Björn Kampa, Department of Neurophysiology, Institute of Zoology, Aachen University | Neural circuits of decision-making in virtual navigation

Abstract

How is our visual environment represented and processed in the brain? In my lab, we seek answers to this fundamental question with a multi-scale approach combining two-photon imaging and electrophysiological recordings with computation model simulations. In this way, we can directly assess how neuronal response properties depend on the local network circuit. Connections between cortical neurons are not made randomly. Specific connections involving excitatory and inhibitory neurons have been measured both statistically and functionally in several areas of rodent neocortex.

However, the precise composition of specific networks and the effect of specific connectivity on information processing in cortex remain in question, especially as a minority of synapses are likely to be made specifically. We found that specific excitatory connectivity can underlie amplification, decorrelation, competition and associative functions for cortex. Furthermore, our model simulations explain several observations of feature binding in visual cortex that we obtained using two-photon imaging of neuronal populations in mouse visual cortex. We also show that tuning for natural visual stimuli is independent of orientation preference, a likely consequence of specific connectivity.

Our results suggest a population code, where the visual environment is dynamically represented in the activation of distinct functional sub-networks. We now focus on how this representation of the visual scenery is used for navigation and decision-making. For this, we investigate the neural circuits in mice navigating in virtual reality allowing perfect control over the animal and the sensory input. Applying combinations of electrophysiology, two-photon imaging and optogenetics in awake behaving animals we analyse the representation of the environment and of the animals behavior in large populations of neurons in mouse visual cortex. Finally, the obtained experimental results are incorporated in computational model studies to further test our hypotheses on the neuronal circuits underlying decision-making.