College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
College - Author 2
College of Engineering
Department - Author 2
Electrical Engineering Department
Degree - Author 2
BS in Electrical Engineering
Date
6-2026
Primary Advisor
Anu Aggarwal, College of Engineering, Electrical Engineering Department
Abstract/Summary
This project seeks to design and simulate a neuromorphic spiking neural network (SNN) that implements the logical AND and logical OR operation through the interaction of a Leaky Integrate-and-Fire (LIF) output neuron and memristive synapses modeled using the Biolek formulation. Two pulsed inputs are transmitted through memristive devices whose conductance serves as the synaptic weights. A supervised Hebbian training strategy, implemented in MATLAB, is employed to adjust the synaptic conductance according to the Hebbian delta rule. The proposed framework demonstrates how elementary logical computation may arise from biologically motivated learning processes embedded in hardware-realistic neuromorphic circuits. The study provides a foundational demonstration of learning and logic inference in a minimal neuromorphic architecture, supporting future work aimed at more complex adaptive systems.
URL: https://digitalcommons.calpoly.edu/eesp/722