Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters

Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.