2019 Graduate Student Showcase

Title of Submission

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

Degree Program

Electrical and Computer Engineering, PhD

Major Advisor Name

Kurtis Cantley

Type of Submission

Scholarly Poster

Judging Session

Both Judging Sessions


In this paper, a feed-forward memristor-based spiking neural network is taught to separate correlated and uncorrelated synapse and learn character inputs using spike-timing-dependent plasticity (STDP). A TiO2 non-linear drift memristor model is used to simulate a neuromorphic circuit with 25 pre- and 1 post-synaptic neuron. During the learning process, memristors are radiated with state-altering radiation and the effect on circuit learning behavior is determined. It is observed that the network recovers when radiation ceases but takes longer to resolve the correlation. Further, at lower but continuous radiation exposure, the circuit may resolve the pattern indefinitely.

Funding Information

The Defense Threat Reduction Agency (DTRA) grant HDTRA1-17-1-0036

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