The leaky, competing, accumulator (LCA)
this is a lightweight python implementation of the leaky, competing, accumulator, based on ,  and . The default is to behave like .
how to use
here's an example that you can play with on google colab:
so simply do
pip install pylca
- ... allows any non-negative self-excitation. [1, 2] assume self-excitation of the accumulators is zero.
- ... doesn't terminate the LCA process when the (activity threshold) criterion is met, which is different from . the user can truncate the activity time course post-hoc.
- ... lower bound the output activity by 0 (i.e. ReLU), like [1, 2].  can do other non-linear transformations
- ... doesn't perform exponential weighted moving average of the inputs.  can do this.
 Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550–592. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/11488378
 Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009). A context maintenance and retrieval model of organizational processes in free recall. Psychological Review, 116(1), 129–156. https://doi.org/10.1037/a0014420