Learning probabilistic inference through spike-timing-dependent plasticity
D Pecevski, W Maass - eneuro, 2016 - eneuro.org
Numerous experimental data show that the brain is able to extract information from complex,
uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information …
uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information …
[PDF][PDF] Maximising information yields spike timing dependent plasticity
AJ Bell, LC Parra - NIPS, 2004 - groups.oist.jp
Experiments show a synaptic weight potentiating if its presynaptic spike just preceded its
postsynaptic one, and depressing if it came just after, with a sharp transition at synchrony. To …
postsynaptic one, and depressing if it came just after, with a sharp transition at synchrony. To …
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
Neural responses in the cortex change over time both systematically, due to ongoing
plasticity and learning, and seemingly randomly, due to various sources of noise and …
plasticity and learning, and seemingly randomly, due to various sources of noise and …
[HTML][HTML] Synaptic and nonsynaptic plasticity approximating probabilistic inference
Learning and memory operations in neural circuits are believed to involve molecular
cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical …
cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical …
[HTML][HTML] Long-and short-term history effects in a spiking network model of statistical learning
The statistical structure of the environment is often important when making decisions. There
are multiple theories of how the brain represents statistical structure. One such theory states …
are multiple theories of how the brain represents statistical structure. One such theory states …
Emerging Bayesian priors in a self-organizing recurrent network
We explore the role of local plasticity rules in learning statistical priors in a self-organizing
recurrent neural network (SORN). The network receives input sequences composed of …
recurrent neural network (SORN). The network receives input sequences composed of …
[HTML][HTML] Network plasticity as Bayesian inference
D Kappel, S Habenschuss, R Legenstein… - PLoS computational …, 2015 - journals.plos.org
General results from statistical learning theory suggest to understand not only brain
computations, but also brain plasticity as probabilistic inference. But a model for that has …
computations, but also brain plasticity as probabilistic inference. But a model for that has …
[HTML][HTML] Where's the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network
Even in the absence of sensory stimulation the brain is spontaneously active. This
background “noise” seems to be the dominant cause of the notoriously high trial-to-trial …
background “noise” seems to be the dominant cause of the notoriously high trial-to-trial …
[HTML][HTML] Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
Animals perform near-optimal probabilistic inference in a wide range of psychophysical
tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties …
tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties …
Learning binary or real-valued time-series via spike-timing dependent plasticity
T Osogami - arXiv preprint arXiv:1612.04897, 2016 - arxiv.org
A dynamic Boltzmann machine (DyBM) has been proposed as a model of a spiking neural
network, and its learning rule of maximizing the log-likelihood of given time-series has been …
network, and its learning rule of maximizing the log-likelihood of given time-series has been …