DeepInsight is a deep learning-based decoding framework for discovering and characterizing the neural correlates of behavior and stimuli in unprocessed neural data. This tool allows raw data usage directly as input, removing the traditional step of spike-sorting. This provides a more objective way of measuring decoding performance.
The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once the neural network is trained, it can be analyzed to determine elements of the neural code that are informative about a given variable.
Paper: https://www.biorxiv.org/content/10.1101/871848v1.full
Paper PDF: https://www.biorxiv.org/content/10.1101/871848v1.full.pdf
Github: https://github.com/CYHSM/DeepInsight
Jupyter Notebook: https://github.com/CYHSM/DeepInsight/blob/master/notebooks/deepinsight_example_usage.ipynb
Installation
For now install DeepInsight with the following command:
pip install -e git+https://github.com/CYHSM/DeepInsight.git#egg=DeepInsightExample (Copied from Github)
import deepinsight
# Load your electrophysiological or calcium-imaging data
(raw_data,
raw_timestamps,
output,
output_timestamps,
info) = deepinsight.util.tetrode.read_tetrode_data(fp_raw_file)
# Transform raw data to frequency domain
deepinsight.preprocess.preprocess_input(fp_deepinsight, raw_data, sampling_rate=info['sampling_rate'],
channels=info['channels'])
# Prepare outputs
deepinsight.util.tetrode.preprocess_output(fp_deepinsight, raw_timestamps, output, output_timestamps,
sampling_rate=info['sampling_rate'])
# Train the model
deepinsight.train.run_from_path(fp_deepinsight, loss_functions, loss_weights)
# Get loss and shuffled loss for influence plot
losses, output_predictions, indices = deepinsight.analyse.get_model_loss(fp_deepinsight, stepsize=10)
shuffled_losses = deepinsight.analyse.get_shuffled_model_loss(fp_deepinsight, axis=1, stepsize=10)
# Plot influence across behaviours
deepinsight.visualize.plot_residuals(fp_deepinsight, frequency_spacing=2)
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
