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]