Churchland lab

I started thinking about randomly connected networks during a recent visit to the Center for Theoretical Neuroscience where I was a seminar speaker last week. The idea behind these networks, which have been championed recently by Larry Abbott, Stefano Fusi and others, is that neurons in some parts of the brain receive inputs from neurons with a broad array of response properties (see figure, below). This means that, at least from the point of view of an experimenter recording from such neurons, their responses seem at first to be quite confusing. In one paper from Stefano Fusi’s group, they describe prefrontal neurons recorded during a task where monkeys were deciding among a number of different cues, according to a rule that could change over the course of the session. The neurons were recorded in Earl Miller‘s lab at MIT. Each neuron appeared to reflect a complicated combination of the two sensory cues, and the particular context in which they were presented. The paper argues that responses like these are expected in randomly connected networks, and indeed could subserve the very flexibility that is required by tasks that require dynamic combinations on sensory inputs and rules. Might this have implications for multisensory research? We don’t yet know, but like the Miller Lab, we present animals with a complex set of stimuli that vary along several dimensions. A randomly connected network might be the most efficient way for the animals to harvest the relevant information.

From Figure 3 of Rigotti et al