Synaptic plasticity: taming the beast Information capacity of the hopfield model Analysis of the stabilized supralinear network URL http://dx.doi Routes to chaos in neural networks with random weights Eigenvalues of block structured asymmetric random matrices Transition to chaos in random networks with cell-type-specific connectivity Low-dimensional dynamics of structured random networks Characteristics of random nets of analog neuron-like elements, Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex, pp.461-4641994, 1972. ,
Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks, Physical Review Letters, vol.45, issue.14, pp.1530-1533, 1985. ,
DOI : 10.1051/jphys:01984004505084300
Methods and Models in Neurophysics, Lecture Notes of the Les Houches Summer School, 2003. ,
Power spectrum analysis of bursting cells in area MT in the behaving monkey, The Journal of Neuroscience, vol.14, issue.5, pp.2870-2892, 1994. ,
DOI : 10.1523/JNEUROSCI.14-05-02870.1994
Recurrent neural networks as versatile tools of neuroscience research, Current Opinion in Neurobiology, vol.46, pp.1-6, 2017. ,
DOI : 10.1016/j.conb.2017.06.003
The Sparseness of Mixed Selectivity Neurons Controls the Generalization-Discrimination Trade-Off, Journal of Neuroscience, vol.33, issue.9, pp.3844-3856 ,
DOI : 10.1523/JNEUROSCI.2753-12.2013
From fixed points to chaos: Three models of delayed discrimination, Progress in Neurobiology, vol.103, pp.214-222, 2013. ,
DOI : 10.1016/j.pneurobio.2013.02.002
Symmetric langevin spin glass dynamics, Ann. Probab, vol.25, issue.3, pp.1367-1422, 1997. ,
Theory of orientation tuning in visual cortex., Proc. Natl. Acad. Sci. USA, pp.3844-3848, 1995. ,
DOI : 10.1073/pnas.92.9.3844
URL : http://www.pnas.org/content/92/9/3844.full.pdf
Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, vol.5, issue.2, pp.157-166, 1994. ,
DOI : 10.1109/72.279181
URL : http://www.research.microsoft.com/~patrice/PDF/long_term.pdf
Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks, Neural Computation, vol.7, issue.7, pp.1413-1436, 2004. ,
DOI : 10.1126/science.274.5293.1724
Predictive Coding of Dynamical Variables in Balanced Spiking Networks, PLoS Computational Biology, vol.10, issue.7, pp.1-16, 2013. ,
DOI : 10.1371/journal.pcbi.1003258.s001
URL : http://doi.org/10.1371/journal.pcbi.1003258
The analysis of visual motion: a comparison of neuronal and psychophysical performance, The Journal of Neuroscience, vol.12, issue.12, pp.4745-47654745, 1992. ,
DOI : 10.1523/JNEUROSCI.12-12-04745.1992
Abstract, Visual Neuroscience, vol.30, issue.06, pp.1157-1169, 1993. ,
DOI : 10.1016/0042-6989(80)90128-5
Timing and Neural Encoding of Somatosensory Parametric Working Memory in Macaque Prefrontal Cortex, Cerebral Cortex, vol.13, issue.11, pp.1196-1207, 2003. ,
DOI : 10.1093/cercor/bhg100
URL : https://academic.oup.com/cercor/article-pdf/13/11/1196/767240/bhg100.pdf
, Bibliography
Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, Journal of Computational Neuroscience, vol.8, issue.3, pp.183-208, 2000. ,
DOI : 10.1023/A:1008925309027
Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates, Neural Computation, vol.15, issue.7, pp.1621-1671, 1999. ,
DOI : 10.1038/373612a0
Effects of Synaptic Noise and Filtering on the Frequency Response of Spiking Neurons, Physical Review Letters, vol.9, issue.10, pp.2186-2189, 2001. ,
DOI : 10.1162/neco.1997.9.5.971
Spike initiation by transmembrane current: a white-noise analysis., The Journal of Physiology, vol.260, issue.2, pp.279-314, 1976. ,
DOI : 10.1113/jphysiol.1976.sp011516
State-dependent computations: spatiotemporal processing in cortical networks, Nature Reviews Neuroscience, vol.16, issue.2, pp.113-125, 2009. ,
DOI : 10.1037/0735-7044.100.5.764
Accurate Path Integration in Continuous Attractor Network Models of Grid Cells, PLoS Computational Biology, vol.16, issue.2, pp.1-16, 2009. ,
DOI : 10.1371/journal.pcbi.1000291.s005
Mean-field equations, bifurcation map and route to chaos in discrete time neural networks, Physica D: Nonlinear Phenomena, vol.74, issue.1-2, pp.24-44, 1994. ,
DOI : 10.1016/0167-2789(94)90024-8
Variance as a Signature of Neural Computations during Decision Making, Neuron, vol.69, issue.4, pp.818-831, 2011. ,
DOI : 10.1016/j.neuron.2010.12.037
Temporal Complexity and Heterogeneity of Single-Neuron Activity in Premotor and Motor Cortex, Journal of Neurophysiology, vol.97, issue.6, pp.4235-4257, 2007. ,
DOI : 10.1038/331679a0
A Central Source of Movement Variability, Neuron, vol.52, issue.6, pp.1085-1096, 2006. ,
DOI : 10.1016/j.neuron.2006.10.034
Neural Variability in Premotor Cortex Provides a Signature of Motor Preparation, Journal of Neuroscience, vol.26, issue.14, pp.3697-3712, 2006. ,
DOI : 10.1523/JNEUROSCI.3762-05.2006
,
Stimulus onset quenches neural variability: a widespread Bibliography cortical phenomenon, Nat. Neurosci, vol.13, issue.3, pp.369-378, 2010. ,
Neural population dynamics during reaching, Nature, vol.26, issue.7405, pp.51-56, 2012. ,
DOI : 10.1523/JNEUROSCI.3762-05.2006
URL : http://europepmc.org/articles/pmc3393826?pdf=render
Neural population dynamics during reaching, Nature, vol.26, issue.7405, pp.51-56, 2012. ,
DOI : 10.1523/JNEUROSCI.3762-05.2006
URL : http://europepmc.org/articles/pmc3393826?pdf=render
Synaptic Plasticity: Multiple Forms, Functions and Mechanisms, Neuropsychopharmacology, vol.16, issue.1, pp.18-41, 2007. ,
DOI : 10.1146/annurev.physiol.64.092501.114547
Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model, Cerebral Cortex, vol.10, issue.9, p.910, 2000. ,
DOI : 10.1093/cercor/10.9.910
URL : https://academic.oup.com/cercor/article-pdf/10/9/910/9751089/100910.pdf
Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model, Physical Review A, vol.24, issue.10, pp.4922-4939, 1987. ,
DOI : 10.1007/BF01312880
Dimensionality reduction for large-scale neural recordings, Nature Neuroscience, vol.19, issue.11, pp.1500-1509, 2014. ,
DOI : 10.1152/jn.00097.2009
URL : http://europepmc.org/articles/pmc4433019?pdf=render
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, 2005. ,
The variability of discharge of simple cells in the cat striate cortex URL https, Experimental Brain Research, vol.44, issue.4, pp.437-440, 1981. ,
Neural network mechanisms underlying stimulus driven variability reduction URL https, PLOS Comput. Biol, vol.8, issue.3, pp.1-10, 2012. ,
DOI : 10.1371/journal.pcbi.1002395
URL : https://doi.org/10.1371/journal.pcbi.1002395
Using firing-rate dynamics to train recurrent networks of spiking model neurons. arXiv preprint, 2016. URL https ,
Destabilization and route to chaos in neural networks with random connectivity, 1993. ,
Self-consistent determination of the spiketrain power spectrum in a neural network with sparse connectivity, Front. Comput. Neurosci, vol.8 ,
Neural Engineering -Computation, Representation, and Dynamics in Neurobiological Systems, 2004. ,
A hypothesis for basal ganglia-dependent reinforcement learning in the songbird, Neuroscience, vol.198, pp.152-170, 2011. ,
DOI : 10.1016/j.neuroscience.2011.09.069
URL : http://europepmc.org/articles/pmc3221789?pdf=render
Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex, Journal of Neurophysiology, vol.61, issue.2, pp.331-34961331, 1989. ,
DOI : 10.1152/jn.1989.61.2.331
On simplicity and complexity in the brave new world of large-scale neuroscience, Current Opinion in Neurobiology, vol.32, pp.148-55, 2015. ,
DOI : 10.1016/j.conb.2015.04.003
A chaos hypothesis for some large systems of random equations. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, pp.291-314, 1982. ,
DOI : 10.1007/bf00535717
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, 2014. ,
DOI : 10.1017/CBO9781107447615
Circular Law, Theory of Probability & Its Applications, vol.29, issue.4, pp.694-706, 1985. ,
DOI : 10.1137/1129095
Noise dynamically suppresses chaos in random neural networks. arXiv preprint, 2016. URL https ,
Partitioning neuronal variability, Nature Neuroscience, vol.108, issue.6, pp.858-865, 2014. ,
DOI : 10.1080/01621459.2013.829001
URL : http://europepmc.org/articles/pmc4135707?pdf=render
How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?, Journal of Computational Neuroscience, vol.12, issue.5965, pp.469-481, 2014. ,
DOI : 10.1016/S0006-3495(72)86068-5
Asynchronous rate chaos in spiking neuronal circuits, PLOS Comput. Biol, vol.11, issue.7, pp.1-38, 2015. ,
DOI : 10.1371/journal.pcbi.1004266
URL : https://doi.org/10.1371/journal.pcbi.1004266
Cortical connectivity and sensory coding, Nature, vol.16, issue.7474, pp.51-58, 2013. ,
DOI : 10.1038/nn.3305
Non-normal amplification in random balanced neuronal networks, Physical Review E, vol.86, issue.1 ,
DOI : 10.1126/science.1179850
URL : https://infoscience.epfl.ch/record/181041/files/e011909.pdf
Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements, Neuron, vol.82, issue.6, pp.1394-1406, 2014. ,
DOI : 10.1016/j.neuron.2014.04.045
Decoding a Perceptual Decision Process across Cortex, Neuron, vol.66, issue.2, pp.300-314, 2010. ,
DOI : 10.1016/j.neuron.2010.03.031
The probability of transmitter release at a mammalian central synapse, Nature, vol.366, issue.6455, pp.569-572, 1993. ,
DOI : 10.1038/366569a0
A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952. ,
DOI : 10.1113/jphysiol.1952.sp004764
Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA, pp.792554-2558, 1982. ,
Once upon a (slow) time in the land of recurrent neuronal networks???, Current Opinion in Neurobiology, vol.46, pp.31-38, 2017. ,
DOI : 10.1016/j.conb.2017.07.003
The ''echo state'' approach to analysing and training recurrent neural networks -with an erratum note. German National Research Center for Information Technology, 2001. ,
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication, Science, vol.304, issue.5667, pp.78-8078, 2004. ,
DOI : 10.1126/science.1091277
URL : http://www.columbia.edu/cu/biology/courses/w4070/Reading_List_Yuste/haas_04.pdf
Transition to Chaos in Random Neuronal Networks, Physical Review X, vol.18, issue.4, p.41030, 2015. ,
DOI : 10.1371/journal.pcbi.1004266
URL : http://doi.org/10.1103/physrevx.5.041030
Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors, Cognitive Computation, vol.114, issue.1, pp.139-159, 2009. ,
DOI : 10.1007/978-3-642-88163-3
Mrsic-Flogel. Functional specificity of local synaptic connections in neocortical networks, Nature, issue.7345, pp.47387-91, 2011. ,
The emergence of functional microcircuits in visual cortex, Nature, vol.26, issue.7443, pp.96-100, 2013. ,
DOI : 10.1007/s10827-008-0117-3
URL : http://europepmc.org/articles/pmc4843961?pdf=render
Robust timing and motor patterns by taming chaos in recurrent neural networks, Nature Neuroscience, vol.20, issue.7, pp.925-933, 2013. ,
DOI : 10.1103/PhysRevE.76.026107
URL : http://europepmc.org/articles/pmc3753043?pdf=render
Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs, Frontiers in Computational Neuroscience, vol.5, p.25, 2011. ,
DOI : 10.3389/fncom.2011.00025
Variability and Correlated Noise in the Discharge of Neurons in Motor and Parietal Areas of the Primate Cortex, The Journal of Neuroscience, vol.18, issue.3, pp.1161-1170, 1161. ,
DOI : 10.1523/JNEUROSCI.18-03-01161.1998
Edge of chaos and prediction of computational performance for neural circuit models, Neural Networks, vol.20, issue.3, pp.323-334, 2007. ,
DOI : 10.1016/j.neunet.2007.04.017
Mean field theory for a balanced hypercolumn model of orientation selectivity in primary visual cortex, Network: Computation in Neural Systems, vol.12, issue.41, pp.131-150, 2006. ,
DOI : 10.1162/089976698300017214
Slow dynamics and high variability in balanced cortical networks with clustered connections, Nature Neuroscience, vol.15, issue.11, pp.1498-1505, 2012. ,
DOI : 10.1063/1.1703954
URL : http://europepmc.org/articles/pmc4106684?pdf=render
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation, vol.7, issue.11 ,
DOI : 10.1038/35009102
URL : http://www.ee.oulu.fi/~johannes/research/papers/maass02realtime.pdf
, Neural Comput, vol.14, issue.11, pp.2531-2560, 2002.
Computational aspects of feedback in neural circuits, PLOS Computat. Biol, vol.3, issue.1, pp.1-20, 2007. ,
Flexible Control of Mutual Inhibition: A Neural Model of Two-Interval Discrimination, Science, vol.307, issue.5712, pp.1121-1124, 1121. ,
DOI : 10.1126/science.1104171
Reliability of spike timing in neocortical neurons, Science, vol.268, issue.5216, pp.1503-1506, 1503. ,
DOI : 10.1126/science.7770778
Context-dependent computation by recurrent dynamics in prefrontal cortex, Nature, vol.6, issue.7474, pp.78-84 ,
DOI : 10.1126/science.1104171
Detecting and Estimating Signals in Noisy Cable Structures, I: Neuronal Noise Sources, Neural Computation, vol.66, issue.5, pp.1797-1829, 1999. ,
DOI : 10.1016/0079-6107(74)90019-4
Learning recurrent neural networks with hessian-free optimization, ICML, 2011. ,
Mean-field theory of echo state networks, Physical Review E, vol.21, issue.4, p.42809, 2013. ,
DOI : 10.1364/OE.20.003241
Intrinsically-generated fluctuating activity in excitatory-inhibitory networks URL https, PLOS Comp. Biol, vol.13, issue.4, pp.1-40, 2017. ,
Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks. eLife, 6, 2017. URL https ,
Suppressing chaos in neural networks by noise, Physical Review Letters, vol.74, issue.26, pp.3717-3719, 1992. ,
DOI : 10.1007/BF01311399
Large deviations and mean-field theory for asymmetric random recurrent neural networks, Probability Theory and Related Fields, vol.123, issue.1, pp.41-75, 2002. ,
DOI : 10.1007/s004400100182
Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns, Neuron, vol.61, issue.4, pp.635-648, 2009. ,
DOI : 10.1016/j.neuron.2009.02.005
A hierarchy of intrinsic timescales across primate cortex, Nature Neuroscience, vol.20, issue.12, pp.1661-1663, 2014. ,
DOI : 10.1093/cercor/bht031
Firing variability in cat association cortex during sleep and wakefulness, Brain Research, vol.18, issue.3, pp.513-526, 1970. ,
DOI : 10.1016/0006-8993(70)90134-4
Interspike interval distributions of spiking neurons driven by fluctuating inputs, Journal of Neurophysiology, vol.16, issue.1, pp.361-373361, 2011. ,
DOI : 10.1126/science.274.5293.1724
URL : http://jn.physiology.org/content/jn/106/1/361.full.pdf
Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons, Nature Neuroscience, vol.51, issue.4, pp.594-600, 2014. ,
DOI : 10.1152/jn.00830.2010
From Spiking Neuron Models to Linear-Nonlinear Models, PLoS Computational Biology, vol.69, issue.Pt 2, pp.1-16 ,
DOI : 10.1371/journal.pcbi.1001056.g007
URL : https://doi.org/10.1371/journal.pcbi.1001056
Recurrent interactions in spiking networks with arbitrary topology, Physical Review E, vol.33, issue.3, p.31916, 2012. ,
DOI : 10.1093/cercor/bhn240
URL : http://arxiv.org/pdf/1201.0288
Eigenvalue Spectra of Random Matrices for Neural Networks, Physical Review Letters, vol.3, issue.18, p.188104, 2006. ,
DOI : 10.1137/1129095
Stimulus-dependent suppression of chaos in recurrent neural networks, Physical Review E, vol.19, issue.1, p.11903, 2010. ,
DOI : 10.1126/science.1179850
URL : http://arxiv.org/pdf/0912.3513
Recurrent Network Models of Sequence Generation and Memory, Neuron, vol.90, issue.1, pp.128-142, 2016. ,
DOI : 10.1016/j.neuron.2016.02.009
URL : https://doi.org/10.1016/j.neuron.2016.02.009
Neocortical Pyramidal Cells Respond as Integrate-and-Fire Neurons to In Vivo???Like Input Currents, Journal of Neurophysiology, vol.90, issue.3, pp.1598-1612, 1598. ,
DOI : 10.1007/BF00374778
URL : http://jn.physiology.org/content/jn/90/3/1598.full.pdf
Variability in neural activity and behavior, Current Opinion in Neurobiology, vol.25, pp.211-220, 2014. ,
DOI : 10.1016/j.conb.2014.02.013
Mean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks, Neural Computation, vol.19, issue.1, pp.1-46, 2006. ,
DOI : 10.1038/370140a0
Mean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks, Neural Computation, vol.19, issue.1, pp.1-46, 2007. ,
DOI : 10.1038/370140a0
The Asynchronous State in Cortical Circuits, Science, vol.28, issue.20, pp.587-590, 2010. ,
DOI : 10.1523/JNEUROSCI.3508-05.2005
URL : http://europepmc.org/articles/pmc2861483?pdf=render
Spikes: Exploring the Neural Code, 1999. ,
The importance of mixed selectivity in complex cognitive tasks, Nature, vol.472, issue.7451, pp.585-590, 2013. ,
DOI : 10.1038/nature09868
Local Dynamics in Trained Recurrent Neural Networks, Physical Review Letters, vol.118, issue.25, p.258101, 2017. ,
DOI : 10.1038/nphys2741
URL : http://arxiv.org/pdf/1511.05222
Neuronal correlates of parametric working memory in the prefrontal cortex, Nature, vol.399, issue.6735, pp.470-473, 1999. ,
DOI : 10.1038/20939
A balanced memory network URL https, PLOS Comput. Biol, vol.3, issue.9, pp.1-22, 2007. ,
DOI : 10.1371/journal.pcbi.0030141.eor
URL : https://doi.org/10.1371/journal.pcbi.0030141.eor
The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex, Neuron, vol.85, issue.2, pp.402-417, 2014. ,
DOI : 10.1016/j.neuron.2014.12.026
URL : https://doi.org/10.1016/j.neuron.2014.12.026
Parallel distributed processing: Explorations in the microstructure of cognition, pp.318-362, 1986. ,
Abstract Context Representations in Primate Amygdala and Prefrontal Cortex, Neuron, vol.87, issue.4, pp.869-881, 2015. ,
DOI : 10.1016/j.neuron.2015.07.024
URL : https://doi.org/10.1016/j.neuron.2015.07.024
A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks, PLoS Computational Biology, vol.20, issue.10 ,
DOI : 10.1371/journal.pcbi.1003301.t001
Short-term response variability of monkey striate neurons, Brain Research, vol.105, issue.2, pp.347-349, 1976. ,
DOI : 10.1016/0006-8993(76)90432-7
Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing, Neural Computation, vol.18, issue.4, pp.1679-1703, 1998. ,
DOI : 10.1016/S0006-3495(95)79995-7
Putting big data to good use in neuroscience, Nature Neuroscience, vol.17, issue.11, pp.1440-1441 ,
DOI : 10.1126/science.3055294
URL : http://europepmc.org/articles/pmc4224030?pdf=render
How the brain keeps the eyes still, Proc. Natl. Acad. Sci. USA, pp.13339-13344, 1996. ,
DOI : 10.1073/pnas.79.8.2554
URL : http://www.pnas.org/content/93/23/13339.full.pdf
Noise, neural codes and cortical organization, Current Opinion in Neurobiology, vol.4, issue.4, pp.569-79, 1994. ,
DOI : 10.1016/0959-4388(94)90059-0
The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding, The Journal of Neuroscience, vol.18, issue.10, pp.3870-3896, 1998. ,
DOI : 10.1523/JNEUROSCI.18-10-03870.1998
URL : http://www.jneurosci.org/content/18/10/3870.full.pdf
Slow diffusive dynamics in a chaotic balanced neural network URL https, PLOS Comput. Biol, vol.13, issue.5, pp.1-26, 2017. ,
DOI : 10.1371/journal.pcbi.1005505
URL : http://doi.org/10.1371/journal.pcbi.1005505
Self-consistent signal-to-noise analysis of the statistical behavior of analog neural networks and enhancement of the storage capacity, Physical Review E, vol.6, issue.2, pp.867-897, 1993. ,
DOI : 10.1016/S0893-6080(05)80076-0
Rate Models for Conductance-Based Cortical Neuronal Networks, Neural Computation, vol.16, issue.8, pp.1809-1841, 2003. ,
DOI : 10.1016/S0006-3495(72)86068-5
URL : https://hal.archives-ouvertes.fr/hal-00173803
Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells, PLoS Computational Biology, vol.94, issue.4, pp.1-18 ,
DOI : 10.1371/journal.pcbi.1003558.s001
URL : https://doi.org/10.1371/journal.pcbi.1003558
On the First Passage Time Probability Problem, Physical Review, vol.36, issue.4, pp.617-623, 1951. ,
DOI : 10.1103/PhysRev.36.823
The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs, The Journal of Neuroscience, vol.13, issue.1, pp.334-350, 1993. ,
DOI : 10.1523/JNEUROSCI.13-01-00334.1993
Chaos in Random Neural Networks, Physical Review Letters, vol.103, issue.3, pp.259-262, 1988. ,
DOI : 10.1007/BF01464284
Reward-based training of recurrent neural networks for cognitive and value-based tasks ,
DOI : 10.7554/elife.21492
URL : https://cdn.elifesciences.org/articles/21492/elife-21492-v2.pdf
Highly nonrandom features of synaptic connectivity in local cortical circuits, 03 2005. URL https ,
DOI : 10.1371/journal.pbio.0030068
URL : https://doi.org/10.1371/journal.pbio.0030068
Dynamics of random neural networks with bistable units, Physical Review E, vol.29, issue.6, p.62710, 2014. ,
DOI : 10.1038/nature03252
URL : http://europepmc.org/articles/pmc4348075?pdf=render
Nonlinear Dynamics And Chaos. Studies in nonlinearity, 2007. ,
Generating Coherent Patterns of Activity from Chaotic Neural Networks, Neuron, vol.63, issue.4, pp.544-557, 2009. ,
DOI : 10.1016/j.neuron.2009.07.018
URL : https://doi.org/10.1016/j.neuron.2009.07.018
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks, Neural Computation, vol.1, issue.7399, pp.626-649, 2012. ,
DOI : 10.1016/j.neuron.2008.09.034
A neural network that finds a naturalistic solution for the production of muscle activity, Nature Neuroscience, vol.110, issue.7, pp.1025-1033, 2015. ,
DOI : 10.1152/jn.00892.2011
Outliers in the spectrum of iid matrices with bounded rank perturbations. Probab. Theory Relat. Fields, pp.231-263, 2013. ,
Random matrices: Universality of ESDs and the circular law, The Annals of Probability, vol.38, issue.5, pp.2023-206510, 2010. ,
DOI : 10.1214/10-AOP534
URL : http://doi.org/10.1214/10-aop534
Decorrelation of neuralnetwork activity by inhibitory feedback, PLOS Computat. Biol, vol.8, issue.8, pp.1-29 ,
Learning universal computations with spikes URL https, PLOS Comput. Biol, vol.12, issue.6, pp.1-29, 2016. ,
DOI : 10.1371/journal.pcbi.1004895
URL : http://doi.org/10.1371/journal.pcbi.1004895
Chaos in Highly Diluted Neural Networks, Europhysics Letters (EPL), vol.14, issue.8, p.727, 1991. ,
DOI : 10.1209/0295-5075/14/8/001
Neuronal variability: non-stationary responses to identical visual stimuli, Brain Research, vol.79, issue.3, pp.405-418, 1974. ,
DOI : 10.1016/0006-8993(74)90438-7
Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime, Physical Review E, vol.84, issue.5, p.51908, 2011. ,
DOI : 10.1209/0295-5075/14/8/001
Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell, Neural Computation, vol.21, issue.5, 1997. ,
DOI : 10.1016/S0022-5193(83)80013-7
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity, Science, vol.274, issue.5293, pp.1724-1726, 1724. ,
DOI : 10.1126/science.274.5293.1724
The response variability of striate cortical neurons in the behaving monkey, Experimental Brain Research, vol.79, issue.2, pp.432-436, 1989. ,
DOI : 10.1007/BF00275002
Chaotic Balanced State in a Model of Cortical Circuits, Neural Computation, vol.13, issue.6, pp.1321-1371, 1998. ,
DOI : 10.1016/S0006-3495(72)86068-5
Topological and Dynamical Complexity of Random Neural Networks, Physical Review Letters, vol.23, issue.11, p.118101, 2013. ,
DOI : 10.1016/0022-0396(83)90011-6
URL : https://hal.archives-ouvertes.fr/hal-00942212
Probabilistic Decision Making by Slow Reverberation in Cortical Circuits, Neuron, vol.36, issue.5, pp.955-968, 2002. ,
DOI : 10.1016/S0896-6273(02)01092-9
URL : https://doi.org/10.1016/s0896-6273(02)01092-9
Decision Making in Recurrent Neuronal Circuits, Neuron, vol.60, issue.2, pp.215-234, 2008. ,
DOI : 10.1016/j.neuron.2008.09.034
URL : https://doi.org/10.1016/j.neuron.2008.09.034
Slow fluctuations in recurrent networks of spiking neurons, Physical Review E, vol.14, issue.4 ,
DOI : 10.1007/978-94-011-7801-3
URL : https://infoscience.epfl.ch/record/212958/files/2015_Wieland_etal_slow_fluctuations_recurr_net.pdf
Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models, PLOS Computational Biology, vol.27, issue.12, pp.1-27 ,
DOI : 10.1371/journal.pcbi.1005141.s004
URL : https://doi.org/10.1371/journal.pcbi.1005141
Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons, Biophysical Journal, vol.12, issue.1, pp.1-24, 1972. ,
DOI : 10.1016/S0006-3495(72)86068-5
URL : https://doi.org/10.1016/s0006-3495(72)86068-5
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions, Journal of Neuroscience, vol.26, issue.4, pp.1314-1328, 1314. ,
DOI : 10.1523/JNEUROSCI.3733-05.2006
URL : http://www.jneurosci.org/content/jneuro/26/4/1314.full.pdf