Fast spike pattern detection using the correlation integral

Abstract

Conventional approaches to detect patterns in neuronal firing are template based. As the pattern length increases, the number of trial patterns to be tested leads to strongly divergent computational costs. To remedy this problem, we propose a different statistical approach, based on the correlation integral. Applications of our method to model and neuronal data demonstrate its reliability, even in the presence of noise. Additionally, our investigation provides interesting insights into the nature of correlation-integral anomalies.

Pattern discrimination and firing behavior of cold receptor model aided with noise

“…The basic biophysics underlying the generation of action potentials (spikes) is somewhat well established, but the encoding mechanism is still unclear. To investigate the encoding meaning of spike timing and the temporal rhythm structures (that is, patterns) of spikes, the series expansion approximation method [1], the information distortion method [2] and other methods have also been used to quantify the information encoded in spike trains [3][4][5][6][7]. These works indicated that the recognition of temporal patterns of a spike train is essential for extracting information from neuronal responses.…”

Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron

“…The first family uses spike train metrics (for an overview, see Brown et al, 2004; Grün and Rotter, 2010). Some of these methods are based on arbitrary features extracted from the observed spike patterns such as rank order (Pan et al, 2009), firing rate (Kermany et al, 2010), or inter-spike intervals (ISI) (Abeles and Gerstein, 1988; Christen et al, 2004). These methods run the risk of preselecting features that are not relevant for biology.…”

Artificial Spiking Neurons and Analog-to-Digital-to-Analog Conversion

“…Potential applications of the ASNs and PCNNs are many, including image processing, associative memories, ultra-wide-band communication, and feature selection [7]- [13]. The ASNs and PCNNs have also been studied as simplified models of biological neurons in order to understand neural information processing function where spike-based coding plays important roles [14]- [16].…”

“…(i) The study of ASN-ADAC contributes to bridge between encoding mechanisms of spiking neurons [14]- [16] and ADC/DAC [17]- [20] via nonlinear dynamical system theories [21], [25]. It may be a trigger to develop a new signal processing framework.…”