Abstract

Scott McNealy, CEO of Sun Microsystems, is rumored to have quipped, “It’s the network dummy”, when a reporter asked where the computer was upon seeing a cluster of workstations. McNealy’s point was that the power of computer networks isn’t the (linear) sum of individual computers; the power is in the (nonlinear) manner in which they can work together. When a computational neuroscientist examines an EM image of neural tissue, does she see a network or a bunch of neurons? The answer will depend on what she understands to be the fundamental unit of neural computation?

We assume the fundamental unit of neural computation is not the individual neuron, compartment, synapse or even circuit in the traditional sense in which electrical engineers think of circuits, but rather ensembles of hundreds or thousands of neurons that organize themselves depending on the task, participate in multiple tasks, switch between tasks depending on context and are easily reprogrammed to perform new tasks. Consequently, the total number of computational units is far fewer than the number of neurons.

We also assume that much of what goes on in individual neurons and their pairwise interactions is in service to maintaining an equilibrium state conducive to performing their primary role in maintaining the body and controlling behavior. This implies that the contribution of small neural circuits to computations facilitating meso- or macro-scale behavior is considerably less than one might expect given the considerable complexity of the individual components. Since much of the complexity will manifest itself in the topology of the network, we need some means of computing topological invariants at multiple scales in order to tease out the computational roles of the multitude of circuit motifs that are likely present even in parts of the brain assumed to be structurally and functionally homogeneous.

In the talk, we describe the convergence of several key technologies that will facilitate our understanding of neural circuits satisfying these assumptions. These technologies include (i) high-throughput electron microscopy and circuit reconstruction for structural connectomics, (ii) dense two-photon-excitation fluorescent voltage and calcium probes for functional connectomics, and (iii) analytical methods from algebraic topology, nonlinear dynamical systems and deep recurrent neural networks for inferring function from structure and activity recordings.