scholarly journals Memory and forgetting processes with the firing neuron model

2018 ◽  
Vol 77 (2) ◽  
pp. 221-233 ◽  
Author(s):  
D. Świetlik ◽  
J. Białowąs ◽  
A. Kusiak ◽  
D. Cichońska
Keyword(s):  
Neuron Model ◽  
Firing Neuron

scholarly journals Modeling the effect of temperature on membrane response of light stimulation in optogenetically-targeted neurons

2018 ◽  
Author(s):  
Helton Maia Peixoto ◽  
Rossana Moreno ◽  
Thiago Moulin ◽  
Richardson N N Leão
Keyword(s):  
Neuron Model ◽  
Gamma Oscillations ◽  
Firing Frequency ◽  
Surrounding Tissue ◽  
Effect Of Temperature ◽  
Spontaneous Firing ◽  
Neuronal Function ◽  
Light Stimulation ◽  
Direct Excitation ◽  
Firing Neuron

Optogenetics is revolutionizing neuroscience but an often neglected effect of light stimulation of the brain is the generation of heat. In extreme cases, light-generated heat kills neurons but mild temperature changes alter neuronal function. In this work, we investigated heat transfer in brain tissue for common optogenetic protocols using the finite element method. We then modeled channelrhodopsin-2 in a single- and a spontaneous-firing neuron to explore the effect of heat in light stimulated neurons. We found that, at commonly used intensities, laser radiation considerably increases the temperature in the surrounding tissue. This effect alters action potential size and shape and cause increase in spontaneous firing frequency in a neuron model. However, the shortening of activation time constants generated by heat in the single firing neuron model produce AP failures in response to light stimulation. We also found changes in the power spectrum density and a reduction in the time required for synchronization in an interneuron network model of gamma oscillations. Our findings indicate that light stimulation with intensities used in optogenetic experiments may affect neuronal function not only by direct excitation of light sensitive ion channels and/or pumps but also by generating heat. This approach serves as a guide to design optogenetic experiments that minimize the role of tissue heating in the experimental outcome.

scholarly journals Modeling the effect of temperature on membrane response of light stimulation in optogenetically-targeted neurons

2018 ◽  
Author(s):  
Helton Maia Peixoto ◽  
Rossana Moreno ◽  
Thiago Moulin ◽  
Richardson N N Leão
Keyword(s):  
Neuron Model ◽  
Gamma Oscillations ◽  
Firing Frequency ◽  
Surrounding Tissue ◽  
Effect Of Temperature ◽  
Spontaneous Firing ◽  
Neuronal Function ◽  
Light Stimulation ◽  
Direct Excitation ◽  
Firing Neuron

Optogenetics is revolutionizing neuroscience but an often neglected effect of light stimulation of the brain is the generation of heat. In extreme cases, light-generated heat kills neurons but mild temperature changes alter neuronal function. In this work, we investigated heat transfer in brain tissue for common optogenetic protocols using the finite element method. We then modeled channelrhodopsin-2 in a single- and a spontaneous-firing neuron to explore the effect of heat in light stimulated neurons. We found that, at commonly used intensities, laser radiation considerably increases the temperature in the surrounding tissue. This effect alters action potential size and shape and cause increase in spontaneous firing frequency in a neuron model. However, the shortening of activation time constants generated by heat in the single firing neuron model produce AP failures in response to light stimulation. We also found changes in the power spectrum density and a reduction in the time required for synchronization in an interneuron network model of gamma oscillations. Our findings indicate that light stimulation with intensities used in optogenetic experiments may affect neuronal function not only by direct excitation of light sensitive ion channels and/or pumps but also by generating heat. This approach serves as a guide to design optogenetic experiments that minimize the role of tissue heating in the experimental outcome.

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 Modeling temperature- and Cav3 subtype-dependent alterations in T-type calcium channel mediated burst firing

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fernando R. Fernandez ◽  
Mircea C. Iftinca ◽  
Gerald W. Zamponi ◽  
Ray W. Turner
Keyword(s):  
Calcium Channel ◽  
Neuronal Excitability ◽  
Neuronal Firing ◽  
Mammalian Brain ◽  
Peripheral Tissues ◽  
Window Current ◽  
Modeling Data ◽  
The Brain ◽  
Firing Neuron ◽  
Cav3 Channel

AbstractT-type calcium channels are important regulators of neuronal excitability. The mammalian brain expresses three T-type channel isoforms (Cav3.1, Cav3.2 and Cav3.3) with distinct biophysical properties that are critically regulated by temperature. Here, we test the effects of how temperature affects spike output in a reduced firing neuron model expressing specific Cav3 channel isoforms. The modeling data revealed only a minimal effect on baseline spontaneous firing near rest, but a dramatic increase in rebound burst discharge frequency for Cav3.1 compared to Cav3.2 or Cav3.3 due to differences in window current or activation/recovery time constants. The reduced response by Cav3.2 could optimize its activity where it is expressed in peripheral tissues more subject to temperature variations than Cav3.1 or Cav3.3 channels expressed prominently in the brain. These tests thus reveal that aspects of neuronal firing behavior are critically dependent on both temperature and T-type calcium channel subtype.

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.

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