Evolving to learn: discovering interpretable plasticity rules for spiking networks
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
DOI:
10.48550/arxiv.2005.14149
Publication Date:
2020-01-01
AUTHORS (4)
ABSTRACT
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.
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