We have proposed a generalized Langevin-type rate-code model subjected to multiplicative noise, in order to study stationary and dynamical properties of an ensemble containing a finite number N of neurons. Calculations using the Fokker-Planck equation have shown that, owing to the multiplicative noise, our rate model yields various kinds of stationary non-Gaussian distributions such as Gamma , ...

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...

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 [...]

apoptosis plays an important role in many pathological processes of the central nervous system. the neuroprotective effect of periodic fasting (pf) in contrast to severe fasting or starvation has been suggested. however, these beneficial effects seem to depend on the type and duration of the used feeding protocol. this study was designed to evaluate the effects of different pf protocols on the ...

In Radial Basis Neural Networks (RBNN), the activation of each neuron depends on the Euclidean distance between a pattern and the neuron center. Such a symmetrical activation assumes that all attributes are equally relevant, which might not be true. Non-symmetrical distances like Mahalanobis can be used. However, this distance is computed directly from the data covariance matrix and therefore t...

A complex interplay of single-neuron properties and the recurrent network structure shapes activity cortical neurons. The statistics differ in general from respective population statistics, including spectra and, correspondingly, autocorrelation times. We develop a theory for self-consistent second-order block-structured sparse random networks spiking In particular, predicts neuron-level times,...

In this paper, we observe some important aspects of Hebbian and error-correction learning rules for complex-valued neurons. These learning rules, which were previously considered for the multi-valued neuron whose inputs and output are located on the unit circle, are generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers. The Hebbian learning rule is also c...

Introduction: The aim of this study was evaluate the ability of notochord to induce neural induction and/or differentiation of mouse embryonic stem cell to neuron and motor neuron, respectively. Methods: In order to produce embryoid bodies, ES cells line Royan B1 were grown in suspension in the absence of LIF for 4 days. EBs were divided into 4 groups. EBs in group 1 & 2 were further cultur...

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNNs is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously ...

We propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates of neuron activity. Our input data consist of spike train measurements of a set of neurons of interest, a candidate list of the known stimuli that govern neuron activity, and the corresponding state of the animal throughout the experiment performed. Using a generalized linear mode...