Graduation Year
2020
Document Type
Campus Only Senior Thesis
Degree Name
Bachelor of Arts
Department
Physics
Reader 1
Professor Sarah Marzen
Reader 2
Professor John Milton
Terms of Use & License Information
Rights Information
2020 Rachel Taubman
Abstract
Biological neural networks encode predictive information about their environment with high energy efficiency and minimal learning supervision. In this study, a Hopfield-like recurrent neural network with two biologically-based learning rules consistently improves both prediction and energy efficiency in multiple parameter regimes with a two-state hidden Markov Model stimulus. The network also exhibits emergent synaptic normalization, which suggests that this feature observed in neurons may emerge from an interaction of other learning rules.
Recommended Citation
Taubman, Rachel, "Recurrent Neural Network with Homeostatic Plasticity Learning Rules Improves Prediction and Energy Efficiency and Leads to Emergent Synaptic Normalization" (2020). Scripps Senior Theses. 1590.
https://scholarship.claremont.edu/scripps_theses/1590
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.
