Information is processed in the brain through communication among networks of neurons. Understanding these interactions will bring great insights into how we perceive the outside world, execute motor actions and store memories, and where these functions go wrong in injured or diseased brain states. Unlike the circuits in a digital computer, neural networks are built from living cells with complex morphologies and biophysical properties. These cellular features impose fundamental limits on communication within the brain but may also hold the key to its unrivaled computational power. Neurons receive the majority of input on their dendrites - thin processes that emanate from cell bodies in elaborately branched tree structures. Physical constraints mean that incoming signals are subject to severe attenuation as they are integrated to produce the output response of a neuron. However, nonlinear interactions within the dendritic tree may compensate for this, or even allow mathematical operations to be performed on the input that are often thought to require entire networks.
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.
