Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.
Abstract
Sequential activity has been observed in multiple neuronal circuits across species,
neural structures, and behaviors. It has been hypothesized that sequences could arise
from learning processes. However, it is still unclear whether biologically plausible
synaptic plasticity rules can organize neuronal activity to form sequences whose statistics
match experimental observations. Here, we investigate temporally asymmetric Hebbian
rules in sparsely connected recurrent rate networks and develop a theory of the transient
sequential activity observed after learning. These rules transform a sequence of random
input patterns into synaptic weight updates. After learning, recalled sequential activity
is reflected in the transient correlation of network activity with each of the stored
input patterns. Using mean-field theory, we derive a low-dimensional description of
the network dynamics and compute the storage capacity of these networks. Multiple
temporal characteristics of the recalled sequential activity are consistent with experimental
observations. We find that the degree of sparseness of the recalled sequences can
be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain
robust decoding, but display highly labile dynamics, when synaptic connectivity is
continuously modified due to noise or storage of other patterns, similar to recent
observations in hippocampus and parietal cortex. Finally, we demonstrate that our
results also hold in recurrent networks of spiking neurons with separate excitatory
and inhibitory populations.
Type
Journal articleSubject
HippocampusParietal Lobe
Nerve Net
Neurons
Animals
Mice
Learning
Neuronal Plasticity
Models, Neurological
Computer Simulation
Neural Networks, Computer
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https://hdl.handle.net/10161/23343Published Version (Please cite this version)
10.1073/pnas.1918674117Publication Info
Gillett, Maxwell; Pereira, Ulises; & Brunel, Nicolas (2020). Characteristics of sequential activity in networks with temporally asymmetric Hebbian
learning. Proceedings of the National Academy of Sciences of the United States of America, 117(47). pp. 29948-29958. 10.1073/pnas.1918674117. Retrieved from https://hdl.handle.net/10161/23343.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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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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