Learning in neural networks by reinforcement of irregular spiking.
about
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning RulesFunctional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.Spike-based reinforcement learning in continuous state and action space: when policy gradient methods failEnabling functional neural circuit simulations with distributed computing of neuromodulated plasticity.Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment.Reinforcement learning on slow features of high-dimensional input streamsLearning shapes spontaneous activity itinerating over memorized states.Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.An imperfect dopaminergic error signal can drive temporal-difference learning.Reinforcement learning, spike-time-dependent plasticity, and the BCM rule.Reinforcement learning using a continuous time actor-critic framework with spiking neurons.Excitatory, inhibitory, and structural plasticity produce correlated connectivity in random networks trained to solve paired-stimulus tasksChoice-correlated activity fluctuations underlie learning of neuronal category representationA Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations.Sniff-Like Patterned Input Results in Long-Term Plasticity at the Rat Olfactory Bulb Mitral and Tufted Cell to Granule Cell Synapse.Computational models of reinforcement learning: the role of dopamine as a reward signal.Theory and simulation in neuroscience.A reinforcement learning framework for spiking networks with dynamic synapses.Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates.STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission.Synaptic theory of replicator-like melioration.Timing is not Everything: Neuromodulation Opens the STDP GateModel of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances.Code-specific learning rules improve action selection by populations of spiking neurons.Stochastic gradient ascent learning with spike timing dependent plasticity.Direct reinforcement learning, spike time dependent plasticity and the BCM rule.Does high firing irregularity enhance learning?Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity.Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression.Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning.Stimulus sampling as an exploration mechanism for fast reinforcement learning.A spiking neural network model of an actor-critic learning agent.Reinforcement learning in complementarity game and population dynamics.Statistical mechanics of structural and temporal credit assignment effects on learning in neural networks.Reinforcement learning with modulated spike timing dependent synaptic plasticity.Dimensional reduction for reward-based learning.
P2860
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P2860
Learning in neural networks by reinforcement of irregular spiking.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
2004年學術文章
@zh-hant
name
Learning in neural networks by reinforcement of irregular spiking.
@en
Learning in neural networks by reinforcement of irregular spiking.
@nl
type
label
Learning in neural networks by reinforcement of irregular spiking.
@en
Learning in neural networks by reinforcement of irregular spiking.
@nl
prefLabel
Learning in neural networks by reinforcement of irregular spiking.
@en
Learning in neural networks by reinforcement of irregular spiking.
@nl
P2860
P1433
P1476
Learning in neural networks by reinforcement of irregular spiking.
@en
P2093
H Sebastian Seung
Xiaohui Xie
P2860
P304
P356
10.1103/PHYSREVE.69.041909
P407
P433
P577
2004-04-30T00:00:00Z