Synaptic plasticity as Bayesian inference
نویسندگان
چکیده
منابع مشابه
Network Plasticity as Bayesian Inference
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 been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution ...
متن کاملSupplement: Network Plasticity as Bayesian Inference
Theorem 1. Let p(x,θ) be a strictly positive, continuous probability distribution over continuous or discrete states xn and continuous parameters θ = (θ1, . . . , θM ), twice continuously differentiable with respect to θ. Let b(θ) be a strictly positive, twice continuously differentiable function. Then the set of stochastic differential equations (S1) leaves the distribution p∗(θ) invariant. Fu...
متن کاملSupplemental Material to Network Plasticity as Bayesian Inference
where μ1 = 0.3, μ2 = 0.9, σ1 = 0.1, σ2 = 0.2 and c = 0.3. In Fig. 1D we used a prior pS(θ) = pS(θ1)pS(θ2), with pS(θi) given by a normal distribution (μ = 0.3, σ = 0.35). A learning rate of η = 0.005 was used to sampled trajectories which had a length of 50 and 300 time steps in Fig. 1C and F, respectively. In Fig. 1F the time-discrete version of the synaptic sampling algorithm (7) was used, wi...
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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 phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant m...
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ژورنال
عنوان ژورنال: Nature Neuroscience
سال: 2021
ISSN: 1097-6256,1546-1726
DOI: 10.1038/s41593-021-00809-5