Probabilistic synapses

Add to your list(s) Download to your calendar using vCal

• Laurence Aitchison
• Friday 12 February 2016, 12:00-13:00
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions).

If you have a question about this talk, please contact Dr Máté Lengyel.

Organisms face a hard problem: based on noisy sensory input, they must set a large number of synaptic weights. However, they do not receive enough information in their lifetime to learn the correct, or optimal weights (i.e. the weights that ensure the circuit, system, and ultimately organism function as effectively as possible). Instead, the best they could possibly do is compute a probability distribution over the optimal weights. Based on this observation, we hypothesize that synapses represent probability distribution over weights — in contrast to the widely held belief that they represent point estimates. From this hypothesis, we derive learning rules for supervised, reinforcement and unsupervised learning. This introduces a new feature: the more uncertain the synapse is about its weight, the more plastic it is. This makes intuitive sense: if the uncertainty about a weight is large, new data should strongly influence its value, while if the uncertainty is small, little learning is needed. This hypothesis makes two predictions about how learning rates should vary across synapses and across time. We also introduce a second hypothesis, which is that the more uncertainty there is about a synaptic weight, the more variable it is. More concretely, the PSP amplitude at a given time is a sample from the probability distribution describing the synapse’s uncertainty. This hypothesis makes several predictions, and we present data for one: that variability should increase as the presynaptic firing rate falls.

This talk is part of the Computational Neuroscience series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Biology
• Biology
• CBL important
• CamBridgeSens
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Neuroscience Seminars
• Cambridge University Engineering Department, CBL, BE-438 (http://learning.eng.cam.ac.uk/Public/Directions)
• Cambridge talks
• Chris Davis' list
• Computational Neuroscience
• Computational and Biological Learning Seminar Series
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Life Science
• Life Science Interface Seminars
• Life Sciences
• Life Sciences
• ME Seminar
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Quantum Matter Journal Club
• Required lists for MLG
• Stem Cells & Regenerative Medicine
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• dh539
• kt532's list
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• rp587
• se456's list
• yk373's list

Note that ex-directory lists are not shown.