A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP

Humans perform remarkably well in many cognitive tasks including pattern recognition.
However, the neuronal mechanisms underlying this process are not well understood.
Nevertheless, artificial neural networks, inspired by brain circuits, have been designed
and used to tackle spatio-temporal pattern recognition tasks. In this paper, we present
a multi-neuronal spike pattern detection structure able to autonomously implement
online learning and recognition of parallel spike sequences (i.e., sequences of pulses
belonging to different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike latency,
which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity,
which allows the own regulation of synaptic weights. From the perspective of the
information representation, the structure allows mapping a spatio-temporal stimulus into
a multi-dimensional, temporal, feature space. In this space, the parameter coordinate
and the time at which a neuron fires represent one specific feature. In this sense, each
feature can be considered to span a single temporal axis. We applied our proposed
scheme to experimental data obtained from a motor-inhibitory cognitive task. The results
show that our method exhibits similar performance compared with other classification
methods, indicating the effectiveness of our approach. In addition, its simplicity and
low computational cost suggest a large scale implementation for real-time recognition
applications in several areas, such as brain-computer interface, personal biometrics
authentication, or early detection of diseases.