A neuron model with pre-synaptic deletion and post-synaptic accumulation decay, and threshold behaviour

1975 ◽  
Vol 19 (2) ◽  
pp. 69-74 ◽  
Author(s):  
S. K. Srinivasan ◽  
G. Sampath
Keyword(s):  
Neuron Model ◽  
Threshold Behaviour

Low Capacity Implementation of a Pulse-Type Chaotic Neuron Model

2016 ◽  
Vol 136 (10) ◽  
pp. 1424-1430 ◽  
Author(s):  
Yoshiki Sasaki ◽  
Katsutoshi Saeki ◽  
Yoshifumi Sekine
Keyword(s):  
Neuron Model ◽  
Pulse Type ◽  
Chaotic Neuron

scholarly journals Optofluidic Platform Based on Liquid Crystals in X-Cut Lithium Niobate: Thresholdless All-Optical Response

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 908
Author(s):  
Fabrizio Ciciulla ◽  
Annamaria Zaltron ◽  
Riccardo Zamboni ◽  
Cinzia Sada ◽  
Francesco Simoni ◽  
...  
Keyword(s):  
Liquid Crystal ◽  
Liquid Crystals ◽  
Light Intensity ◽  
Lithium Niobate ◽  
Photovoltaic Effect ◽  
Optical Control ◽  
Crystal Layer ◽  
Liquid Crystal Layer ◽  
Threshold Behaviour ◽  
All Optical

In this study, we present a new configuration of the recently reported optofluidic platform exploiting liquid crystals reorientation in lithium niobate channels. In order to avoid the threshold behaviour observed in the optical control of the device, we propose microchannels realized in a x-cut crystal closed by a z-cut crystal on the top. In this way, the light-induced photovoltaic field is not uniform inside the liquid crystal layer and therefore the conditions for a thresholdless reorientation are realized. We performed simulations of the photovoltaic effect based on the well assessed model for Lithium Niobate, showing that not uniform orientation and value of the field should be expected inside the microchannel. In agreement with the re-orientational properties of nematic liquid crystals, experimental data confirm the expected thresholdless behaviour. The observed liquid crystal response exhibits two different regimes and the response time shows an unusual dependence on light intensity, both features indicating the presence of additional photo-induced fields appearing above a light intensity of 107 W/m2.

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

scholarly journals Digital Implementation of the Two-Compartmental Pinsky–Rinzel Pyramidal Neuron Model

2018 ◽  
Vol 12 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Elahe Rahimian ◽  
Soheil Zabihi ◽  
Mahmood Amiri ◽  
Bernabe Linares-Barranco
Keyword(s):  
Pyramidal Neuron ◽  
Neuron Model ◽  
Digital Implementation

COMPLEX BIFURCATION STRUCTURES IN THE HINDMARSH–ROSE NEURON MODEL

2007 ◽  
Vol 17 (09) ◽  
pp. 3071-3083 ◽  
Author(s):  
J. M. GONZÀLEZ-MIRANDA
Keyword(s):  
Phase Diagram ◽  
Bifurcation Diagram ◽  
Parameter Space ◽  
Neuron Model ◽  
Two Dimensional ◽  
Dimensional Parameter ◽  
Rapid Responses ◽  
Bifurcation Structures

The results of a study of the bifurcation diagram of the Hindmarsh–Rose neuron model in a two-dimensional parameter space are reported. This diagram shows the existence and extent of complex bifurcation structures that might be useful to understand the mechanisms used by the neurons to encode information and give rapid responses to stimulus. Moreover, the information contained in this phase diagram provides a background to develop our understanding of the dynamics of interacting neurons.

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