Stochastic oscillations near the “blue sky catastrophe” bifurcation in neuron model

2018 ◽  
Author(s):  
Lev Ryashko ◽  
Evdokia Slepukhina
Keyword(s):  
Neuron Model ◽  
Blue Sky Catastrophe ◽  
Stochastic Oscillations

Stochastic oscillations in a neuron model with two-dimensional map

2019 ◽  
Author(s):  
V. M. Nasyrova ◽  
L. B. Ryashko ◽  
I. N. Tsvetkov
Keyword(s):  
Neuron Model ◽  
Two Dimensional ◽  
Stochastic Oscillations

Noise-induced spiking-bursting transition in the neuron model with the blue sky catastrophe

2019 ◽  
Vol 99 (6) ◽  
Author(s):  
Irina Bashkirtseva ◽  
Lev Ryashko ◽  
Evdokia Slepukhina
Keyword(s):  
Neuron Model ◽  
Blue Sky Catastrophe

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 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
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