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Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.

Publication ,  Journal Article
Gillett, M; Pereira, U; Brunel, N
Published in: Proc Natl Acad Sci U S A
November 24, 2020

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.

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Published In

Proc Natl Acad Sci U S A

DOI

EISSN

1091-6490

Publication Date

November 24, 2020

Volume

117

Issue

47

Start / End Page

29948 / 29958

Location

United States

Related Subject Headings

  • Parietal Lobe
  • Neurons
  • Neuronal Plasticity
  • Neural Networks, Computer
  • Nerve Net
  • Models, Neurological
  • Mice
  • Learning
  • Hippocampus
  • Computer Simulation
 

Citation

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Gillett, M., Pereira, U., & Brunel, N. (2020). Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. Proc Natl Acad Sci U S A, 117(47), 29948–29958. https://doi.org/10.1073/pnas.1918674117
Gillett, Maxwell, Ulises Pereira, and Nicolas Brunel. “Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.Proc Natl Acad Sci U S A 117, no. 47 (November 24, 2020): 29948–58. https://doi.org/10.1073/pnas.1918674117.
Gillett M, Pereira U, Brunel N. Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29948–58.
Gillett, Maxwell, et al. “Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.Proc Natl Acad Sci U S A, vol. 117, no. 47, Nov. 2020, pp. 29948–58. Pubmed, doi:10.1073/pnas.1918674117.
Gillett M, Pereira U, Brunel N. Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29948–29958.
Journal cover image

Published In

Proc Natl Acad Sci U S A

DOI

EISSN

1091-6490

Publication Date

November 24, 2020

Volume

117

Issue

47

Start / End Page

29948 / 29958

Location

United States

Related Subject Headings

  • Parietal Lobe
  • Neurons
  • Neuronal Plasticity
  • Neural Networks, Computer
  • Nerve Net
  • Models, Neurological
  • Mice
  • Learning
  • Hippocampus
  • Computer Simulation