SPIKING ACTIVITY OF LIFH NEURON MODEL WITH VARIABLE INPUT STIMULUS

2021 ◽  
Vol 10 (1) ◽  
pp. 471-481
Author(s):  
V.D.S. Baghela ◽  
S.K. Bharti ◽  
P.K. Bharti
Keyword(s):  
Information Processing ◽  
Neuron Model ◽  
Constant Input ◽  
Input Stimulus ◽  
Stochastic Input ◽  
Spiking Activity

Neuronal information processing occurs in term of spikes. A neuron can emits various kinds of spiking patterns based on the applied input stimulus. In this article, we study the spiking pattern of LIFH neuron model in the presence of four different kinds of applied input stimulus, namely, constant input stimulus, uniformly distributed input stimulus, Gaussian distributed input stimulus and stochastic input stimulus. Here, we notice the tonic and semi-tonic spiking pattern for Gaussian distributed input stimulus and stochastic input stimulus.

Oscillatory Activity in Electrosensory Neurons Increases with the Spatial Correlation of the Stochastic Input Stimulus

2004 ◽  
Vol 93 (4) ◽  
Author(s):  
Brent Doiron ◽  
Benjamin Lindner ◽  
André Longtin ◽  
Leonard Maler ◽  
Joseph Bastian
Keyword(s):  
Spatial Correlation ◽  
Oscillatory Activity ◽  
Input Stimulus ◽  
Stochastic Input

Single-Electron Devices and Circuits Utilizing Stochastic Operation for Intelligent Information Processing

2011 ◽  
pp. 75-99
Author(s):  
Takashi Morie
Keyword(s):  
Information Processing ◽  
Large Scale ◽  
Neuron Model ◽  
Single Electron ◽  
Spiking Neuron ◽  
Spiking Neuron Model ◽  
Single Electron Devices ◽  
Electron Devices ◽  
Intelligent Information ◽  
Circuit Technology

The single-electron circuit technology should aim at developing information processing systems using the intrinsic properties of single-electron devices. The operation principles of single-electron devices are completely different from that of conventional CMOS devices, but both devices should co-exist in the information processing systems. In this paper, according to a scenario for achieving large-scale integrated systems of single-electron devices, some single-electron devices and circuits utilizing stochastic operation for associative processing and a spiking neuron model are described.

Current Stimuli That Provide Membrane Voltage Tracking in a Six Dimensional Neuron Model

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Melinda E. Koelling ◽  
Damon A. Miller ◽  
Michael Ellinger ◽  
Frank L. Severance ◽  
John Stahl
Keyword(s):  
Optimal Control ◽  
Neuron Model ◽  
Optimization Techniques ◽  
Reference Voltage ◽  
Membrane Voltage ◽  
Neuron Models ◽  
Input Stimulus ◽  
Optimal Input ◽  
Stimulus Current ◽  
Order Neuron

Optimization techniques have been applied to neuron models for a variety of purposes, including control of neuron firing rates and minimizing input stimulus current magnitudes. Optimal control is used to minimize a quantity of interest; often, the time or energy needed to complete an objective. Rather than attempting to control or modify neuron dynamics, this paper demonstrates that optimal control can be used to obtain an optimal input stimulus current i*(t) which causes a six dimensional Hodgkin–Huxley type neuron model to approximate a specified reference membrane voltage. The reference voltages considered in this paper consist of one or more action potentials as evoked by an input current i(t). In the described method, the user prescribes a balance of low squared integral of input stimulus current (input stimulus “energy”) and accurate tracking of the original reference voltage. In a previous work, the authors applied this approach to a reduced order neuron model. This paper demonstrates the applicability of this technique to biologically plausible higher dimensional conductance based neuron models. For each investigated neuron response, the method discovered optimal input stimuli current i*(t) having a lower energy than the original i(t), while still providing accurate tracking of the reference voltage.

scholarly journals Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations

2019 ◽  
Author(s):  
Joaquin J. Torres ◽  
Fabiano Baroni ◽  
Roberto Latorre ◽  
Pablo Varona
Keyword(s):  
Information Processing ◽  
Neuron Model ◽  
Temporal Discrimination ◽  
Temporal Structure ◽  
Synaptic Strength ◽  
Input Output ◽  
Subthreshold Oscillations ◽  
Dynamic Synapses ◽  
Synaptic Dynamics ◽  
Neuronal Computation

AbstractThe interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthreshold oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. We employ the Generalized Integrate-and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input-output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength, suggesting that they likely contribute to neuronal computation in biological circuits. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models.Author summaryNeuronal subthreshold oscillations underlie key aspects of information processing in single neuron and network dynamics. Dynamic synapses provide a channel-specific temporal modulation of input information. We combine a neuron model that displays subthreshold oscillations and a dynamic synapse to analytically assess their interplay in processing trains of spike-mediated synaptic currents. Our results show that the co-action of intrinsic and synaptic dynamics builds nontrivial input-output relationships, which are resistant to noise and to changes in synaptic strength. The discrimination of a precise temporal structure of the input signal is shaped as a function of the joint interaction of intrinsic oscillations and synaptic dynamics. This interaction can result in channel-specific recognition of precise temporal patterns, hence greatly expanding the flexibility and complexity in information processing achievable by individual neurons with respect to temporal discrimination mechanisms based on intrinsic neuronal dynamics alone.

scholarly journals Information Processing in GLIF Neuron Model with Noisy Conductance

2019 ◽  
Vol 03 (02) ◽  
pp. 102-113
Author(s):  
Vishwadeepak Singh Baghela ◽  
Sunil Kumar Bharti ◽  
Saket Kumar Choudhary
Keyword(s):  
Information Processing ◽  
Neuron Model
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