# Probabilistic Synapses

@inproceedings{Aitchison2014ProbabilisticS, title={Probabilistic Synapses}, author={Laurence Aitchison and Alexandre Pouget and Peter E. Latham}, year={2014} }

Learning, especially rapid learning, is critical for survival. However, learning is hard: a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights — not just point estimates — is the optimal strategy. Here we hypothesize that synapses take that optimal strategy: they do not store just the mean weight; they also store their degree of uncertainty — in essence, they put…

## 5 Citations

Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma

- BiologyeLife
- 2021

It is found that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses, thus connecting aPresynaptic notion of importance to a computational role in lifelong learning.

Presynaptic Stochasticity Improves Energy Efficiency and Alleviates the Stability-Plasticity Dilemma

- BiologybioRxiv
- 2021

It is found that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses, thus connecting a presyaptic notion of importance to a computational role in lifelong learning.

Ongoing, rational calibration of reward-driven perceptual biases

- Psychology, BiologybioRxiv
- 2018

It is shown that for an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity that were consistent with a normative accumulate-to-bound process.

Rapid Bayesian learning in the mammalian olfactory system

- Biology, Computer SciencebioRxiv
- 2019

Olfactory learning is formulated as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit, which is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development.

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