Grid Cells-From Data Acquisition to Hardware Implementation: A Model for Connectome-Oriented Neuroscience

Abstract

One of the main challenges in modern neuroscience is to extract causal information from the brain in a way that it can be reproduced in a different substrate, such as a computer or a robot. While some major advancements have been made in neuroscience towards that goal, ranging from the discovery of the action potential, to cortical columns, to grid cells and their crystalline structure, there is no single model at the moment on how to best acquire and model and implement such data. While the correlation between light or sound stimuli with spiking activity is by now rather well established, the causal connection between higher-level stimuli, such as a complex environments, learning, memory, or decision making and neuronal spiking activity is not entirely clear. One exception to this vagueness is the discovery of grid cells.