Network Science approach to the cortical microcircuit
The primary focus of our group is to apply network science to cortical microcircuit dynamics in order to: 1) establish the higher-order cellular and synaptic mechanisms that propagate spikes, 2) to build improved encoding models of single-trial microcircuit activity in behaving mammals, and 3) to decode single-trial dynamics without relying on trial-averaged lower dimensional summaries. The appeal is that this approach makes it possible to unify both mechanism and the code.
The neocortical microcircuit
The neocortical microcircuits we study include neurons and their synaptic connections within volumes of ~500 μm3. Because most connections in neocortex are between neighboring neurons, these microcircuits represent the scale over which (1) most excitatory and inhibitory interactions take place and (2) synaptic connections strengthen or weaken according to the relative spike timing between pre- and post-synaptic neurons – meaning that this is also the scale at which Hebbian learning occurs. Consequently, the microcircuit is the scale at which the majority of the algorithms that underlie cortical computation(s) are implemented, and therefore the ideal scale to reveal rules that govern information processing in neocortex.
Network science draws on statistics, physics, and computer science to study large complex systems. The complex system is made tractable by reducing it to a network based on its connections, or some more abstract interactions. It is then possible to quantitatively compare and contrast networks, probe them for specific structural features, model information transmission, and develop null models against which to test hypotheses. We summarize the statistical dependencies in spiking activity between pairs of neocortical neurons across a population as weighted, directed networks, using a variety of algorithmic approaches. These summaries of neocortical microcircuit dynamics present substantial analytic challenges our group is happy to tackle.