Neuron, vol.76, issue.4, p.838846, 2012.
DOI : 10.1016/j.neuron.2012.09.029

N. Cain and E. Shea-brown, Computational models of decision making: integration, stability, and noise, Current Opinion in Neurobiology, vol.22, issue.6, p.10471053, 2012.
DOI : 10.1016/j.conb.2012.04.013

M. Carandini, Area V1, Scholarpedia, vol.7, issue.7, p.12105, 2012.
DOI : 10.4249/scholarpedia.12105

S. M. Chase and E. D. Young, First-spike latency information in single neurons increases when referenced to population onset, Proceedings of the National Academy of Sciences, vol.104, issue.12, p.51755180, 2007.
DOI : 10.1073/pnas.0610368104

D. Chicharro, T. Kreuz, and R. G. Andrzejak, What can spike train distances tell us about the neural code?, Journal of Neuroscience Methods, vol.199, issue.1, p.146165, 2011.
DOI : 10.1016/j.jneumeth.2011.05.002

A. K. Churchland, R. Kiani, R. Chaudhuri, X. Wang, A. Pouget et al., Variance as a Signature of Neural Computations during Decision Making, Neuron, vol.69, issue.4, p.818831, 2011.
DOI : 10.1016/j.neuron.2010.12.037

A. Compte, Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model, Cerebral Cortex, vol.10, issue.9, p.910923, 2000.
DOI : 10.1093/cercor/10.9.910

A. Compte, N. Brunel, P. S. Goldman-rakic, W. , and X. J. , Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model, Cerebral Cortex, vol.10, issue.9, p.910923, 2000.
DOI : 10.1093/cercor/10.9.910

P. S. Goldman-rakic, W. , and X. , Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task, J Neurophysiol, issue.5, p.9034413454, 2003.

G. Deco, E. T. Rolls, L. Albantakis, and R. Romo, Brain mechanisms for perceptual and reward-related decision-making, Progress in Neurobiology, vol.103, 2013.
DOI : 10.1016/j.pneurobio.2012.01.010

E. Degenetais, Electrophysiological Properties of Pyramidal Neurons in the Rat Prefrontal Cortex: An In Vivo Intracellular Recording Study, Cerebral Cortex, vol.12, issue.1, p.116, 2002.
DOI : 10.1093/cercor/12.1.1

M. Deger, T. Schwalger, R. Naud, and W. Gerstner, Fluctuations and information ltering in coupled populations of spiking neurons with adaptation, 2014.

A. Destexhe, M. Rudolph, and D. Paré, The high-conductance state of neocortical neurons in vivo, Nat Rev Neurosci, vol.4, issue.9, p.739751, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00299172

K. Diba, H. A. Lester, and C. Koch, Intrinsic Noise in Cultured Hippocampal Neurons: Experiment and Modeling, Journal of Neuroscience, vol.24, issue.43, p.2497239733, 2004.
DOI : 10.1523/JNEUROSCI.1721-04.2004

M. Dipoppa and B. S. Gutkin, Correlations in background activity control persistent state stability and allow execution of working memory tasks, Frontiers in Computational Neuroscience, vol.7, p.139, 2013.
DOI : 10.3389/fncom.2013.00139

M. Dipoppa and B. S. Gutkin, Flexible frequency control of cortical oscillations enables computations required for working memory, Proceedings of the National Academy of Sciences, vol.110, issue.31, p.1101282812833, 2013.
DOI : 10.1073/pnas.1303270110

D. Durstewitz, N. M. Vittoz, S. B. Floresco, and J. K. Seamans, Abrupt Transitions between Prefrontal Neural Ensemble States Accompany Behavioral Transitions during Rule Learning, Neuron, vol.66, issue.3, p.66438448, 2010.
DOI : 10.1016/j.neuron.2010.03.029
URL : http://doi.org/10.1016/j.neuron.2010.03.029

F. Farkhooi, E. Muller, and M. P. Nawrot, Adaptation reduces variability BIBLIOGRAPHY 233, 2011.

L. Fenno, O. Yizhar, and K. Deisseroth, The Development and Application of Optogenetics, Annual Review of Neuroscience, vol.34, issue.1, p.389412, 2011.
DOI : 10.1146/annurev-neuro-061010-113817

N. Fourcaud-trocmé and N. Brunel, Dynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics, Journal of Computational Neuroscience, vol.16, issue.3, p.311, 2005.
DOI : 10.1007/s10827-005-0337-8

N. Fourcaud-trocmé, D. Hansel, C. Van-vreeswijk, and N. Brunel, How spike generation mechanisms determine the neuronal response to uctuating inputs, J Neurosci, issue.37, p.231162811640, 2003.

T. Freund and S. Kali, Interneurons, Scholarpedia, vol.3, issue.9, p.4720, 2008.
DOI : 10.4249/scholarpedia.4720
URL : https://hal.archives-ouvertes.fr/hal-00292588

S. Funahashi, C. J. Bruce, and P. S. Goldman-rakic, Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex, J Neurophysiol, issue.2, p.61331349, 1989.

S. Funahashi, C. J. Bruce, and P. S. Goldman-rakic, Dorsolateral prefrontal lesions and oculomotor delayed-response performance: evidence for mnemonic "scotomas, J Neurosci, vol.13, issue.4, p.14791497, 1993.

J. M. Fuster, Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory, J Neurophysiol, vol.36, issue.1, p.6178, 1973.

S. Ganguli, J. W. Bisley, J. D. Roitman, M. N. Shadlen, M. E. Goldberg et al., One-Dimensional Dynamics of Attention and Decision Making in LIP, Neuron, vol.58, issue.1, p.1525, 2008.
DOI : 10.1016/j.neuron.2008.01.038

. Gerstner, Time structure of the activity in neural network models, Physical Review E, vol.51, issue.1, p.738758, 1995.
DOI : 10.1103/PhysRevE.51.738

. Gerstner and J. Van-hemmen, Coherence and incoherence in a globally coupled ensemble of pulse-emitting units, Physical Review Letters, vol.71, issue.3, p.312315, 1993.
DOI : 10.1103/PhysRevLett.71.312

W. Gerstner, Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking, Neural Computation, vol.20, issue.1, p.4389, 2000.
DOI : 10.1007/BF00288786

W. Gerstner and W. M. Kistler, Spiking Neuron Models, 2002.
DOI : 10.1017/cbo9780511815706

W. Gerstner, W. M. Kistler, R. Naud, and L. Paninski, Neuronal Dynamics, 2014.
DOI : 10.1017/CBO9781107447615

G. Gigante, M. Mattia, D. Giudice, and P. , Diverse Population-Bursting Modes of Adapting Spiking Neurons, Physical Review Letters, vol.98, issue.14, p.98148101, 2007.
DOI : 10.1103/PhysRevLett.98.148101

J. Gjorgjieva, C. Clopath, J. Audet, and J. Pster, A triplet spiketiming-dependent plasticity model generalizes the bienenstock-cooper-munro rule to higher-order spatiotemporal correlations, Proc Natl Acad Sci, vol.108, issue.48, 2011.

M. S. Goldman, Memory without feedback in a neural network, Neuron, vol.61, issue.4, p.621634, 2009.

B. S. Gutkin, C. R. Laing, C. L. Colby, C. C. Chow, and G. B. Ermentrout, Turning on and o with excitation: the role of spike-timing asynchrony and synchrony in sustained neural activity, J Comput Neurosci, vol.11, issue.2, p.121134, 2001.

R. M. Haefner, S. Gerwinn, J. H. Macke, and M. Bethge, Inferring decoding strategies from choice probabilities in the presence of correlated variability, Nature Neuroscience, vol.18, issue.2, p.235242, 2013.
DOI : 10.1016/j.conb.2008.09.004

D. P. Hanes, K. G. Thompson, and J. D. Schall, Relationship of presaccadic activity in frontal eye eld and supplementary eye eld to saccade initiation in macaque: Poisson spike train analysis, Exp Brain Res, vol.103, issue.1, pp.85-96, 1995.

T. D. Hanks, J. Ditterich, and M. N. Shadlen, Microstimulation of macaque area lip aects decision-making in a motion discrimination task, Nat Neurosci, vol.9, issue.5, p.682689, 2006.

T. D. Hanks, C. D. Kopec, B. W. Brunton, C. A. Duan, J. C. Erlich et al., Distinct relationships of parietal and prefrontal cortices to evidence accumulation, Nature, vol.22, issue.7546, p.520220223, 2015.
DOI : 10.1146/annurev-neuro-062111-150439

K. D. Harris, D. A. Henze, J. Csicsvari, H. Hirase, and G. Buzsáki, Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements, J Neurophysiol, vol.84, issue.1, p.401414, 2000.

B. Y. Hayden, S. R. Heilbronner, J. M. Pearson, and M. L. Platt, Surprise Signals in Anterior Cingulate Cortex: Neuronal Encoding of Unsigned Reward Prediction Errors Driving Adjustment in Behavior, Journal of Neuroscience, vol.31, issue.11, p.3141784187, 2011.
DOI : 10.1523/JNEUROSCI.4652-10.2011

B. Y. Hayden, J. M. Pearson, and M. L. Platt, Neuronal basis of sequential foraging decisions in a patchy environment, Nature Neuroscience, vol.22, issue.7, pp.14933-939, 2011.
DOI : 10.3758/BF03195489

M. Helias, T. Tetzla, and M. Diesmann, Echoes in correlated neural systems, New Journal of Physics, vol.15, issue.2, p.23002, 2013.
DOI : 10.1088/1367-2630/15/2/023002

L. Hertäg, D. Durstewitz, and N. Brunel, Analytical approximations of the ring rate of an adaptive exponential integrate-and-re neuron in the presence of synaptic noise, Front Comput Neurosci, vol.8, p.116, 2014.

D. Hoaglin, F. Mosteller, and J. Tukey, Understanding robust and exploratory data analysis. Wiley series in probability and mathematical statistics: Applied probability and statistics, 1983.

C. Holmgren, T. Harkany, B. Svennenfors, and Y. Zilberter, Pyramidal cell communication within local networks in layer 2/3 of rat neocortex, The Journal of Physiology, vol.551, issue.1, p.551139153, 2003.
DOI : 10.1113/jphysiol.2003.044784

J. Hopeld, Hopfield network, Scholarpedia, vol.2, issue.5, p.1977, 2007.
DOI : 10.4249/scholarpedia.1977

J. J. Hopeld, Neural networks and physical systems with emergent collective computational abilities, Proc Natl Acad Sci U S A, issue.8, p.7925542558, 1982.

T. Hromádka, M. R. Deweese, and A. M. Zador, Sparse Representation of Sounds in the Unanesthetized Auditory Cortex, PLoS Biology, vol.426, issue.1, p.16, 2008.
DOI : 10.1371/journal.pbio.0060016.sd002

A. C. Huk and M. N. Shadlen, Neural activity in macaque parietal cortex reects temporal integration of visual motion signals during perceptual decision making, J Neurosci, issue.45, p.251042010436, 2005.

R. S. Johansson and I. Birznieks, First spikes in ensembles of human tactile afferents code complex spatial fingertip events, Nature Neuroscience, vol.7, issue.2, pp.170-177, 2004.
DOI : 10.1038/nn1177

R. Jolivet, A. Rauch, H. Lüscher, and W. Gerstner, Predicting spike timing of neocortical pyramidal neurons by simple threshold models, Journal of Computational Neuroscience, vol.426, issue.66, p.3549, 2006.
DOI : 10.1007/s10827-006-7074-5

M. P. Karlsson, D. G. Tervo, and A. Y. Karpova, Network Resets in Medial Prefrontal Cortex Mark the Onset of Behavioral Uncertainty, Science, vol.338, issue.6103, p.338135139, 2012.
DOI : 10.1126/science.1226518

H. A. Katnani and N. J. Gandhi, Time course of motor preparation during visual search with exible stimulus-response association, J Neurosci, issue.24, p.331005710065, 2013.

A. Kepecs, N. Uchida, H. A. Zariwala, and M. , Neural correlates, computation and behavioural impact of decision condence, Z. F. Nature, issue.7210, p.455227231, 2008.

M. Khamassi, P. Enel, P. F. Dominey, and E. Procyk, Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters, Prog Brain Res, vol.202, p.441464, 2013.
DOI : 10.1016/B978-0-444-62604-2.00022-8

J. N. Kim and M. N. Shadlen, Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque, Nat Neurosci, vol.2, issue.2, p.176185, 1999.

R. Kobayashi, Y. Tsubo, and S. Shinomoto, Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold, Frontiers in Computational Neuroscience, vol.3, p.9, 2009.
DOI : 10.3389/neuro.10.009.2009

E. Koechlin, C. Ody, and F. Kouneiher, The Architecture of Cognitive Control in the Human Prefrontal Cortex, Science, vol.302, issue.5648, p.30211811185, 2003.
DOI : 10.1126/science.1088545

H. Köndgen, C. Geisler, S. Fusi, X. Wang, H. Lüscher et al., The Dynamical Response Properties of Neocortical Neurons to Temporally Modulated Noisy Inputs In Vitro, Cerebral Cortex, vol.18, issue.9, p.20862097, 2008.
DOI : 10.1093/cercor/bhm235

D. Kvitsiani, S. Ranade, B. Hangya, H. Taniguchi, J. Z. Huang et al., Distinct behavioural and network correlates of two interneuron types in prefrontal cortex, Nature, vol.455, issue.7454, p.498363366, 2013.
DOI : 10.1038/nature12176

L. Camera, G. Rauch, A. Lüscher, H. Senn, W. Fusi et al., Minimal BIBLIOGRAPHY 237 models of adapted neuronal response to in vivo-like input currents, Neural Comput, issue.10, p.1621012124, 2004.

L. Camera, G. Rauch, A. Thurbon, D. Lüscher, H. Senn et al., Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons, Journal of Neurophysiology, vol.96, issue.6, p.9634483464, 2006.
DOI : 10.1152/jn.00453.2006

K. W. Latimer, J. L. Yates, M. L. Meister, A. C. Huk, and J. W. Pillow, Neuronal modeling. single-trial spike trains in parietal cortex reveal discrete steps during decision-making, Science, issue.6244, p.349184187, 2015.

S. Lim and M. S. Goldman, Balanced cortical microcircuitry for maintaining information in working memory, Nature Neuroscience, vol.75, issue.9, p.13061314, 2013.
DOI : 10.1162/089976698300017845

B. Lindner and L. Schimansky-geier, Transmission of Noise Coded versus Additive Signals through a Neuronal Ensemble, Physical Review Letters, vol.86, issue.14, p.8629342937, 2001.
DOI : 10.1103/PhysRevLett.86.2934

A. Litwin-kumar and B. Doiron, Slow dynamics and high variability in balanced cortical networks with clustered connections, Nature Neuroscience, vol.15, issue.11, p.1514981505, 2012.
DOI : 10.1063/1.1703954

D. Liu, X. Gu, J. Zhu, X. Zhang, Z. Han et al., Medial prefrontal activity during delay period contributes to learning of a working memory task, Science, vol.346, issue.6208, p.346458463, 2014.
DOI : 10.1126/science.1256573

X. Liu, S. Ramirez, P. T. Pang, C. B. Puryear, A. Govindarajan et al., Optogenetic stimulation of a hippocampal engram activates fear memory recall, Nature, vol.390, issue.7394, p.484381385, 2012.
DOI : 10.1038/nature11028

R. Llinas, Neuron, Scholarpedia, vol.3, issue.8, p.1490, 2008.
DOI : 10.4249/scholarpedia.1490

L. Logiaco, R. Quilodran, E. Procyk, and A. Arleo, Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex, PLOS Biology, vol.29, issue.25, 2015.
DOI : 10.1371/journal.pbio.1002222.s020
URL : https://hal.archives-ouvertes.fr/hal-01224093

R. Luna, A. Hernández, C. D. Brody, and R. Romo, Neural codes 238 BIBLIOGRAPHY for perceptual discrimination in primary somatosensory cortex, Nat Neurosci, vol.8, issue.9, p.12101219, 2005.

C. K. Machens, R. Romo, and C. D. Brody, Flexible Control of Mutual Inhibition: A Neural Model of Two-Interval Discrimination, Science, vol.307, issue.5712, p.30711211124, 2005.
DOI : 10.1126/science.1104171

C. K. Machens, H. Schütze, A. Franz, O. Kolesnikova, M. B. Stemmler et al., Single auditory neurons rapidly discriminate conspecic communication signals, Nat Neurosci, vol.6, issue.4, p.341342, 2003.

E. Marder, M. L. Goeritz, and A. G. Otopalik, Robust circuit rhythms in small circuits arise from variable circuit components and mechanisms, Current Opinion in Neurobiology, vol.31, p.156163, 2015.
DOI : 10.1016/j.conb.2014.10.012

S. Martinez-conde, S. L. Macknik, and D. H. Hubel, Microsaccadic eye movements and ring of single cells in the striate cortex of macaque monkeys, 2000.

M. Martínez-garcía, E. T. Rolls, G. Deco, and R. Romo, Neural and computational mechanisms of postponed decisions, Proceedings of the National Academy of Sciences, vol.108, issue.28, p.1162611631, 2011.
DOI : 10.1073/pnas.1108137108

R. A. Mease, S. Lee, A. T. Moritz, R. K. Powers, M. D. Binder et al., Context-dependent coding in single neurons, Journal of Computational Neuroscience, vol.93, issue.3, p.459480, 2014.
DOI : 10.1007/s10827-014-0513-9

M. Medalla and H. Barbas, Synapses with inhibitory neurons dierentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control, Neuron, issue.4, p.61609620, 2009.

M. Megías, Z. Emri, T. F. Freund, and A. I. Gulyás, Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells, Neuroscience, vol.102, issue.3, p.527540, 2001.
DOI : 10.1016/S0306-4522(00)00496-6

S. Mensi, R. Naud, and W. Gerstner, From stochastic nonlinear integrate-and-re to generalized linear models, Advances in Neural Information Processing Systems 24, p.13771385, 2011.

S. Mensi, R. Naud, C. Pozzorini, M. Avermann, C. C. Petersen et al., Parameter extraction and classication of three cortical neuron types reveals two distinct adaptation mechanisms, J Neurophysiol, vol.107, issue.6, p.17561775, 2012.

T. Michelet, B. Bioulac, N. Langbour, M. Goillandeau, D. Guehl et al., Electrophysiological Correlates of a Versatile Executive Control System in the Monkey Anterior Cingulate Cortex, Cerebral Cortex, vol.26, issue.4, 2015.
DOI : 10.1093/cercor/bhv004
URL : https://hal.archives-ouvertes.fr/hal-01155375

G. Mongillo, O. Barak, and M. Tsodyks, Synaptic Theory of Working Memory, Science, vol.319, issue.5869, p.31915431546, 2008.
DOI : 10.1126/science.1150769

G. Mongillo, D. Hansel, and C. Van-vreeswijk, Bistability and Spatiotemporal Irregularity in Neuronal Networks with Nonlinear Synaptic Transmission, Physical Review Letters, vol.108, issue.15, p.158101, 2012.
DOI : 10.1103/PhysRevLett.108.158101

J. Moore, Voltage clamp, Scholarpedia, vol.2, issue.9, p.3060, 2007.
DOI : 10.4249/scholarpedia.3060

R. Moreno-bote, J. Beck, I. Kanitscheider, X. Pitkow, P. Latham et al., Information-limiting correlations, Nature Neuroscience, vol.16, issue.10, p.1714101417, 2014.
DOI : 10.1162/NECO_a_00125

R. Moreno-bote and N. Parga, Response of integrate-and-re neurons to noisy inputs ltered by synapses with arbitrary timescales: ring rate and correlations, Neural Comput, issue.6, p.2215281572, 2010.

E. Muller, L. Buesing, J. Schemmel, M. , and K. , Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories, Neural Computation, vol.20, issue.23, 2007.
DOI : 10.1016/S0006-3495(72)86068-5

R. Naud, B. Bathellier, and W. Gerstner, Spike-timing prediction in cortical neurons with active dendrites, Frontiers in Computational Neuroscience, vol.76, issue.66, p.90, 2014.
DOI : 10.1126/science.289.5488.2347
URL : https://hal.archives-ouvertes.fr/hal-01179725

R. Naud, F. Gerhard, S. Mensi, and W. Gerstner, Improved Similarity Measures for Small Sets of Spike Trains, Neural Computation, vol.47, issue.3, p.30163069, 2011.
DOI : 10.1523/JNEUROSCI.3699-06.2007

R. Naud and W. Gerstner, Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram, PLoS Computational Biology, vol.8, issue.10, p.1002711, 2012.
DOI : 10.1371/journal.pcbi.1002711.s004

R. Naud and W. Gerstner, The performance (and limits) of, 2012.

S. Ohayon, P. Grimaldi, N. Schweers, and D. Y. Tsao, Saccade modulation by optical and electrical stimulation in the macaque frontal eye eld, J Neurosci, issue.42, p.331668416697, 2013.

M. W. Oram, N. G. Hatsopoulos, B. J. Richmond, and J. P. Donoghue, Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures, J Neurophysiol, vol.86, issue.4, p.17001716, 2001.

S. Ostojic, Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons, Nature Neuroscience, vol.51, issue.4, p.594600, 2014.
DOI : 10.1152/jn.00830.2010

S. Ostojic and N. Brunel, From Spiking Neuron Models to Linear-Nonlinear Models, PLoS Computational Biology, vol.69, issue.Pt 2, p.1001056, 2011.
DOI : 10.1371/journal.pcbi.1001056.g007

A. R. Paiva, I. Park, and J. C. Principe, A comparison of binless spike train measures, Neural Computing and Applications, vol.96, issue.1, p.405419, 2010.
DOI : 10.1007/s00521-009-0307-6

A. Pala and C. C. Petersen, In vivo measurement of cell-typespecic synaptic connectivity and synaptic transmission in layer 2/3 mouse barrel cortex, Neuron, vol.85, issue.1, p.6875, 2015.

S. Panzeri, N. Brunel, N. K. Logothetis, and C. Kayser, Sensory neural codes using multiplexed temporal scales, Trends in Neurosciences, vol.33, issue.3, p.111120, 2010.
DOI : 10.1016/j.tins.2009.12.001

I. M. Park, M. L. Meister, A. C. Huk, and J. W. Pillow, Encoding and BIBLIOGRAPHY 241, 2014.

J. W. Pillow, J. Shlens, L. Paninski, A. Sher, A. M. Litke et al., Spatio-temporal correlations and visual signalling in a complete neuronal population, Nature, vol.22, issue.7207, p.454995999, 2008.
DOI : 10.1038/nature07140

C. Pozzorini, S. Mensi, O. Hagens, and W. Gerstner, Enhanced sensitivity to rapid input uctuations by nonlinear threshold dynamics, Frontiers in Neuroscience, issue.1, 2015.

C. Pozzorini, R. Naud, S. Mensi, and W. Gerstner, Temporal whitening by power-law adaptation in neocortical neurons, Nature Neuroscience, vol.23, issue.7, p.16942948, 2013.
DOI : 10.1088/0954-898X/15/4/002

E. Procyk and P. S. Goldman-rakic, Modulation of Dorsolateral Prefrontal Delay Activity during Self-Organized Behavior, Journal of Neuroscience, vol.26, issue.44, p.261131311323, 2006.
DOI : 10.1523/JNEUROSCI.2157-06.2006
URL : https://hal.archives-ouvertes.fr/inserm-00132158

E. Procyk, Y. L. Tanaka, J. , and J. P. , Anterior cingulate activity during routine and non-routine sequential behaviors in macaques, Nat Neurosci, vol.3, issue.5, p.502508, 2000.
URL : https://hal.archives-ouvertes.fr/inserm-00132133

E. Procyk, C. R. Wilson, F. M. Stoll, M. C. Faraut, M. Petrides et al., Midcingulate Motor Map and Feedback Detection: Converging Data from Humans and Monkeys, Cerebral Cortex, 2014.
DOI : 10.1093/cercor/bhu213
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4712795

R. Quilodran, M. Rothé, and E. Procyk, Behavioral Shifts and Action Valuation in the Anterior Cingulate Cortex, Neuron, vol.57, issue.2, p.314325, 2008.
DOI : 10.1016/j.neuron.2007.11.031
URL : https://hal.archives-ouvertes.fr/inserm-00906686

M. W. Reimann, C. A. Anastassiou, R. Perin, S. L. Hill, H. Markram et al., A biophysically detailed model of neocortical local eld potentials predicts the critical role of active membrane currents, Neuron, issue.2, p.79375390, 2013.

A. Renart, J. De-la-rocha, P. Bartho, L. Hollender, N. Parga et al., The Asynchronous State in Cortical Circuits, Science, vol.327, issue.5965, p.327587590, 2010.
DOI : 10.1126/science.1179850

A. Renart, R. Moreno-bote, X. Wang, and N. Parga, Meandriven and uctuation-driven persistent activity in recurrent networks, Neural Comput, vol.19, issue.1, p.146, 2007.

M. J. Richardson and W. Gerstner, Synaptic shot noise and 242 BIBLIOGRAPHY conductance uctuations aect the membrane voltage with equal signicance, 2005.

K. R. Ridderinkhof, M. Ullsperger, E. A. Crone, and S. Nieuwenhuis, The Role of the Medial Frontal Cortex in Cognitive Control, Science, vol.306, issue.5695, p.306443447, 2004.
DOI : 10.1126/science.1100301

M. Rigotti, O. Barak, M. R. Warden, X. Wang, N. D. Daw et al., The importance of mixed selectivity in complex cognitive tasks, Nature, vol.472, issue.7451, p.497585590, 2013.
DOI : 10.1038/nature12160

M. Rigotti, D. Ben-dayan-rubin, X. Wang, and S. Fusi, Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses, Frontiers in Computational Neuroscience, vol.4, p.24, 2010.
DOI : 10.3389/fncom.2010.00024

E. T. Rolls, F. Grabenhorst, and G. Deco, Decision-making, errors, and condence in the brain, J Neurophysiol, vol.104, issue.5, p.23592374, 2010.

M. A. Rossi, V. Y. Hayrapetyan, B. Maimon, K. Mak, H. S. Je et al., Prefrontal cortical mechanisms underlying delayed alternation in mice, Journal of Neurophysiology, vol.108, issue.4, 2012.
DOI : 10.1152/jn.01060.2011

M. Rothé, R. Quilodran, J. Sallet, and E. Procyk, Coordination of High Gamma Activity in Anterior Cingulate and Lateral Prefrontal Cortical Areas during Adaptation, Journal of Neuroscience, vol.31, issue.31, p.311111011117, 2011.
DOI : 10.1523/JNEUROSCI.1016-11.2011

A. T. Roussin, A. E. Agostino, A. M. Fooden, J. D. Victor, D. Lorenzo et al., Taste Coding in the Nucleus of the Solitary Tract of the Awake, Freely Licking Rat, Journal of Neuroscience, vol.32, issue.31, p.321049410506, 2012.
DOI : 10.1523/JNEUROSCI.1856-12.2012

M. Rudolph and A. Destexhe, Tuning neocortical pyramidal neurons between integrators and coincidence detectors, Journal of Computational Neuroscience, vol.14, issue.3, pp.239-51, 2003.
DOI : 10.1023/A:1023245625896
URL : https://hal.archives-ouvertes.fr/hal-00123835

M. Rudolph, M. Pospischil, I. Timofeev, and A. Destexhe, Inhibition Determines Membrane Potential Dynamics and Controls Action Potential Generation in Awake and Sleeping Cat Cortex, Journal of Neuroscience, vol.27, issue.20, p.2752805290, 2007.
DOI : 10.1523/JNEUROSCI.4652-06.2007
URL : https://hal.archives-ouvertes.fr/hal-00151880

H. P. Saal, S. Vijayakumar, and R. S. Johansson, Information about BIBLIOGRAPHY 243, 2009.

T. Satake, H. Mitani, K. Nakagome, and K. Kaneko, Individual and additive eects of neuromodulators on the slow components of afterhyperpolarization currents in layer v pyramidal cells of the rat medial prefrontal cortex, Brain Research, vol.1229, p.4760, 2008.

J. D. Schall, A. Morel, D. J. King, and J. Bullier, Topography of visual cortex connections with frontal eye eld in macaque: convergence and segregation of processing streams, J Neurosci, issue.6, p.1544644487, 1995.

T. Schwalger and B. Lindner, Patterns of interval correlations in neural oscillators with adaptation, Frontiers in Computational Neuroscience, vol.7, p.164, 2013.
DOI : 10.3389/fncom.2013.00164

H. S. Seung, How the brain keeps the eyes still, Proceedings of the National Academy of Sciences, vol.93, issue.23, p.931333913344, 1996.
DOI : 10.1073/pnas.93.23.13339

H. S. Seung, D. D. Lee, B. Y. Reis, and D. W. Tank, Stability of the Memory of Eye Position in a Recurrent Network of Conductance-Based Model Neurons, Neuron, vol.26, issue.1, p.259271, 2000.
DOI : 10.1016/S0896-6273(00)81155-1

M. N. Shadlen and W. T. Newsome, The variable discharge of cortical neurons: implications for connectivity, computation, and information coding, 1998.

A. Shenhav, M. M. Botvinick, and J. D. Cohen, The Expected Value of Control: An Integrative Theory of Anterior Cingulate Cortex Function, Neuron, vol.79, issue.2, p.79217240, 2013.
DOI : 10.1016/j.neuron.2013.07.007

S. A. Sheth, M. K. Mian, S. R. Patel, W. F. Asaad, Z. M. Williams et al., Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation, Nature, vol.174, issue.7410, p.488218221, 2012.
DOI : 10.1038/nature11239

T. Shmiel, R. Drori, O. Shmiel, Y. Ben-shaul, Z. Nadasdy et al., Neurons of the cerebral cortex exhibit 244 BIBLIOGRAPHY precise interspike timing in correspondence to behavior, Proc Natl Acad Sci, vol.102, issue.51, p.1865518657, 2005.

T. Shmiel, R. Drori, O. Shmiel, Y. Ben-shaul, Z. Nadasdy et al., Temporally precise cortical ring patterns are associated with distinct action segments, J Neurophysiol, issue.5, p.9626452652, 2006.

B. Shoelson, Deleteoutliers, 2003.

V. Sindhwani, S. Rakshit, D. Deodhare, D. Erdogmus, J. C. Principe et al., Feature Selection in MLPs and SVMs Based on Maximum Output Information, IEEE Transactions on Neural Networks, vol.15, issue.4, p.937948, 2004.
DOI : 10.1109/TNN.2004.828772

C. Sompolinsky and S. , Chaos in Random Neural Networks, Physical Review Letters, vol.61, issue.3, p.259262, 1988.
DOI : 10.1103/PhysRevLett.61.259

I. H. Stevenson and K. P. Kording, How advances in neural recording aect data analysis, Nat Neurosci, vol.14, issue.2, p.139142, 2011.

M. G. Stokes, M. Kusunoki, N. Sigala, H. Nili, D. Gaan et al., Dynamic Coding for Cognitive Control in Prefrontal Cortex, Neuron, vol.78, issue.2, p.78364375, 2013.
DOI : 10.1016/j.neuron.2013.01.039

R. L. Stratanovitch, Topics in the Theory of Random Noise, volume Volume I, 1963.

D. Sussillo and O. Barak, Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks, Neural Computation, vol.1, issue.7399, p.626649, 2013.
DOI : 10.1016/j.neuron.2008.09.034

B. Szatmáry and E. M. Izhikevich, Spike-Timing Theory of Working Memory, PLoS Computational Biology, vol.94, issue.2, 2010.
DOI : 10.1371/journal.pcbi.1000879.s006

T. Tchumatchenko, A. Malyshev, F. Wolf, and M. Volgushev, Ultrafast Population Encoding by Cortical Neurons, Journal of Neuroscience, vol.31, issue.34, p.311217112179, 2011.
DOI : 10.1523/JNEUROSCI.2182-11.2011

T. Tchumatchenko and F. Wolf, Representation of Dynamical Stimuli in Populations of Threshold Neurons, PLoS Computational Biology, vol.10, issue.10, pp.1002239-245, 2011.
DOI : 10.1371/journal.pcbi.1002239.t001

T. Tetzla, M. Helias, G. T. Einevoll, and M. Diesmann, Decorrelation of Neural-Network Activity by Inhibitory Feedback, PLoS Computational Biology, vol.8, issue.8, p.1002596, 2012.
DOI : 10.1371/journal.pcbi.1002596.t002

P. Theodoni, G. Kovács, M. W. Greenlee, and G. Deco, Neuronal adaptation eects in decision making, J Neurosci, issue.1, p.31234246, 2011.

E. G. Thomas, J. L. Van-hemmen, and W. M. Kistler, Calculation of volterra kernels for solutions of nonlinear dierential equations, SIAM Journal on Applied Mathematics, issue.1, p.61121, 2000.

K. Thurley, W. Senn, and H. Lüscher, Dopamine Increases the Gain of the Input-Output Response of Rat Prefrontal Pyramidal Neurons, Journal of Neurophysiology, vol.99, issue.6, p.9929852997, 2008.
DOI : 10.1152/jn.01098.2007

N. K. Totah, M. E. Jackson, and B. Moghaddam, Preparatory Attention Relies on Dynamic Interactions between Prelimbic Cortex and Anterior Cingulate Cortex, Cerebral Cortex, vol.23, issue.3, p.729738, 2013.
DOI : 10.1093/cercor/bhs057

T. Toyoizumi, K. R. Rad, and L. Paninski, Mean-eld approximations for coupled populations of generalized linear model spiking neurons with markov refractoriness, Neural Comput, vol.21, issue.5, p.12031243, 2009.

W. Truccolo, U. T. Eden, M. R. Fellows, J. P. Donoghue, and E. N. Brown, A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate eects, J Neurophysiol, vol.93, issue.2, p.10741089, 2005.

M. Tsodyks and S. Wu, Short-term synaptic plasticity, Scholarpedia, vol.8, issue.10, p.3153, 2013.
DOI : 10.4249/scholarpedia.3153

M. Ullsperger, C. Danielmeier, and G. Jocham, Neurophysiology of Performance Monitoring and Adaptive Behavior, Physiological Reviews, vol.94, issue.1, p.3579, 2014.
DOI : 10.1152/physrev.00041.2012

M. C. Van-rossum, A Novel Spike Distance, Neural Computation, vol.76, issue.4, p.751763, 2001.
DOI : 10.1088/0954-898X/8/2/003

C. Van-vreeswijk, L. F. Abbott, and G. B. Ermentrout, When inhibition not excitation synchronizes neural ring, J Comput Neurosci, vol.1, issue.4, p.313321, 1994.

C. Van-vreeswijk, H. Sompolinsky, C. Van-vreeswijk, and H. Sompolinsky, Chaos in neuronal networks with balanced excitatory and inhibitory activity Chaotic balanced state in a model of cortical circuits, Science Neural Comput, vol.274, issue.106, pp.17241726-246, 1996.

R. K. Vasudevan, M. B. Okatan, I. Rajapaksa, Y. Kim, D. Marincel et al., Higher order harmonic detection for exploring nonlinear interactions with nanoscale resolution, Scientific Reports, vol.20, p.2677, 2013.
DOI : 10.1038/srep02677

J. D. Victor, Spike train metrics, Current Opinion in Neurobiology, vol.15, issue.5, p.585592, 2005.
DOI : 10.1016/j.conb.2005.08.002

J. D. Victor and K. P. Purpura, Nature and precision of temporal coding in visual cortex: a metric-space analysis, J Neurophysiol, vol.76, issue.2, p.131026, 1996.

J. D. Victor and K. P. Purpura, Metric-space analysis of spike trains: theory, algorithms and application, Network: Computation in Neural Systems, vol.8, issue.2, p.127164, 1997.
DOI : 10.1088/0954-898X_8_2_003

G. Wainrib and J. Touboul, Topological and Dynamical Complexity of Random Neural Networks, Physical Review Letters, vol.110, issue.11, p.118101, 2013.
DOI : 10.1103/PhysRevLett.110.118101
URL : https://hal.archives-ouvertes.fr/hal-00942212

H. Wang, G. G. Stradtman, X. Wang, and W. Gao, A specialized NMDA receptor function in layer 5 recurrent microcircuitry of the adult rat prefrontal cortex, Proceedings of the National Academy of Sciences, vol.105, issue.43, p.1051679116796, 2008.
DOI : 10.1073/pnas.0804318105

M. Wang, Y. Yang, C. Wang, N. J. Gamo, L. E. Jin et al., Nmda receptors subserve persistent neuronal ring during working memory in dorsolateral prefrontal cortex, Neuron, vol.77, issue.4, p.736749, 2013.

X. Wang, Probabilistic Decision Making by Slow Reverberation in Cortical Circuits, Neuron, vol.36, issue.5, p.955968, 2002.
DOI : 10.1016/S0896-6273(02)01092-9

Y. Wang, H. Markram, P. H. Goodman, T. K. Berger, J. Ma et al., Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nature Neuroscience, vol.96, issue.4, p.534542, 2006.
DOI : 10.1038/nn1670

H. R. Wilson and J. D. Cowan, Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons, Biophysical Journal, vol.12, issue.1, p.124, 1972.
DOI : 10.1016/S0006-3495(72)86068-5

K. Wimmer, A. Compte, A. Roxin, D. Peixoto, A. Renart et al., Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT, Nature Communications, vol.448, p.6177, 2015.
DOI : 10.1038/ncomms7177

K. Wimmer, D. Q. Nykamp, C. Constantinidis, and A. Compte, Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory, Nature Neuroscience, vol.31, issue.3, p.431439, 2014.
DOI : 10.1093/cercor/bhj021

T. Womelsdorf, S. Ardid, S. Everling, and T. A. Valiante, Burst ring synchronizes prefrontal and anterior cingulate cortex during attentional control, 2014.

T. Womelsdorf, K. Johnston, M. Vinck, and S. Everling, Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors, Proceedings of the National Academy of Sciences, vol.107, issue.11, p.52485253, 2010.
DOI : 10.1073/pnas.0906194107

K. Wong and A. C. Huk, Temporal dynamics underlying perceptual decision making: Insights from the interplay between an attractor model and parietal neurophysiology, frontiers in Neuroscience, vol.2, issue.2, p.245254, 2008.
DOI : 10.3389/neuro.01.028.2008

K. Wong and X. Wang, A Recurrent Network Mechanism of Time Integration in Perceptual Decisions, Journal of Neuroscience, vol.26, issue.4, p.13141328, 2006.
DOI : 10.1523/JNEUROSCI.3733-05.2006

R. S. Zucker and W. G. Regehr, Short-term synaptic plasticity, Annu Rev Physiol, vol.64, p.355405, 2002.

R. Quilodran, E. Procyk, and A. Arleo, Spatiotemporal spike coding of behavioral adaptation in the dorsal anterior cingulate cortex In preparation: A dynamic non-linear mean-eld analysis of recurrent networks of adapting neurons in the asynchronous state, Appendices List of scientific communications Journal articles Logiaco

A. Arleo and W. Gerstner, A dynamic non-linear mean eld method for networks of adapting neurons in the asynchronous state, Beyond Mean Field workshop, 2015.

L. Logiaco, M. Deger, T. Schwalger, A. Arleo, and W. Gerstner, Towards the control of bistable attractors by temporally modulated inputs, Bernstein Conference, 2014.

L. Logiaco, R. Quilodran, E. Procyk, W. Gerstner, and A. Arleo, Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling, Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013, 2013.
DOI : 10.1152/jn.00408.2011
URL : https://hal.archives-ouvertes.fr/inserm-00842292

L. Logiaco, R. Quilodran, M. Rothe, E. Procyk, and A. Arleo, The spatiotemporal structure of anterior cingulate cortex activity contributes to behavioural adaptation coding 252 BIBLIOGRAPHY Selected talks 04: Seminar at the European Institute for Theoretical Neuroscience 06, 8th FENS Forum of Neuroscience Seminar at the Brain and Mind Institute research day, 2011.