Evolving to learn: discovering interpretable plasticity rules for spiking networks

• DOI: 10.48550/arxiv.2005.14149
• Publication Date: 2020-01-01
• Authors: Jakob Jordan; Maximilian Schmidt; Walter Senn; Mihai A. Petrovici
• Keywords: 0301 basic medicine; Computational Neuroscience; 0303 health sciences; 03 medical and health sciences; Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; Neurons and Cognition (q-bio.NC); Learning, plasticity and memory

Continuous adaptation allows survival in an ever-changing world. Adjustments the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at phenomenological level, as so called "plasticity rules", is both understanding biological information processing and developing cognitively performant artificial systems. We suggest automated approach discovering biophysically plausible plasticity rules based on definition of task families, associated performance measures biophysical constraints. By evolving compact symbolic expressions we ensure discovered amenable to intuitive understanding, fundamental successful communication human-guided generalization. successfully apply our typical learning scenarios discover previously unknown mechanisms efficiently rewards, recover efficient gradient-descent methods target signals, uncover various functionally equivalent STDP-like with tuned homeostatic mechanisms.