scholarly journals Analog implementation of a Hodgkin–Huxley model neuron

2020 ◽  
Vol 88 (11) ◽  
pp. 918-923
Author(s):  
George H. Rutherford ◽  
Zach D. Mobille ◽  
Jordan Brandt-Trainer ◽  
Rosangela Follmann ◽  
Epaminondas Rosa
Keyword(s):  
Model Neuron ◽  
Analog Implementation

The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

2006 ◽  
Vol 18 (3) ◽  
pp. 660-682 ◽  
Author(s):  
Melchi M. Michel ◽  
Robert A. Jacobs
Keyword(s):  
Model Neuron ◽  
Joint Probability ◽  
High Order ◽  
Neural Population ◽  
Neural Responses ◽  
Significant Information ◽  
Statistical Correlations ◽  
Neural Populations ◽  
Simulation Results ◽  
Dependence Tree

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.

Nonlinear Behavior of Sinusoidally Forced Pyloric Pacemaker Neurons

2001 ◽  
Vol 85 (4) ◽  
pp. 1623-1638 ◽  
Author(s):  
Attila Szűcs ◽  
Robert C. Elson ◽  
Michail I. Rabinovich ◽  
Henry D. I. Abarbanel ◽  
Allen I. Selverston
Keyword(s):  
Complex Dynamics ◽  
Cooperative Behavior ◽  
Model Neuron ◽  
Nonlinear Behavior ◽  
Periodic Forcing ◽  
Pacemaker Neurons ◽  
Strongly Coupled ◽  
Electronic Model ◽  
Group 2 ◽  
Low Dimensional

Periodic current forcing was used to investigate the intrinsic dynamics of a small group of electrically coupled neurons in the pyloric central pattern generator (CPG) of the lobster. This group contains three neurons, namely the two pyloric dilator (PD) motoneurons and the anterior burster (AB) interneuron. Intracellular current injection, using sinusoidal waveforms of varying amplitude and frequency, was applied in three configurations of the pacemaker neurons: 1) the complete pacemaker group, 2) the two PDs without the AB, and 3) the AB neuron isolated from the PDs. Depending on the frequency and amplitude of the injected current, the intact pacemaker group exhibited a wide variety of nonlinear behaviors, including synchronization to the forcing, quasiperiodicity, and complex dynamics. In contrast, a single, broad 1:1 entrainment zone characterized the response of the PD neurons when isolated from the main pacemaker neuron AB. The isolated AB responded to periodic forcing in a manner similar to the complete pacemaker group, but with wider zones of synchronization. We have built an analog electronic circuit as an implementation of a modified Hindmarsh-Rose model for simulating the membrane potential activity of pyloric neurons. We subjected this electronic model neuron to the same periodic forcing as used in the biological experiments. This four-dimensional electronic model neuron reproduced the autonomous oscillatory firing patterns of biological pyloric pacemaker neurons, and it expressed the same stationary nonlinear responses to periodic forcing as its biological counterparts. This adds to our confidence in the model. These results strongly support the idea that the intact pyloric pacemaker group acts as a uniform low-dimensional deterministic nonlinear oscillator, and the regular pyloric oscillation is the outcome of cooperative behavior of strongly coupled neurons, having different dynamical and biophysical properties when isolated.

ANALOG HARDWARE IMPLEMENTATIONS OF ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 20 (03) ◽  
pp. 349-373 ◽  
Author(s):  
NADIA NEDJAH ◽  
RODRIGO MARTINS DA SILVA ◽  
LUIZA DE MACEDO MOURELLE
Keyword(s):  
Computing System ◽  
Activation Function ◽  
Lookup Table ◽  
Training Process ◽  
Systems Software ◽  
Hardware Implementations ◽  
Intrinsic Parallelism ◽  
Software Implementations ◽  
Analog Implementation ◽  
Artificial Neural

There are several possible implementations of artificial neural network that are based either on software or hardware systems. Software implementations are rather inefficient due to the fact that the intrinsic parallelism of the underlying computation is usually not taken advantage of in a mono-processor kind of computing system. Existing hardware implementations of ANNs are efficient as the dedicated datapath used is optimized and the hardware is usually parallel. Hardware implementations of ANNs may be either digital, analog, or even hybrid. Digital implementations of ANNs tend to be of high complexity, thus of high cost, and somehow imprecise due to the use of lookup table for the activation function. On the other hand, analog implementation of ANNs are generally very simple and much more precise. In this paper, we focus on possible analog implementations of ANNs. The neuron is based on a simple operational amplifier. The reviewed implementations allow for the use of both negative and positive synaptic weights. An alternative implementation permits the realization of the training process.

Techniques for obtaining analytical solutions for Rall's model neuron

1987 ◽  
Vol 20 (2) ◽  
pp. 151-166 ◽  
Author(s):  
George W. Bluman ◽  
Henry C. Tuckwell
Keyword(s):  
Analytical Solutions ◽  
Model Neuron

Weighted median filters: An analog implementation

Integration ◽  
2016 ◽  
Vol 55 ◽  
pp. 227-231 ◽  
Author(s):  
H.C. Bandala-Hernandez ◽  
J.M. Rocha-Pérez ◽  
A. Díaz-Sánchez ◽  
J. Lemus-López ◽  
H Vázquez-Leal ◽  
...  
Keyword(s):  
Median Filters ◽  
Weighted Median ◽  
Analog Implementation ◽  
Weighted Median Filters
Sign in / Sign up

Export Citation Format

Share Document