BitBrain is a learning algorithm based upon a novel synthesis of ideas from sparse coding, computational neuroscience and information theory that support single-pass learning, accurate and robust inference, and the potential for continuous adaptive learning. They are designed to be implemented efficiently on current and future neuromorphic devices as well as on more conventional CPU and memory architectures.

This work was presented during NICE conference 2022: https://flagship.kip.uni-heidelberg.de/jss/HBPm?mI=235&publicVideoID=8944. Patent pending for the BitBrain algorithm.

How to run BitBrain code for MH version:

1. Install all required Python libraries: cv2, numpy, scipy
2. Compile the C library: e.g. cc -fPIC -shared -o MH_comp.so MH_lib.c
3. Run the Python script: python BitBrain_Python_MH.py

How to run BitBrain code for random filters version:

1. Install all required Python libraries: cv2, numpy, scipy
2. Go to "Random_filters" folder
3. Compile the C library: e.g. cc -fPIC -shared -o Random_comp.so Random_lib.c
4. Run the Python script: python BitBrain_Python_Random.py