A Biophysical Basis for Learning and Transmitting Sensory Predictions

Muller, S. Z.; Abbott, L. F.; Sawtell, N. B.

doi:10.1101/2022.10.31.514538

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bioRxiv · 10.1101/2022.10.31.514538

A Biophysical Basis for Learning and Transmitting Sensory Predictions

Muller, S. Z.·Abbott, L. F.·Sawtell, N. B.

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Abstract

Homeostatic (anti-Hebbian) forms of synaptic are effective at eliminating "prediction errors" that signal the differences between predicted and actual sensory input. However, such mechanisms appear to preclude the possibility of transmitting the resulting predictions to downstream circuits, severely limiting their utility. Using modeling and recordings from the electrosensory lobe of mormyrid fish, we reveal interactions between axonal and dendritic spikes that support both the learning and transmission of predictions. We find that sensory input modulates the rate of dendritic spikes by adjusting the amplitude of backpropagating axonal action potentials. Homeostatic plasticity counteracts these effects through changes in the underlying membrane potential, allowing the dendritic spike rate to be restored to equilibrium while simultaneously transmitting predictions through modulation of the axonal spike rate. These results reveal how two types of spikes dramatically enhance the computational power of single neurons in support of an ethologically relevant multi-layer computation.

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Muller, S. Z., Abbott, L. F., Sawtell, N. B.. 2022-11-01. A Biophysical Basis for Learning and Transmitting Sensory Predictions. https://doi.org/10.1101/2022.10.31.514538

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