Year: 2007
Type: article
Abstract: Dendrites are not static structures, new synaptic connec-tions are established and old ones disappear. Moreover, it is now known that plasticity can vary with distance from the soma [1]. Consequently it is of great interest to com-bine learning algorithms with spatially extended neuron models. In particular this may shed further light on the computational advantages of plastic dendrites, say for direction selectivity or coincidence detection. Direction selective neurons fire for one spatio-temporal input sequence on their dendritic tree but stay silent if the tem-poral order is reversed [2], whilst "coincidence-detectors" such as those in the auditory brainstem are known to make use of dendrites to detect temporal differences in sound arrival times between ears to an astounding accu-racy [3]. Here we develop one such combination of learn-ing and dendritic dynamics by extending the "Spike-Diffuse-Spike " [4] framework of an active dendritic tree to incorporate both artificial (tempotron style [5]) and bio-logical learning rules (STDP style [2]).
Source: BMC Neuroscience
Institutions: University of Nottingham, University of Warwick
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W2158627045