Spike-v: an Adaptive Mechanism for Speech-rate Independent Timing

An Adaptive Mechanism for Speech-Rate Independent Timing

Measures of spike train synchrony for data with multiple time scales

BACKGROUND Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. N...

A spiking network that learns to extract spike signatures from speech signals

Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynamic learning, provides a good test problem to study SNN functionality. We present a simple, novel, and efficient nonrecurrent SNN that learns to convert a speech signal into a spike train signature. The signature is dis...

Independent Component Analysis in Spiking Neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing depend...

An adaptive spike-timing-dependent plasticity rule

We examine the conditions under which spike-timing-dependent plasticity (STDP) normalizes post-synaptic 0ring rates. Our simulations show that the rate normalization property of STDP is fragile and small changes in the LTD=LTP ratio or pre-synaptic input rates can lead to high 0ring rates. We propose an adaptive scheme to dynamically control the LTP=LTD ratio. The biophysics of synapses lead us...