Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks: A Fokker-Planck approach

Liqiang Zhu, Ying-Cheng Lai, Frank C. Hoppensteadt, Jiping He

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

It is believed that both Hebbian and homeostatic mechanisms are essential in neural learning. While Hebbian plasticity selectively modifies synaptic connectivity according to activity experienced, homeostatic plasticity constrains this change so that neural activity is always within reasonable physiological limits. Recent experiments reveal spike timing-dependent plasticity (STDP) as a new type of Hebbian learning with high time precision and heterosynaptic plasticity (HSP) as a new homeostatic mechanism acting directly on synapses. Here, we study the effect of STDP and HSP on randomly connected neural networks. Despite the reported successes of STDP to account for neural activities at the single-cell level, we find that, surprisingly, at the network level, networks trained using STDP alone cannot seem to generate realistic neural activities. For instance, STDP would stipulate that past sensory experience be maintained forever if it is no longer activated. To overcome this difficulty, motivated by the fact that HSP can induce strong competition between sensory experiences, we propose a biophysically plausible learning rule by combining STDP and HSP. Based on the Fokker-Planck theory and extensive numerical computations, we demonstrate that HSP and STDP operated on different time scales can complement each other, resulting in more realistic network activities. Our finding may provide fresh insight into the learning mechanism of the brain.

Original languageEnglish (US)
Article number023105
JournalChaos
Volume16
Issue number2
DOIs
StatePublished - 2006
Externally publishedYes

Fingerprint

Fokker-Planck
Spike
spikes
plastic properties
Plasticity
Timing
time measurement
Neural Networks
Neural networks
Dependent
learning
Hebbian Learning
synapses
Rule Learning
Synapse
complement
Numerical Computation
brain
Brain

ASJC Scopus subject areas

  • Applied Mathematics
  • Physics and Astronomy(all)
  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks : A Fokker-Planck approach. / Zhu, Liqiang; Lai, Ying-Cheng; Hoppensteadt, Frank C.; He, Jiping.

In: Chaos, Vol. 16, No. 2, 023105, 2006.

Research output: Contribution to journalArticle

@article{4b03def773f5498f8c4299eca477f306,
title = "Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks: A Fokker-Planck approach",
abstract = "It is believed that both Hebbian and homeostatic mechanisms are essential in neural learning. While Hebbian plasticity selectively modifies synaptic connectivity according to activity experienced, homeostatic plasticity constrains this change so that neural activity is always within reasonable physiological limits. Recent experiments reveal spike timing-dependent plasticity (STDP) as a new type of Hebbian learning with high time precision and heterosynaptic plasticity (HSP) as a new homeostatic mechanism acting directly on synapses. Here, we study the effect of STDP and HSP on randomly connected neural networks. Despite the reported successes of STDP to account for neural activities at the single-cell level, we find that, surprisingly, at the network level, networks trained using STDP alone cannot seem to generate realistic neural activities. For instance, STDP would stipulate that past sensory experience be maintained forever if it is no longer activated. To overcome this difficulty, motivated by the fact that HSP can induce strong competition between sensory experiences, we propose a biophysically plausible learning rule by combining STDP and HSP. Based on the Fokker-Planck theory and extensive numerical computations, we demonstrate that HSP and STDP operated on different time scales can complement each other, resulting in more realistic network activities. Our finding may provide fresh insight into the learning mechanism of the brain.",
author = "Liqiang Zhu and Ying-Cheng Lai and Hoppensteadt, {Frank C.} and Jiping He",
year = "2006",
doi = "10.1063/1.2189969",
language = "English (US)",
volume = "16",
journal = "Chaos (Woodbury, N.Y.)",
issn = "1054-1500",
publisher = "American Institute of Physics Publising LLC",
number = "2",

}

TY - JOUR

T1 - Cooperation of spike timing-dependent and heterosynaptic plasticities in neural networks

T2 - A Fokker-Planck approach

AU - Zhu, Liqiang

AU - Lai, Ying-Cheng

AU - Hoppensteadt, Frank C.

AU - He, Jiping

PY - 2006

Y1 - 2006

N2 - It is believed that both Hebbian and homeostatic mechanisms are essential in neural learning. While Hebbian plasticity selectively modifies synaptic connectivity according to activity experienced, homeostatic plasticity constrains this change so that neural activity is always within reasonable physiological limits. Recent experiments reveal spike timing-dependent plasticity (STDP) as a new type of Hebbian learning with high time precision and heterosynaptic plasticity (HSP) as a new homeostatic mechanism acting directly on synapses. Here, we study the effect of STDP and HSP on randomly connected neural networks. Despite the reported successes of STDP to account for neural activities at the single-cell level, we find that, surprisingly, at the network level, networks trained using STDP alone cannot seem to generate realistic neural activities. For instance, STDP would stipulate that past sensory experience be maintained forever if it is no longer activated. To overcome this difficulty, motivated by the fact that HSP can induce strong competition between sensory experiences, we propose a biophysically plausible learning rule by combining STDP and HSP. Based on the Fokker-Planck theory and extensive numerical computations, we demonstrate that HSP and STDP operated on different time scales can complement each other, resulting in more realistic network activities. Our finding may provide fresh insight into the learning mechanism of the brain.

AB - It is believed that both Hebbian and homeostatic mechanisms are essential in neural learning. While Hebbian plasticity selectively modifies synaptic connectivity according to activity experienced, homeostatic plasticity constrains this change so that neural activity is always within reasonable physiological limits. Recent experiments reveal spike timing-dependent plasticity (STDP) as a new type of Hebbian learning with high time precision and heterosynaptic plasticity (HSP) as a new homeostatic mechanism acting directly on synapses. Here, we study the effect of STDP and HSP on randomly connected neural networks. Despite the reported successes of STDP to account for neural activities at the single-cell level, we find that, surprisingly, at the network level, networks trained using STDP alone cannot seem to generate realistic neural activities. For instance, STDP would stipulate that past sensory experience be maintained forever if it is no longer activated. To overcome this difficulty, motivated by the fact that HSP can induce strong competition between sensory experiences, we propose a biophysically plausible learning rule by combining STDP and HSP. Based on the Fokker-Planck theory and extensive numerical computations, we demonstrate that HSP and STDP operated on different time scales can complement each other, resulting in more realistic network activities. Our finding may provide fresh insight into the learning mechanism of the brain.

UR - http://www.scopus.com/inward/record.url?scp=33745683431&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745683431&partnerID=8YFLogxK

U2 - 10.1063/1.2189969

DO - 10.1063/1.2189969

M3 - Article

C2 - 16822008

AN - SCOPUS:33745683431

VL - 16

JO - Chaos (Woodbury, N.Y.)

JF - Chaos (Woodbury, N.Y.)

SN - 1054-1500

IS - 2

M1 - 023105

ER -