For the paper:

Gutig R, Sompolinsky H (2009) Time-warp-invariant neuronal
processing. PLoS Biol 7:e1000141

Abstract:

Fluctuations in the temporal durations of sensory signals constitute a
major source of variability within natural stimulus ensembles. The
neuronal mechanisms through which sensory systems can stabilize
perception against such fluctuations are largely unknown. An
intriguing instantiation of such robustness occurs in human speech
perception, which relies critically on temporal acoustic cues that are
embedded in signals with highly variable duration. Across different
instances of natural speech, auditory cues can undergo temporal
warping that ranges from 2-fold compression to 2-fold dilation without
significant perceptual impairment. 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. Our results demonstrate the important
functional role of synaptic conductances in spike-based neuronal
information processing and learning. The biophysics of temporal
integration at neuronal membranes can endow sensory pathways with
powerful time-warp-invariant computational capabilities.

Brian simulator models for the basic model are available at this web
page:

http://briansimulator.org/docs/examples-frompapers_Guetig_Sompolinsky_2009.html

The simulation reproduces Fig. 2:



and Fig. 3C (but not in a speech recognition context):



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