Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
about
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral InhibitionBayesian Estimation and Inference Using Stochastic Electronics.Spike-Based Bayesian-Hebbian Learning of Temporal SequencesBuilding Machines That Learn and Think Like PeopleTop-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagationBayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticityNEVESIM: event-driven neural simulation framework with a Python interfaceEnsembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.Spiking neuron network Helmholtz machine.Population rate dynamics and multineuron firing patterns in sensory cortexStochastic variational learning in recurrent spiking networks.Bayesian networks in neuroscience: a survey.Synaptic and nonsynaptic plasticity approximating probabilistic inference.Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.Stochastic phase-change neurons.Rhythmic Inhibition Allows Neural Networks to Search for Maximally Consistent States.Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.Counter-factual mathematics of counterfactual predictive models.Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.Stochastic computations in cortical microcircuit models.Evaluation of ambiguous associations in the amygdala by learning the structure of the environment.Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.The high-conductance state enables neural sampling in networks of LIF neurons.Stochastic inference with spiking neurons in the high-conductance state.Probabilistic Inference in Neural Networks
P2860
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P2860
Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
description
2011 nî lūn-bûn
@nan
2011 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Probabilistic inference in gen ...... ic networks of spiking neurons
@ast
Probabilistic inference in gen ...... ic networks of spiking neurons
@en
Probabilistic inference in gen ...... ic networks of spiking neurons
@nl
type
label
Probabilistic inference in gen ...... ic networks of spiking neurons
@ast
Probabilistic inference in gen ...... ic networks of spiking neurons
@en
Probabilistic inference in gen ...... ic networks of spiking neurons
@nl
prefLabel
Probabilistic inference in gen ...... ic networks of spiking neurons
@ast
Probabilistic inference in gen ...... ic networks of spiking neurons
@en
Probabilistic inference in gen ...... ic networks of spiking neurons
@nl
P2860
P1476
Probabilistic inference in gen ...... ic networks of spiking neurons
@en
P2093
Dejan Pecevski
Lars Buesing
P2860
P304
P356
10.1371/JOURNAL.PCBI.1002294
P577
2011-12-15T00:00:00Z