OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a...
Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed tha...
OBJECTIVE: We studied the fundamentals of muscle afferentation by building a Neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of...
OBJECTIVE: One goal of neuromorphic engineering is to create 'realistic' robotic systems that interact with the physical world by adopting neuromechanical principles from biology. Critical to this is the methodology to implement the spinal circuitry ...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 19, 2016
High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce...
Neural networks : the official journal of the International Neural Network Society
Nov 8, 2016
Training a Spiking Neural Network using SpikeProp and its derivatives faces stability issues. Surges, marked by a sudden rise in learning cost, are a common occurrence during the learning process. They disrupt the learning process and often destabili...
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the unde...
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combine...
In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitio...
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational ...