Cargando…

Stabilizing working memory in spiking networks with biologically plausible synaptic dynamics

Detalles Bibliográficos
Autores principales:	Seeholzer, Alexander, Deger, Moritz, Gerstner, Wulfram
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2014
Materias:	Poster Presentation
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125078/ http://dx.doi.org/10.1186/1471-2202-15-S1-P157

Ejemplares similares

Stability of working memory in continuous attractor networks under the control of short-term plasticity
por: Seeholzer, Alexander, et al.
Publicado: (2019)

Multicontact Co-operativity in Spike-Timing–Dependent Structural Plasticity Stabilizes Networks
por: Deger, Moritz, et al.
Publicado: (2018)

Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics
por: Schwalger, Tilo, et al.
Publicado: (2015)

Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics
por: Faraji, Mohammad Javad, et al.
Publicado: (2014)

The influence of structure on the response properties of biologically plausible neural network models
por: Tomm, Christian, et al.
Publicado: (2011)

The role of interconnected hub neurons in cortical dynamics
por: Setareh, Hesam, et al.
Publicado: (2014)

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling
por: Logiaco, Laureline, et al.
Publicado: (2013)

Synaptic tagging and capture: a bridge from molecular to behaviour
por: Ziegler, Lorric, et al.
Publicado: (2011)

Towards a biological plausible model of the interaction of long-term memory and working memory
por: Nachstedt, Timo, et al.
Publicado: (2015)

Temporal sequence learning via adaptation in biologically plausible spiking neural networks
por: Duarte, Renato, et al.
Publicado: (2014)

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
por: Zenke, Friedemann, et al.
Publicado: (2015)

General conditions for spiking neurons and plasticity rules to perform independent component analysis
por: Brito, Carlos SN, et al.
Publicado: (2011)

Plasticity and stability in recurrent neural networks
por: Zenke, Friedemann, et al.
Publicado: (2011)

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
por: Schwalger, Tilo, et al.
Publicado: (2017)

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity
por: Schmutz, Valentin, et al.
Publicado: (2020)

Determinants of associative memory performance in spiking and non-spiking neural networks with different synaptic plasticity regimes
por: Metaxas, Alex, et al.
Publicado: (2012)

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

Phenomenological models of synaptic plasticity based on spike timing
por: Morrison, Abigail, et al.
Publicado: (2008)

Surprise minimization as a learning strategy in neural networks
por: Faraji, Mohammad Javad, et al.
Publicado: (2015)

Biological plausibility of kernel-based learning
por: Fortney, Kristen, et al.
Publicado: (2007)

Effective neuronal refractoriness dominates the statistics of superimposed spike trains
por: Deger, Moritz, et al.
Publicado: (2011)

Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
por: Pan, Wenxuan, et al.
Publicado: (2023)

Efficient modeling of neural activity using coupled renewal processes
por: Gerhard, Felipe, et al.
Publicado: (2011)

Biologically plausible reinforcement learning of continuous actions
por: Rombouts, Jaldert O, et al.
Publicado: (2013)

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

A hierarchy of time scales supports unsupervised learning of behavioral sequences
por: Muscinelli, Samuel P, et al.
Publicado: (2015)

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

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

Linking neural mass signals and spike train statistics through point process and linear systems theory
por: Deger, Moritz, et al.
Publicado: (2013)

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

A biological plausible recurrent model of V1 hypercolumns
por: Afşar, Atahan, et al.
Publicado: (2011)

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

How adaptation currents and synaptic inhibition change threshold, gain and variability of neuronal spiking
por: Ladenbauer, Josef, et al.
Publicado: (2013)

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

GPGPU implementation of a synaptically optimized, anatomically accurate spiking network simulator
por: Scorcioni, Ruggero
Publicado: (2010)

Non-instantaneous synaptic transmission in spiking neuron networks and equivalence with delay distribution
por: Biggio, Matteo, et al.
Publicado: (2013)

Emergent functional neural networks organized by spike timing dependent synaptic plasticity
por: Shin, Chang-Woo, et al.
Publicado: (2007)

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

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

Synaptic transmission of spike trains with arbitrary interspike intervals
por: Bird, Alex D, et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_7sls07piphb6nlijq84k1asdqv