Yuri Dabaghian - US grants

The reliability of our memories is nothing short of remarkable. Thousands of neurons die every day, synaptic connections appear and disappear, and the networks formed by these neurons constantly change due to various forms of synaptic plasticity. How can the brain develop a reliable representation of the world, learn and retain memories despite, or perhaps due to, such complex dynamics? Answering such questions is the natural evolution of recent work by the Dabaghian lab, which has been studying spatial cognition by modeling mechanisms of learning, based on algebraic topology methods developed by the Memoli group. This approach rests on the insight that the animal brain must first construct a rough-and-ready map of the environment before being able to fill it in with geometric details, which would be too computationally costly in light of typical navigational goals such as evading predators, returning to a nest, or finding a cafe. This basic map pays particular attention to the connectivity between places in the environment and is thus based on spatial topology; as such, the investigators hypothesized, it would be amenable to analysis by topological methods.

By simulating exploratory movements through different environments Dabaghian and Memoli will study how stable topological features arise in assemblies of simulated neurons operating under a wide range of conditions, including variations in firing rate, the size of the space each cell "senses," the number of cells in the population, and electrical oscillations in the brain that alter the behavior of the ensemble. They will use several novel methods from Persistent Homology Theory to understand how connections between cells (synapses) influence the speed and reliability of spatial learning. One might assume that learning would be enhanced if synapses never disappeared, but biology has clearly evolved to favor great synaptic plasticity. One reason may be that the loss of certain connections allows more room for mistakes to be unlearned. The objectives of this project are to study synaptic plasticity in a computational model, which will allow the influences of different parameters on the outcome of learning to be studied in detail. Principles that emerge on spatial learning in the hippocampus should be translatable to spatial cognition in machines.

There is a tension in neuroscience between the emergent phenomena of interested, such as learning and memory, and the level at which most data are acquired. For example, numerous experimental labs study how the strengths of synaptic connections and their dynamics affect cognition by establishing empirical correlations between in vitro electrophysiology measurements and data collected in animal behavioral experiments. However, these correlations fall short of causal explanations: to date, there exist no mechanisms connecting recordings in individual neurons and synapses with cognitive learning dynamics. The problem is not due to a lack of observations at either the neuronal or the systemic level; rather, it reflects a principal gap in our ability to link these two scales. Even if a full description of every neuron in the brain could be produced, there would still be a gap in our ability to transition from local data to making qualitative conclusions about how it combines to produce systemic cognitive outcomes. Addressing this problem requires a conceptual framework encompassing a computational model that would link the experimentally derived characteristics of individual cells with effects of those characteristics at the ensemble level.

The proposed research aims to provide a way to establish such a connection: developing a data-driven, systemic model of hippocampal spatial learning based on the parameters of the hippocampal synaptic architecture, including the parameters of synaptic plasticity, using novel topological and geometric techniques. Recent developments in Algebraic Topology will be used to integrate the parameters of synaptic connectivity and synaptic plasticity (e.g., long- and short-term potentiation and depression), to study structure of this map, the mechanisms of its formation and deterioration, and to evaluate the time required to produce a spatial map of a given environment, etc. This project is a natural evolution of prior work done by the Dabaghian group on modeling the mechanisms of spatial learning, based on algebraic topology methods developed by the M?moli group. The theory-based insight into learning phenomena will produce a qualitatively better understanding of how to interpret data, how to design new experiments, what variables should be targeted in measurements, as well as how to minimize use of animals, and in general how to optimize use physical and intellectual resources.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.