Models and properties of power-law adaptation in neural systems.
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.
Drew
Patrick J
PJ
Neurobiology Section 0357, Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. pjdrew@biomail.ucsd.edu
Abbott
L F
LF
D000465:Algorithms; D004594:Electrophysiology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology