Understanding the patterns of interconnections between neurons in complex networks is an enormous challenge using traditional physiological approaches. Here we combine the use of an information theoretic approach with intracellular recording to estab...
IEEE transactions on neural networks and learning systems
Jan 27, 2015
This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike...
Journal of computational neuroscience
Jan 21, 2015
In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks w...
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) descri...
IEEE transactions on neural networks and learning systems
Jan 6, 2015
It has been shown that brain-like self-repair can arise from the interactions between neurons and astrocytes where endocannabinoids are synthesized and released from active neurons. This retrograde messenger feeds back to local synapses directly and ...
IEEE transactions on neural networks and learning systems
Oct 22, 2014
This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different p...
BACKGROUND: The information obtained from signal recorded with extracellular electrodes is essential in many research fields with scientific and clinical applications. These signals are usually considered as a point process and a spike detection meth...
Periodic synchronization of activity among neuronal pools has been related to substantial neural processes and information throughput in the neocortical network. However, the mechanisms of generating such periodic synchronization among distributed po...
Neural networks : the official journal of the International Neural Network Society
Jun 5, 2014
We investigated the organization of a recurrent network under ongoing synaptic plasticity using a model of neural oscillators coupled by dynamic synapses. In this model, the coupling weights changed dynamically, depending on the timing between the os...
Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding me...