Although variability is a ubiquitous characteristic of the nervous system, under appropriate conditions neurons can generate precisely timed action potentials. Thus considerable attention has been given to the study of a neuron’s output in relation to its stimulus. In this study, we consider an increasingly popular spiking neuron model, the adaptive exponential integrateand-fire neuron. For ana...

Introduction: The induction of human umbilical cord-derived mesenchymal stem cells (HUC-MSCs) toward dopaminergic neurons is a major challenge in tissue engineering and experimental and clinical treatments of various neurodegenerative diseases, including Parkinson disease. This study aims to differentiate HUC-MSCs into dopaminergic neuron-like cells. Methods: Following the isolation and charac...

Sensory systems extract behaviorally relevant information from a continuous stream of complex high-dimensional input signals. Understanding the detailed dynamics and precise neural code, even of a single neuron, is therefore a non-trivial task. Automated closed-loop approaches that integrate data analysis in the experimental design ease the investigation of sensory systems in three directions: ...

The aim of this paper is to study an Information Theory based learning theory for neural units endowed with adaptive activation functions. The learning theory has the target to force the neuron to approximate the input–output transference that makes it flat (uniform) the probability density function of its output or, equivalently, that maximizes the entropy of the neuron response. Then, a netwo...

The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise. In this paper we present a new adaptive scene-based non-uniformity correction (NUC) technique. The method simultaneously estimates detector’s parameters and performs the non-uniformity compensation using a neural approach and a Kalman estimator in a frame by frame recur...

This paper examines a class of neuron based learning systems for dynamic control that rely on adaptive range coding of sensor inputs. Sensors are assumed to provide binary coded range vectors that coarsely describe the system state. These vectors are input to neuron-like processing elements. Output decisions generated by these "neurons" in turn affect the system state, subsequently producing ne...

Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require long training time and large numbers of neurons to deal with complex problems. To overcome these drawbacks, a generalised neuron (GN) has been developed that requires much sma...

The human brain has hundreds of billions neurons and at least 7 million dendrites have been hypothesized to exist for each neuron, with over 100 trillion neuron–neuron, neuron–muscle, neuron–endocrine cell synapses [...]

This paper presents the development and implementation of a hybrid controller to automatically levitate and precisely regulate the position of a suspended rotor in Active Magnetic Bearing system. The inherent nonlinearity and open-loop instability of AMBs present challenges for design a controller and the problem can be formulated as a dynamic, multivariable optimization problem. A single neuro...