In one project, we are developing models of spiking networks that relate neurons responses to circuit and cellular properties. The theoretical work is being done in collaboration with Nicolas Brunel (Duke), and we are testing predictions in the laboratory using neurophysiology with silicon multi-site probes that allow recording many neurons across cortical layers simultaneously. Some results have been submitted in abstract form for presentation (Sanzeni et al., Society for Neuroscience meeting, 2017) and are being elaborated into a manuscript (Sanzeni et al., in prep). A manuscript with an early form of some of these results, obtained via simulation, has been submitted (Histed, under review). This work is shedding light on what computations the cortex can perform, and what circuit properties like connection number and strengths, are needed to control those computations. In a second project, we are examining how the cortex controls the gain of responses by using a learning paradigm. Animals and humans improve their performance in sensory tasks with practice. But it is not known in general whether cortical representations become stronger (response gain increases) with practice, or whether downstream decoding becomes more effective. To examine the neural basis of learning, we are training animals to perform controlled learning tasks and examining how neural responses change with learning. We will adapt the models from project 1 to predict what circuit changes might lead to any response changes, and follow up that work with experimental tests of the predictions. This work will explain how the brain, particularly the cerebral cortex, changes during learning to support improved behavior. Understanding the circuit elements that change with learning is made possible by insights from theoretical work on networks that relates neural activity to circuit and anatomical features.
| Histed, Mark H (2018) Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks. eNeuro 5: |
