Models and properties of power-law adaptation in neural systems.

@article{Drew2006, 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.}, address = {Neurobiology Section 0357, Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. pjdrew@biomail.ucsd.edu}, author = {Drew, P. J. and Abbott, L. F. }, citeulike-article-id = {952053}, doi = {http://dx.doi.org/10.1152/jn.00134.2006}, issn = {0022-3077}, journal = {J Neurophysiol}, keywords = {adaptation, power-law}, month = {August}, number = {2}, pages = {826--833}, posted-at = {2008-07-23 18:03:57}, priority = {2}, title = {Models and properties of power-law adaptation in neural systems.}, url = {http://dx.doi.org/10.1152/jn.00134.2006}, volume = {96}, year = {2006} }

TY - JOUR ID - Drew2006 L3 - citeulike-article-id:952053 TI - Models and properties of power-law adaptation in neural systems. JF - J Neurophysiol VL - 96 IS - 2 SP - 826 EP - 833 AD - Neurobiology Section 0357, Division of Biology, University of California at San Diego, La Jolla, CA 92093, USA. pjdrew@biomail.ucsd.edu SN - 0022-3077 N2 - 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. KW - adaptation KW - power-law AU - Drew, PJ AU - Abbott, LF PY - 2006/08// UR - http://dx.doi.org/10.1152/jn.00134.2006 ER -