A place cell system is developed that is able to provide robust localisation using simple sonar information only. The system uses egocentric view cells, based on the Adaptive Response Function Neuron, as sensory input to correct for path integration errors. The major advantage of this system is that place field centres are fixed prior to training, allowing downstream navigational systems to mak...

In the paper, a new flexible modification of neofuzzy neuron, neuro-fuzzy network based on these neurons and adaptive learning algorithms for the tuning of their all parameters are proposed. The algorithms are of interest because they ensure the on-line tuning of not only the synaptic weights and membership function parameters but also forms of these functions that provide improving approximati...

This feature of Applied Optics is devoted to papers on the optical implementation of neural-network models of computation. Papers are included on optoelectronic neuron array devices, optical interconnection techniques using holograms and spatial light modulators, optical associative memories, demonstrations of optoelectronic systems for learning, classification, and target recognition, and on t...

We present a model that can perform ICA-like learning by simple, local, biologically plausible rules. By combining synaptic learning with homeostatic regulation of neuron properties and adaptive lateral inhibition, the neural network can robustly learn Gabor-like receptive fields from natural images. With spatially localized inhibitory connections, a topographic map can be achieved. Additionall...

Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achi...

Neuromorphic systems that display synaptic conditioning based on biochemical signaling activity have recently been introduced in the form of artificial synapses are model devices to develop tissue-interfaced platforms. In this regard, biohybrid promise adaptive neuron-integrated functions. However, these suffer from both molecular cross-talk as biological neural circuits signal transmission typ...

A third generation wide-band-gap SiC semiconductor device is used in the arc welding power source, which has a higher inverter frequency and greatly improves dynamic characteristics of providing opportunities for control algorithm optimization. composite method source combining expert system single neuron Proportional-Integral-Derivative (PID) proposed this paper, aimed at fact that proportiona...

Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firing rates to drive learning, this new form of learning involves precise firing times. Hence, such algorithms can capture temporal spike correlations. We present circuits and methods to implement temporally-asymmetric Hebb...

The present paper describes several models of the central respiratory pattern generator (CRPG) developed employing experimental data and current hypotheses for respiratory rhythmogenesis. Each CRPG model includes a network of respiratory neuron types (e.g., early inspiratory; ramp inspiratory; late inspiratory; decrementing expiratory; postinspiratory; stage II expiratory; stage II constant fir...