From synapse to network: models of information storage and retrieval in neural circuits
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Nicolas Brunel | Johnatan Aljadeff | Maxwell Gillett | Ulises Pereira Obilinovic | N. Brunel | Johnatan Aljadeff | Maxwell Gillett
[1] Y. Miyashita. Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.
[2] Zoran Tiganj,et al. Sequential Firing Codes for Time in Rodent Medial Prefrontal Cortex , 2017, Cerebral cortex.
[3] E. Gardner. The space of interactions in neural network models , 1988 .
[4] V. Jayaraman,et al. Ring attractor dynamics in the Drosophila central brain , 2017, Science.
[5] Marc W Howard,et al. Time Cells in Hippocampal Area CA3 , 2016, The Journal of Neuroscience.
[6] Ulises Pereira,et al. Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data , 2017, Neuron.
[7] G. Bi,et al. Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses , 2009, Proceedings of the National Academy of Sciences.
[8] Christos Constantinidis,et al. Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex , 2016, Proceedings of the National Academy of Sciences.
[9] Haim Sompolinsky,et al. Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity , 2017, Proceedings of the National Academy of Sciences.
[10] P. Jonas,et al. Symmetric spike timing-dependent plasticity at CA3–CA3 synapses optimizes storage and recall in autoassociative networks , 2016, Nature Communications.
[11] Sandro Romani,et al. Learning in realistic networks of spiking neurons and spike‐driven plastic synapses , 2005, The European journal of neuroscience.
[12] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[13] D. Amit,et al. Statistical mechanics of neural networks near saturation , 1987 .
[14] R. Froemke. Plasticity of cortical excitatory-inhibitory balance. , 2015, Annual review of neuroscience.
[15] Sho Yagishita,et al. A critical time window for dopamine actions on the structural plasticity of dendritic spines , 2014, Science.
[16] Wei Ji Ma,et al. A diverse range of factors affect the nature of neural representations underlying short-term memory , 2018, Nature Neuroscience.
[17] Katie C. Bittner,et al. Behavioral time scale synaptic plasticity underlies CA1 place fields , 2017, Science.
[18] Terrence J Sejnowski,et al. Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks , 2020, Nature neuroscience.
[19] Kenneth D. Miller,et al. The Role of Constraints in Hebbian Learning , 1994, Neural Computation.
[20] Nicolas Brunel,et al. Is cortical connectivity optimized for storing information? , 2016, Nature Neuroscience.
[21] Aparna Suvrathan,et al. Beyond STDP—towards diverse and functionally relevant plasticity rules , 2019, Current Opinion in Neurobiology.
[22] Nicolas Y. Masse,et al. Circuit mechanisms for the maintenance and manipulation of information in working memory , 2018, Nature Neuroscience.
[23] G. Mongillo,et al. Inhibitory connectivity defines the realm of excitatory plasticity , 2018, Nature Neuroscience.
[24] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[25] Nicolas Brunel,et al. Synaptic plasticity rules with physiological calcium levels , 2020, Proceedings of the National Academy of Sciences.
[26] Everton J. Agnes,et al. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks , 2015, Nature Communications.
[27] K. Nakamura,et al. Mnemonic firing of neurons in the monkey temporal pole during a visual recognition memory task. , 1995, Journal of neurophysiology.
[28] Sukbin Lim,et al. Mechanisms underlying sharpening of visual response dynamics with familiarity , 2019, eLife.
[29] Christopher Summerfield,et al. Characterizing emergent representations in a space of candidate learning rules for deep networks , 2020, NeurIPS.
[30] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[31] Kanter,et al. Temporal association in asymmetric neural networks. , 1986, Physical review letters.
[32] D. Amit. The Hebbian paradigm reintegrated: Local reverberations as internal representations , 1995, Behavioral and Brain Sciences.
[33] O. Paulsen,et al. Activity-Dependent Downscaling of Subthreshold Synaptic Inputs during Slow-Wave-Sleep-like Activity In Vivo , 2018, Neuron.
[34] M. Sur,et al. Locally coordinated synaptic plasticity of visual cortex neurons in vivo , 2018, Science.
[35] N. Brunel,et al. Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location , 2012, Proceedings of the National Academy of Sciences.
[36] Ulises Pereira,et al. Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning , 2020, Proceedings of the National Academy of Sciences.
[37] A. Litwin-Kumar,et al. Formation and maintenance of neuronal assemblies through synaptic plasticity , 2014, Nature Communications.
[38] Richard Hans Robert Hahnloser,et al. Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity , 2010, Neuron.
[39] Naama Brenner,et al. Stable memory with unstable synapses , 2018, Nature Communications.
[40] Christos Constantinidis,et al. Persistent Spiking Activity Underlies Working Memory , 2018, The Journal of Neuroscience.
[41] Brent Doiron,et al. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses , 2015, PLoS Comput. Biol..
[42] D. Feldman. Synaptic mechanisms for plasticity in neocortex. , 2009, Annual review of neuroscience.
[43] J. Hopfield,et al. All-or-none potentiation at CA3-CA1 synapses. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[44] H Sompolinsky,et al. Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators. , 1988, Biophysical journal.
[45] Carl Smith,et al. Fast state-space methods for inferring dendritic synaptic connectivity , 2013, Journal of Computational Neuroscience.
[46] James M Murray,et al. Local online learning in recurrent networks with random feedback , 2018, bioRxiv.
[47] David J. Freedman,et al. Inferring learning rules from distribution of firing rates in cortical neurons , 2015, Nature Neuroscience.
[48] L. Cooper,et al. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[49] Christopher D. Harvey,et al. Recurrent Network Models of Sequence Generation and Memory , 2016, Neuron.
[50] Alex Roxin,et al. Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells , 2018, eLife.
[51] Sompolinsky,et al. Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.
[52] Thomas Miconi,et al. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks , 2016, bioRxiv.
[53] Alessandro Treves,et al. Efficiency of local learning rules in threshold-linear associative networks , 2020 .
[54] Earl K Miller,et al. Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not , 2018, The Journal of Neuroscience.
[55] Lisandro Montangie,et al. Autonomous emergence of connectivity assemblies via spike triplet interactions , 2020, PLoS computational biology.
[56] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[57] J. Kotaleski,et al. Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches , 2010, Nature Reviews Neuroscience.
[58] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[59] Richard Kempter,et al. Memory replay in balanced recurrent networks , 2016, bioRxiv.
[60] Timothy O’Leary,et al. Causes and consequences of representational drift , 2019, Current Opinion in Neurobiology.
[61] Misha Tsodyks,et al. Chaos in Highly Diluted Neural Networks , 1991 .
[62] E. Gardner,et al. An Exactly Solvable Asymmetric Neural Network Model , 1987 .
[63] Xiao-Jing Wang,et al. Task representations in neural networks trained to perform many cognitive tasks , 2019, Nature Neuroscience.
[64] Jochen Triesch,et al. Robust development of synfire chains from multiple plasticity mechanisms , 2014, Front. Comput. Neurosci..
[65] Julio Chapeton,et al. Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons , 2012, Proceedings of the National Academy of Sciences.
[66] W. Gerstner,et al. Connectivity reflects coding: a model of voltage-based STDP with homeostasis , 2010, Nature Neuroscience.
[67] W. Gerstner,et al. Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.
[68] Yoram Burak,et al. Shaping Neural Circuits by High Order Synaptic Interactions , 2016, PLoS Comput. Biol..
[69] S. Wang,et al. Malleability of Spike-Timing-Dependent Plasticity at the CA3–CA1 Synapse , 2006, The Journal of Neuroscience.
[70] Peter A. Appleby,et al. Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity , 2012, Front. Comput. Neurosci..
[71] M. Tsodyks,et al. The Enhanced Storage Capacity in Neural Networks with Low Activity Level , 1988 .
[72] S. Nelson,et al. Hebb and homeostasis in neuronal plasticity , 2000, Current Opinion in Neurobiology.
[73] U. Rutishauser,et al. Between persistently active and activity‐silent frameworks: novel vistas on the cellular basis of working memory , 2019, Annals of the New York Academy of Sciences.
[74] Sandro Romani,et al. Discrete attractor dynamics underlies persistent activity in the frontal cortex , 2019, Nature.
[75] Zachary P. Kilpatrick,et al. Networks that learn the precise timing of event sequences , 2014, bioRxiv.
[76] Daniel L. K. Yamins,et al. Identifying Learning Rules From Neural Network Observables , 2020, NeurIPS.