Predictive representation learning in biological neural networks Speaker: Friedemann Zenke

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• Friedemann Zenke, FMI
• Tuesday 25 April 2023, 11:00-12:00
• EEE Room 611 (Gabor Seminar Room).

If you have a question about this talk, please contact Dan F M Goodman.

Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains perform this processing in deep sensory networks shaped through plasticity. However, our understanding of the underlying plasticity mechanisms still needs to be improved. I will introduce Latent Predictive Learning (LPL), a local learning rule that conceptually extends Bienenstock-Cooper-Munro (BCM) theory. We show that deep neural networks equipped with LPL develop disentangled object representations without supervision. The same rule accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Finally, our model generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity (STDP). LPL thus constitutes a plausible normative theory of representation learning in the brain while making concrete testable predictions.

This talk is part of the Daniel F M Goodman's list series.

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• EEE Room 611 (Gabor Seminar Room)

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