Conductance-based dendrites perform reliability-weighted opinion pooling
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Date
2020-03
Publication Type
Conference Paper
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yes
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Abstract
Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative model we show that the dynamics of multi-compartment neurons with conductance-based dendrites naturally implement the required probabilistic computations. The associated error-driven plasticity rule allows neurons to learn the relative reliability of different pathways from data samples, approximating Bayes-optimal observers in multisensory integration tasks. Additionally, the model provides a functional interpretation of neural recordings from multisensory integration experiments and makes specific predictions for membrane potential and conductance dynamics of individual neurons. © 2020 ACM.
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published
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Book title
Proceedings of the Neur-Inspired Computational Elements Workshop (NICE '20)
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Volume
Pages / Article No.
11
Publisher
Association for Computing Machinery
Event
8th Annual Neuro-Inspired Computational Elements Workshop (NICE 2020)
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Date created
Subject
Bayesian cue combination; Conductance-based coupling; Multisensory integration; Neural networks; Synaptic plasticity
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Notes
Due to the Coronavirus (COVID-19) the conference was rescheduled from March 17-20, 2020 to March 16-19, 2021.