Time-warp-invariant neuronal processing (Gutig & Sompolinsky 2009)


" ... Here, we report that time-warp-invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons. We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task. Applying this general biophysical mechanism to the example of speech processing, we propose a neuronal network model for time-warp-invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task. ..."

Model Type: Connectionist Network

Model Concept(s): Pattern Recognition

Simulation Environment: Brian (web link to method); Python (web link to model)

Implementer(s): Brette R

References:

G├╝tig R, Sompolinsky H. (2009). Time-warp-invariant neuronal processing. PLoS biology. 7 [PubMed]


This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.