Hey All,
I am a long term Numenta follower even before the Thousand Brains Theory, I have been following Numenta since the cortical Learning algorithm whitepaper days. Today I wanted to share my passion project that I have been working on the sidelines for quite some time now , it is comparatively complete , can do few tricks but most importantly these are all Numenta Inspired HTM Neurons using Sparse Distributed representation for inter communication, uses Depolarization to classify and Predict , Uses Sensory Motor Inference, Grid Cells , so on and so forth. Most importantly this is extensible to create the motor model as well, there is no support for Motor Learning currently and I have plans of implementing that in the project.
Here’s a quick youtube video run through on how the project is working :
Video Link : Passion Project Update for TBT Agent : Hentul - 1/22/2024
Neural Architecture:
There are 4 different Components to the Neural Architecture: there are 3 different Neuronal Layers composed of HTM Neurons that implement Sequence Memory and there is HC-EC Complex which helps associating locations of the agent with the corresponding sensations being sensed. Among the 3 layers there is one First Order Memory Layer similar to Layer 4 in cortex and 2 Second Order Memory layers which are similar to Layer 3A and Layer 3B also in the cortex. The HC-EC complex is the key component in the architecture which is responsible for classification and interpreting agent locations in different places in the agentic universe.
Project Environment : In this project I am using my current Desktop itself as the agents universe and the agent directly controls the mouse cursor via output motor vectors on the screen , whenever the agent wants to make a movement it sends out a movement signal vector and the motor cortex performs that action moving the cursor to the exact position and sensory cortex kick into action once the cursor is moved there by reading all the pixel values in the location to update its internal state of the agent w.r.t to the screen. There are different modes the agent is currently supported to run in first “LEARNING” mode & “PREDICTION” mode, each of these modes helps the agent to learn new objects and classify new object into its known categories, make predictions to classify object or even on how the new position is supposed to feel like on known objects.
Link :
The blogs are raw and are a work in progress, I have worded them in layman terms for easy understanding.
- [Reference Frame Based Classification] ([Reference Frame Based Classification]
- [BURST PREVENTION POOLING ALGORTIHM] ([BURST AVOIDANCE STABILIZATION BY UNSUPERVISED LEARNING]
My Blogs : Blogs