Analysis and modulation of spike trains and oscillations in identified neural network of Lymnaea stagnalis L

2000 ◽  
Vol 51 (2-4) ◽  
pp. 231-235
Author(s):  
G. Molnár ◽  
A. Szűcs ◽  
Katalin S. Rózsa
Keyword(s):  
Neural Network ◽  
Lymnaea Stagnalis ◽  
Spike Trains

scholarly journals A convolutional neural network for estimating synaptic connectivity from spike trains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

AbstractThe recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

scholarly journals Spiking neural network classification for spike train analysis of physiotherapy movements

2020 ◽  
Vol 9 (1) ◽  
pp. 319-325
Author(s):  
Fadilla ‘Atyka Nor Rashid ◽  
Nor Surayahani Suriani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Promising Result ◽  
Spike Trains ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Neural Network Classification ◽  
Vision Rehabilitation ◽  
Rehabilitation Exercises ◽  
Spike Patterns

Classifying gesture or movements nowadays become a demanding business as the technologies of sensor rose. This has enchanted many researchers to actively investigated widely within the area of computer vision. Rehabilitation exercises is one of the most popular gestures or movements that being worked by the researchers nowadays. Rehab session usually involves experts that monitored the patients but lacking the experts itself made the session become longer and unproductive. This works adopted a dataset from UI-PRMD that assembled from 10 rehabilitation movements. The data has been encoded into spike trains for spike patterns analysis. Next, we tend to train the spike trains into Spiking Neural Networks and resulting into a promising result. However, in future, this method will be tested with other data to validate the performance, also to enhance the success rate of the accuracy.

Ergodicity of Spike Trains: When Does Trial Averaging Make Sense?

2003 ◽  
Vol 15 (6) ◽  
pp. 1341-1372 ◽  
Author(s):  
Naoki Masuda ◽  
Kazuyuki Aihara
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Spike Trains ◽  
Neural Network Models ◽  
Large Variability ◽  
Synaptic Inputs ◽  
Synchronous Firing ◽  
High Membrane Potential ◽  
External Stimuli ◽  
The Brain

Neuronal information processing is often studied on the basis of spiking patterns. The relevant statistics such as firing rates calculated with the peri-stimulus time histogram are obtained by averaging spiking patterns over many experimental runs. However, animals should respond to one experimental stimulation in real situations, and what is available to the brain is not the trial statistics but the population statistics. Consequently, physiological ergodicity, namely, the consistency between trial averaging and population averaging, is implicitly assumed in the data analyses, although it does not trivially hold true. In this letter, we investigate how characteristics of noisy neural network models, such as single neuron properties, external stimuli, and synaptic inputs, affect the statistics of firing patterns. In particular, we show that how high membrane potential sensitivity to input fluctuations, inability of neurons to remember past inputs, external stimuli with large variability and temporally separated peaks, and relatively few contributions of synaptic inputs result in spike trains that are reproducible over many trials. The reproducibility of spike trains and synchronous firing are contrasted and related to the ergodicity issue. Several numerical calculations with neural network examples are carried out to support the theoretical results.

scholarly journals TESTING OF INFORMATION CONDENSATION IN A MODEL REVERBERATING SPIKING NEURAL NETWORK

2011 ◽  
Vol 21 (03) ◽  
pp. 187-198 ◽  
Author(s):  
ALEXANDER VIDYBIDA
Keyword(s):  
Neural Network ◽  
External World ◽  
Spike Trains ◽  
Condensation Process ◽  
Spiking Neural Network ◽  
Periodic Activity ◽  
Information Dynamics ◽  
Geometric Size ◽  
Fully Connected ◽  
Input Spike

Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.

scholarly journals Radar-Based Hand Gesture Recognition Using Spiking Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1405
Author(s):  
Ing Jyh Tsang ◽  
Federico Corradi ◽  
Manolis Sifalakis ◽  
Werner Van Leekwijck ◽  
Steven Latré
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Liquid State ◽  
Continuous Wave ◽  
Spike Trains ◽  
Hand Gesture Recognition ◽  
Radar Signal ◽  
Support Vector ◽  
Hand Gesture ◽  
Wave Radar

We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets.

scholarly journals CoNNECT: Convolutional Neural Network for Estimating synaptic Connectivity from spike Trains

2020 ◽  
Author(s):  
Daisuke Endo ◽  
Ryota Kobayashi ◽  
Ramon Bartolo ◽  
Bruno B. Averbeck ◽  
Yasuko Sugase-Miyamoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generalized Linear Model ◽  
Simulated Data ◽  
Spike Trains ◽  
Synaptic Connectivity ◽  
Neuronal Circuits ◽  
Cortical Areas ◽  
Neuronal Spike Trains ◽  
Individual Dataset

The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians, because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms, GLMCC. Although the GLMCC algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another algorithm using a convolutional neural network for estimating synaptic connectivity from spike trains, CoNNECT. After adaptation to very large amounts of simulated data, this algorithm robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new algorithm, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

Sign in / Sign up

Export Citation Format

Share Document