Abstract
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 in 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 out 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.
Highlights
In recent years there has been an increasing interest in applying artificial neural networks to solve pattern recognition tasks
In order to show how the developed Multi-Neuronal spike-Sequence Detector (MNSD) tool can be used to tackle pattern recognition problems, we test the structure on two different datasets, dataset 1 and dataset 2
In this study we have presented a multi-neuronal spike-pattern detection structure, MNSD, which combines the Leaky Integrate-andFire with Latency (LIFL) neuron model and heterosynaptic Spike-Timing-Dependent Plasticity (STDP), to perform online learning and recognition of multi-neuronal spike patterns
Summary
In recent years there has been an increasing interest in applying artificial neural networks to solve pattern recognition tasks. It remains challenging to design more realistic spiking neuronal networks (SNNs) which use biologically plausible mechanisms to achieve these tasks (Diehl and Cook, 2015). The recognition of stimuli is possible by detecting spike patterns during the processing of peripheral inputs. Precise spike timings of neural activity have been observed in many brain regions, including the retina, the lateral geniculate nucleus, and the visual cortex, suggesting that the temporal structure of spike trains serves as an important component of the neuronal representation of the stimuli (Gütig and Sompolinsky, 2006; Zhang et al, 2016). Some investigations are oriented to the study of the spike activity of single neurons, many others consider the timing of spikes across a population of afferent neurons (Gautrais and Thorpe, 1998; Stark et al, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
