International Journal of Neural Systems
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AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs.
We present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementatio...
Modeling and optimization of a pharmaceutical formulation system using radial basis function network.
A Pharmaceutical formulation is composed of several formulation factors and process variables. Quantitative model based pharmaceutical formulation involves establishing mathematical relations between the formulation variables and the resulting responses, and optimizing the formulation conditions. In...
An improvement of extreme learning machine for compact single-hidden-layer feedforward neural networks.
Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output w...
A new method for the re-implementation of threshold logic functions with cellular neural networks.
Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example....
A delayed neural network method for solving convex optimization problems.
In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming pr...
Fast digital simulation of spiking neural networks and neuromorphic integration with SPIKELAB.
We present a simulation environment called SPIKELAB which incorporates a simulator that is able to simulate large networks of spiking neurons using a distributed event driven simulation. Contrary to a time driven simulation, which is usually used to simulate spiking neural networks, our simulation n...
Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.
We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to...
A new linear muscle fiber model for neural control of saccades.
A comprehensive model for the control of horizontal saccades is presented using a new muscle fiber model for the lateral and medial rectus muscles. The importance of this model is that each muscle fiber has a separate neural input. This model is robust and accounts for the neural activity for both l...
Efficacy of a new charge-balanced biphasic electrical stimulus in the isolated sciatic nerve and the hippocampal slice.
We compared the efficacy of three types of charge-balanced biphasic pulses (CBBPs, nominal duration 100 μs) in isolated sciatic nerves and in in vitro hippocampal brain slices of the rat. Using these two models, we tested the efficacy of several stimulus shapes exclusively on axons (in the sciatic n...
Reactive control of epileptiform discharges in realistic computational neuronal models with bistability.
We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of...