Cargando…

STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission

Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information transmission has also been recently postulated...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hennequin, Guillaume, Gerstner, Wulfram, Pfister, Jean-Pascal
Formato:	Texto
Lenguaje:	English
Publicado:	Frontiers Research Foundation 2010
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001990/ https://www.ncbi.nlm.nih.gov/pubmed/21160559 http://dx.doi.org/10.3389/fncom.2010.00143

Ejemplares similares

Voltage and Spike Timing Interact in STDP – A Unified Model
por: Clopath, Claudia, et al.
Publicado: (2010)

STDP in Recurrent Neuronal Networks
por: Gilson, Matthieu, et al.
Publicado: (2010)

STDP in Oscillatory Recurrent Networks: Theoretical Conditions for Desynchronization and Applications to Deep Brain Stimulation
por: Pfister, Jean-Pascal, et al.
Publicado: (2010)

An Adaptive STDP Learning Rule for Neuromorphic Systems
por: Gautam, Ashish, et al.
Publicado: (2021)

STDP and STDP variations with memristors for spiking neuromorphic learning systems
por: Serrano-Gotarredona, T., et al.
Publicado: (2013)

Adaptive STDP-based on-chip spike pattern detection
por: Gautam, Ashish, et al.
Publicado: (2023)

Spike-timing prediction in cortical neurons with active dendrites
por: Naud, Richard, et al.
Publicado: (2014)

STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons
por: Masquelier, Timothée
Publicado: (2018)

STDP in the Developing Sensory Neocortex
por: Larsen, Rylan S., et al.
Publicado: (2010)

STDP-based adaptive graph convolutional networks for automatic sleep staging
por: Zhao, Yuan, et al.
Publicado: (2023)

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model
por: Gastaldi, Chiara, et al.
Publicado: (2019)

Fast and richly structured activity in cortical networks with local inhibition
por: Hennequin, Guillaume, et al.
Publicado: (2011)

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector
por: Zenke, Friedemann, et al.
Publicado: (2013)

Homeostatic Plasticity and STDP: Keeping a Neuron's Cool in a Fluctuating World
por: Watt, Alanna J., et al.
Publicado: (2010)

STDP: Spiking, Timing, Rates and Beyond
por: Cooper, Leon N.
Publicado: (2010)

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram
por: Naud, Richard, et al.
Publicado: (2012)

From Hebb Rules to Spike-Timing-Dependent Plasticity: A Personal Account
por: Gerstner, Wulfram
Publicado: (2010)

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons
por: Setareh, Hesam, et al.
Publicado: (2017)

Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection
por: Masquelier, Timothée, et al.
Publicado: (2018)

Timing is not Everything: Neuromodulation Opens the STDP Gate
por: Pawlak, Verena, et al.
Publicado: (2010)

Modulating STDP Balance Impacts the Dendritic Mosaic
por: Iannella, Nicolangelo, et al.
Publicado: (2017)

Inference of neuronal network spike dynamics and topology from calcium imaging data
por: Lütcke, Henry, et al.
Publicado: (2013)

Optimal spike pattern v.s. noise separation by neurons equipped with STDP
por: Masquelier, Timothée, et al.
Publicado: (2013)

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo
por: Pawlak, Verena, et al.
Publicado: (2013)

Limits to high-speed simulations of spiking neural networks using general-purpose computers
por: Zenke, Friedemann, et al.
Publicado: (2014)

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
por: Frémaux, Nicolas, et al.
Publicado: (2016)

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
por: Gilra, Aditya, et al.
Publicado: (2017)

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
por: Muscinelli, Samuel P., et al.
Publicado: (2019)

Single electrode dynamic clamp with StdpC
por: Samu, David, et al.
Publicado: (2012)

Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation
por: Setareh, Hesam, et al.
Publicado: (2018)

Questions about STDP as a General Model of Synaptic Plasticity
por: Lisman, John, et al.
Publicado: (2010)

Unsupervised Feature Learning With Winner-Takes-All Based STDP
por: Ferré, Paul, et al.
Publicado: (2018)

On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs
por: Rastogi, Mehul, et al.
Publicado: (2021)

Rapid learning of predictive maps with STDP and theta phase precession
por: George, Tom M, et al.
Publicado: (2023)

Stochastic variational learning in recurrent spiking networks
por: Jimenez Rezende, Danilo, et al.
Publicado: (2014)

On the choice of metric in gradient-based theories of brain function
por: Surace, Simone Carlo, et al.
Publicado: (2020)

Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory
por: Martinolli, Marco, et al.
Publicado: (2018)

STDP and Mental Retardation: Dysregulation of Dendritic Excitability in Fragile X Syndrome
por: Meredith, Rhiannon M., et al.
Publicado: (2010)

Event-Based, Timescale Invariant Unsupervised Online Deep Learning With STDP
por: Thiele, Johannes C., et al.
Publicado: (2018)

On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights
por: Yousefzadeh, Amirreza, et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_6sru5b5maae88leeobnpnu0or1