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Articles 1 - 8 of 8
Full-Text Articles in Computational Neuroscience
Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher
Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher
Northeast Journal of Complex Systems (NEJCS)
We propose an extension of renormalization into the domain of spiking neural networks, thereby providing a novel framework for coarse-graining neural networks without disrupting their critical properties. The proposed coarse-graining technique merges neurons and synaptic connections based on a graph-theoretic distance derived from synaptic weight strength and is configured to effectively prune the reservoir size while preserving the scale-free spiking dynamics indicative of criticality. Criticality in spiking neural networks may provide information-theoretic advantages by optimizing information processing and sensitivity to input. Using time-series prediction benchmarks, we demonstrate that networks operating at criticality exhibit up to 32% higher prediction accuracy before …
Web Application For Simulation Of An Agent-Based Model In Netlogo3d, Chris Davis Perumal, Abraham Nofal, Benedict J. Kolber, Rachael Miller Neilan
Web Application For Simulation Of An Agent-Based Model In Netlogo3d, Chris Davis Perumal, Abraham Nofal, Benedict J. Kolber, Rachael Miller Neilan
Spora: A Journal of Biomathematics
Agent-based models (ABMs) are computer simulation models for studying systems of autonomous agents. Modelers often use specialized software like NetLogo to develop ABMs, but this software poses barriers to researchers in other disciplines with no prior programming experience. To address this issue, we developed a web application that allows users to simulate our ABM via a web browser, eliminating the need for the user to download and use specialized software. While presented in the context of a specific ABM, our approach can be applied to other ABMs to enhance accessibility. The ABM presented here was developed in NetLogo3D. The model …
Improved Efficiency And Sensitivity Analysis Of 3-D Agent-Based Model For Pain-Related Neural Activity In The Amygdala, Kayla Kraeuter, Carley Reith, Benedict J. Kolber, Rachael Miller Neilan
Improved Efficiency And Sensitivity Analysis Of 3-D Agent-Based Model For Pain-Related Neural Activity In The Amygdala, Kayla Kraeuter, Carley Reith, Benedict J. Kolber, Rachael Miller Neilan
Spora: A Journal of Biomathematics
Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model’s usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed …
The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska
The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska
Karbala International Journal of Modern Science
Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.
Extending Power Series Methods For The Hodgkin-Huxley Equations, Including Sensitive Dependence, James S. Sochacki
Extending Power Series Methods For The Hodgkin-Huxley Equations, Including Sensitive Dependence, James S. Sochacki
CODEE Journal
A neural cell or neuron is the basic building block of the brain and transmits information to other neurons. This paper demonstrates the complicated dynamics of the neuron through a numerical study of the Hodgkin-Huxley differential equations that model the ionic mechanisms of the neuron: slight changes in parameter values and inputted electrical impulses can lead to very different (unexpected) results. The methods and ideas developed for the ordinary differential equations are extended to partial differential equations for Hodgkin-Huxley networks of neurons in one, two and three dimensions.
Strengthening Relationships Between Neural Ideals And Receptive Fields, Angelique Morvant
Strengthening Relationships Between Neural Ideals And Receptive Fields, Angelique Morvant
Rose-Hulman Undergraduate Mathematics Journal
Neural codes are collections of binary vectors that represent the firing patterns of neurons. The information given by a neural code C can be represented by its neural ideal JC. In turn, the polynomials in JC can be used to determine the relationships among the receptive fields of the neurons. In a paper by Curto et al., three such relationships, known as the Type 1-3 relations, were linked to the neural ideal by three if-and-only-if statements. Later, Garcia et al. discovered the Type 4-6 relations. These new relations differed from the first three in that they were …
Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang
Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang
Journal of the Arkansas Academy of Science
The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.
Predicting Phase Resetting Due To Multiple Stimuli, Kelsey M. Vollmer, Davy C. Vanderweyen, Derek R. Tuck, Sorinel A. Oprisan
Predicting Phase Resetting Due To Multiple Stimuli, Kelsey M. Vollmer, Davy C. Vanderweyen, Derek R. Tuck, Sorinel A. Oprisan
Journal of the South Carolina Academy of Science
We generalized the phase resetting curve (PRC) to a more realistic case of neural oscillators receiving two or more inputs per cycle. The PRC tabulates the transient change in the firing period of a neuron due to an external perturbation, such as a presynaptic stimulus. We used a conductance-based model neuron to estimate experimentally the two-stimulus PRC and compared the results against our mathematical prediction based on the assumption of instantaneous recurrent stimulation. Within the limits of the recurrent stimulation assumptions, we found that the newly introduced prediction for the two-stimulus PRC matched experimental measurements. Our new results open the …