A model of neural population dynamics for flexible sensorimotor control

This repository contains the code and data from the article "A model of neural population dynamics for flexible sensorimotor control"<br>To get started install Anaconda python package and other dependencies such as: h5py, numpy, argpars, sklearn, pickle, json.<b>To simulate a prepare to reach task to 8 targets</b>Then you can just run the python notebook file "reachingTask.ipynb" to simulate the task on a randomly connected dense RNN whose weights are stored in the folder datastore/WeightsData/denseRNN/dense_network_weights.hdf5 The simulation results are stored inside datastore/SimulationData/8dirreach_task/denseRNN/denseRNN_results_singlesimulation.hdf5 If you are using VScode, you can install a HDF5 viewer plugin so that you can visualize the contents of the hdf5 files in which we work in this code.<b>To run the analysis:</b>Run the file named "analyze_plot_results". This will plot the hand paths, kinematics, neural activities and also the PCA trajectories in the movement and preparation periods of the prepare-to-reach task.<br>for any queries please contact: hari.kalidindi at donders.ru.nl