Models and properties of power-law adaptation in neural systems

Patrick James Drew, L. F. Abbott

Research output: Contribution to journalArticle

99 Scopus citations

Abstract

Many biological systems exhibit complex temporal behavior that cannot be adequately characterized by a single time constant. This dynamics, observed from single channels up to the level of human psychophysics, is often better described by power-law rather than exponential dependences on time. We develop and study the properties of neural models with scale-invariant, power-law adaptation and contrast them with the more commonly studied exponential case. Responses of an adapting firing-rate model to constant, pulsed, and oscillating inputs in both the power-law and exponential cases are considered. We construct a spiking model with power-law adaptation based on a nested cascade of processes and show that it can be "programmed" to produce a wide range of time delays. Finally, within a network model, we use power-law adaptation to reproduce long-term features of the tilt aftereffect.

Original languageEnglish (US)
Pages (from-to)826-833
Number of pages8
JournalJournal of neurophysiology
Volume96
Issue number2
DOIs
StatePublished - Aug 7 2006

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All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Physiology

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