Periodic Reporting for period 1 - DendritesInVivo (Prediction and validation of in vivo dendritic processing)
Période du rapport: 2020-03-01 au 2022-02-28
In this project we explored the hypothesis that neural communication is tailored to make maximal use of dendritic capabilities. We developed a new theoretical approach for investigating the function of single neurons by combining biophysical modeling with machine learning. We applied this to address two main questions: 1) How should input to a neuron be structured to maximize the discriminability of different stimuli?; 2) For a given regime of input and computational task, how are the structure and biophysics of dendrites predicted to be exploited by the brain? Our simulations and analysis showed that single neurons are exquisitely sensitive to both the spatial and temporal structure of their inputs. When information is encoded in both of these input properties simultaneously, distinct processing strategies can be synergistically combined to maximize computational power. Focusing on a canonical 'feature-binding' problem, we derived experimentally testable predictions about how two different biophysical mechanisms can be exploited for nonlinear computation, and how their relative contributions will vary throughout a dendritic tree.