Research Project 3 - Theory and computation of internal state dynamics

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Research Project 3 - Theory and computation of internal state dynamics Leads: Surya Ganguli PhD and Krishna Shenoy PhD (with David Sussillo PhD) Project Summary This research project will develop both theoretical principles and computational methods for elucidating how external inputs interact with diverse internal neural state dynamics to drive fundamental neural computations, including: (1) the generation of accurate percepts through cortical amplification of weak sensory inputs amidst spontaneous background activity; (2) Bayesian integration of multisensory inputs to compute internal estimates of external state variables and their uncertainty; and (3) the triggering and maintenance of discrete internal attractor states dictating stable behaviors. Additionally, we will develop general and widely applicable computational tools to empower the simultaneous all-optical read-write multi-SLM technology, developed in RP1 and generalized to RP2 and RP4. These tools will: (1) employ state of the art systems identification methods to algorithmically extract from neural data network models of internal state dynamics; and (2) employ model based control theoretic methods to identify interesting optogenetic stimulation patterns that can both reveal computational insights into, as well as enable control of, the dynamics of cortical circuits. Our theories and computational tools for systems identification, insight and control, will both drive the design of experiments, and in-turn be iteratively refined by the outcomes of these experiments, across all RP’s. In Aim 1 we will develop theories for how the interplay of external inputs, and internal spontaneous activity in spiking neural networks with multiple cell-types, can set fundamental limits on the perceptual sensitivity of cortical networks. We will iteratively test these theories in a tight theory-experiment loop across 2 homologous sensory systems: mouse V1 in RP1 and macaque V1 in RP2. This parallel study will enable us to elucidate both convergent and divergent properties of the fundamental computation of sensory amplification in two different cortical networks that differ drastically in scale. We will also explore how the interplay between spontaneous and evoked activity is modified by diverse internal state changes, including attention, thirst, satiety, and top- down control of V1 in tight collaboration with experiments done in RP1 and RP2. In Aim 2 we will develop theories for how neural circuits with basic synaptic and cellular properties can combine internal states with external inputs to perform Bayesian integration of evidence. We will iteratively test and refine such theories in theory driven experiments on Bayesian updating of position in mouse V1, MEC, RSC and hippocampus in RP4, and Bayesian updating of evidence in joint recordings of macaque V1 and FEF in RP2. And in Aim 3 we will develop our generalized computational tools described above for systems identification, insight and control, and we will validate them by studying diverse internal state dynamics, including the robustness and flexibility of attractor transitions in mouse OFC in RP1 and mouse RSC in RP4, Bayesian integration of position in mouse RSC in RP4 and Bayesian integration of evidence in macaque V1-FEF loops in RP2. Thus overall, RP3 plays a unifying role through tight bi-directional feedback loops with RP1, RP2, and RP4 and the DSC.