Giorgio Ascoli, Professor, Bioengineering, received funding for the study: "CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems."
Ascoli is collaborating with Gina C. Adam, Assistant Professor, School of Engineering and Applied Science, at The George Washington University, on this research.
Ascoli and Adam are proposing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired by advances in computer science and engineering.
Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters.
The model will first be trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method.
This project goes beyond the existing state-of-the-art by looking at large-scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. Hippocampome was created by, and is maintained by, Ascoli’s lab at Mason.
This project will help estimate how much of the learning in these neuroanatomically constrained networks is due to dynamic synaptic plasticity and how much is due to fixed neuroanatomy.
These insights could be used in the future to ground further experimental testing of different hippocampal microcircuitry functionality and other theoretical and modeling approaches in other regions of the hippocampus.
Long term, scientific advances in this area could lead to pharmacological solutions and even cognitive prostheses to help the millions who suffer from Alzheimer's disease, epilepsy and other hippocampal-related cognitive impairments. With an in-depth understanding of the trade-offs between neuronal and synaptic diversity, regional connectivity, and learning rules in these regions, new algorithms and hardware implementations can be developed.
Ascoli received $250,000 from the U.S. Department of Energy for this project. Funding began in Aug. 2022 and will end in Aug. 2025.
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