xnar-adlhazi's Stars
nikorose87/DJS-analysis
Dynamic Joint Stiffness analysis
anastasiasenyk/estimating_joint_stiffness
Estimating joint stiffness using OpenSim and Python
Livingstone99/stiffness_analysis
static analysis
Munashe-zw/Analysis-of-Plane-Trusses---Structure-Stiffness-Method
This program was developed such that it can be used to analyze any statically determinate or indeterminate plane truss, of any arbitrary configuration, subjected to any system of joint loads.
qq7933086/Static-and-dynamic-stiffness-of-rock-samples-from-a-shale-oil-reservoir
ritchie46/anaStruct
2D structural analysis in Python
TeamLEGWORK/LEGWORK
Package for making predictions about stellar-origin sources in space-based gravitational wave detectors
SpikeFrame/MultilayerSpiker
Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks
IGITUGraz/adaptation_working_memory
Spike frequency adaptation supports network computations on temporally dispersed information
Priya2296/Preferential-deletion-of-nodes-in-a-dynamic-network-using-betweenness-centrality
Formation of dynamic network by adding nodes using linear preferential attachment rule and deletion of nodes with minimum betweenness centrality.
AAshqar/GammaCoupling
A project to model gamma coupling of a biological neural network consisting of pyramidal neurons and interneurons
alanakil/SampleCode
Sample code for a recurrent network of 5000 EIF neurons with random connectivity, driven by ffwd input from a population of 1000 Poisson spike trains (simulating an external population). Provided by Robert Rosenbaum.
sweta111/Hodgkin_Huxley
Electrical Modeling of Synapse
ramisdb/Firefly-synchrony-in-C
A unique way of demonstrating synchronous oscillation using a multithreaded program
shandysulen/NeuronNetworkSimulation
Simulates a neuronal network of 1000 neurons to output a graph showing recurrent spike trains
lavalleedelgado/som
Self-organizing map, an unsupervised neural network that models the topology of high-dimensional data.
Lior-Lebovich/ICB
Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics
IGITUGraz/Spike-Frequency-Adaptation-Supports-Network-Computations
Code for: Spike Frequency Adaptation Supports Network Computations on Temporally Dispersed Information
SkrighYZ/VisNeuro
Implementation of visual cortical neurons' behaviors.
anisotropic-connectivity-local-circuits/code
Computations and figures for "Nonrandom connectivity in local cortical circuits from anisotropic axon morphology" (working title).
shilpakancharla/event-based-velocity-prediction-snn
Neuromorphic computing uses very-large-scale integration (VLSI) systems with the goal of replicating neurobiological structures and signal conductance mechanisms. Neuromorphic processors can run spiking neural networks (SNNs) that mimic how biological neurons function, particularly by emulating the emission of electrical spikes. A key benefit of using SNNs and neuromorphic technology is the ability to optimize the size, weight, and power consumed in a system. SNNs can be trained and employed in various robotic and computer vision applications; we attempt to use event-based to create a novel approach in order to the predict velocity of objects moving in frame. Data generated in this work is recorded and simulated as event camera data using ESIM. Vicon motion tracking data provides the ground truth position and time values, from which the velocity is calculated. The SNNs developed in this work regress the velocity vector, consisting of the x, y, and z-components, while using the event data, or the list of events associated with each velocity measurement, as the input features. With the use of the novel dataset created, three SNN models were trained and then the model that minimized the loss function the most was further validated by omitting a subset of data used in the original training. The average loss, in terms of RMSE, on the test set after using the trained model on the omitted subset of data was 0.000386. Through this work, it is shown that it is possible to train an SNN on event data in order to predict the velocity of an object in view. (Spring 2022 MS Computer Science Thesis - North Carolina State University)
ModelDBRepository/232096
Statistical Long-term Synaptic Plasticity (statLTSP) (Costa et al 2017)
IGITUGraz/LSNN-official
Long short-term memory Spiking Neural Networks
codeaudit/Topology-of-Learning-in-Artificial-Neural-Networks
Understanding how neural networks learn using topology
exo-cortex/dde_network_solver
A rewrite of my network DDE solver used for benchmarking reservoir computing performance in differen topologies of delay-coupled oscillators.
reservoirpy/reservoirpy
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing
Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
yuan-feng/neural-network-structural-dynamics
neural network for structural dynamics
tiwarylab/LSTM-predict-MD
Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network
jithendaraa/ODE-RL
Using Neural Ordinary Differential Equations to model continuous time dynamics