Navigational Neural Coding and De-noising

Abstract

The work discussed in this thesis is the product of investigation on information and coding theoretic properties of colluding populations of navigationally relevant mammalian neurons. For brevity and completeness, that work is presented chronologically in the order in which it was investigated. This thesis details coding theoretic properties of (and develop a model for communication between) colluding populations of spatially responsive neurons in the hippocampus (HC) and medial entorhinal cortex (MEC) through a hypothetical layer of interneurons (each of which posesses exclusively excitatory or inhibitory synapses). This work presents analysis of the changes in network structure induced by an anti-Hebbian learning process and translate these analyses into biologically testable hypotheses. Further, it is demonstrated that for appropriately parameterized codes (i.e. populations of grid and place cells in MEC and HC, respectively), this network is able to learn the code and correct for errors introduced by neural noise, potentially explaining the results of a correlational study: Place cell variability sharply decreases at a time that coincides with the maturation of the grid cell network in developing mice. Further, this work predicts that disruption of the grid cell network (e.g. via optogenetic inactivation and lesioning) should increase the variability of place cell firing, and impair decoding from these place cells' activities. Continuing down this avenue, we consider how the inclusion of a population of the somewhat controversial time cells (purportedly residing in HC and MEC) impacts de-noising network structure, coding properties of the population of populations of all three classes of navigatory neuron, and denoisability. These results are translated to testable neurobiological predictions. Additionally, to ensure realistic stimulus statistics, locations and times are taken from real rat paths recorded from navigating rats in the Computational and Experimental Neuroscience Laboratory at the University of Arizona. Interestingly, while time cells exhibit some of the coding and information theoretic trends described in chapter 4, in certain cases, they admit surprising connectivity trends. Most surprisingly, after including time cells in this framework it was discovered that some classes of neural noise appear to improve decoding accuracy over the entire path while simultaneously impairing accuracy of decoding position and time independently.