Motivic information, path integrals and spiking networks

I’m writing a series of posts that will explore the connections between these topics. Here is a rough outline of the series, which I will fill in slowly over time.

Motivic Information

1. Building foundations of information theory on relative information

2. Conditional relative information and its axiomizations

3. Zeta functions, Mellin transforms and the Gelfand-Leray form

4. Motivic relative information

Path Integrals

1. Path integrals and continuous-time Markov chains

2. Biased stochastic approximation

Spiking Networks

1. Machine learning with relative information

2. Process learning with relative information

3. Relative inference for mutable processes

4. Biased stochastic approximation for mutable processes

5. Convergence of biased stochastic approximation

6. Spiking neural networks

Processes and variety maximization   Building foundations of information theory on relative information