Neural decoder example

[ncguilbeault]

Summary

This PR adds a new example demonstrating how to use the Bonsai.ML.NeuralDecoder package. It uses a bayesian state space point process model to decode an animal's position in real time using either spike sorted or clusterless neural activity from tetrode recordings in the hippocampus.

Changes

• Added bonsai environment
• Added decoder python library
• Added workflow with extensions
• Added README
• Added clusterless spike decoder model
• Changed name to sorted spikes
• Added clusterless spikes bonsai workflow
• Updated data iterator to handle outputting position bins
• Changed name of core to decoder
• Added likelihood module
• Updated model loader to use decoder module
• Updated init file
• Add bonsai shaders package
• Updated to use shaders render frequency for timing
• Renamed folders more appropriately to NeuralDecoding/PositionDecodingFromHippocampus
• Updated to remove cuda path declaration and copy of decoder class
• Updated to have filename declarations at top
• Updated
• Added README to example
• Added requirements.txt file
• Updated notebook for 50Hz down sampling
• Added cell for plotting
• Added online FPS estimate
• Updated to properly use 50 Hz
• Remove exensions and use new Bonsai.ML.NeuralDecoding package
• Updated to use Bonsai.ML package
• Removed requirements.txt file in favor of installing from git repo
• Updated notebooks for new package
• Added new dataset to README
• Updated example README
• Removed python package from inside example folder
• Renamed workflows
• Updated notebooks and removed redundant loading from file
• Updated package version correctly