Bayesian reconstruction of memories stored in neural networks from their connectivity
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Abstract
The advent of comprehensive synaptic wiring diagrams of large neural circuits
has created the field of connectomics and given rise to a number of open
research questions. One such question is whether it is possible to reconstruct
the information stored in a recurrent network of neurons, given its synaptic
connectivity matrix. Here, we address this question by determining when solving
such an inference problem is theoretically possible in specific attractor
network models and by providing a practical algorithm to do so. The algorithm
builds on ideas from statistical physics to perform approximate Bayesian
inference and is amenable to exact analysis. We study its performance on three
different models and explore the limitations of reconstructing stored patterns
from synaptic connectivity.
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Journal articlePermalink
https://hdl.handle.net/10161/23341Collections
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Show full item recordScholars@Duke
Nicolas Brunel
Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn
information from their inputs. Our current work focuses on the mechanisms of learning
and memory, from the synapse to the network level, in collaboration with various experimental
groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity
of a network that maximizes storage capacity reproducestwo key experimentally observed
features: low connection proba

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