Kurtis D. Cantley

Orcid: 0000-0003-0254-4346

According to our database1, Kurtis D. Cantley authored at least 12 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Visual Analysis of Leaky Integrate-and-Fire Spiking Neuron Models and Circuits.
Proceedings of the 67th IEEE International Midwest Symposium on Circuits and Systems, 2024

2023
Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks.
Proceedings of the 66th IEEE International Midwest Symposium on Circuits and Systems, 2023

2020
A Model for $R(t)$ Elements and $R(t)$ -Based Spike-Timing-Dependent Plasticity With Basic Circuit Examples.
IEEE Trans. Neural Networks Learn. Syst., 2020

2019
A Spatiotemporal Pattern Detector.
Proceedings of the 62nd IEEE International Midwest Symposium on Circuits and Systems, 2019

Radiation Effect on Learning Behavior in Memristor-Based Neuromorphic Circuit.
Proceedings of the 62nd IEEE International Midwest Symposium on Circuits and Systems, 2019

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation.
Proceedings of the International Conference on Neuromorphic Systems, 2019

2017
Signal-to-noise ratio enhancement using graphene-based passive microelectrode arrays.
Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems, 2017

A CMOS synapse design implementing tunable asymmetric spike timing-dependent plasticity.
Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems, 2017

Spatio-temporal pattern recognition in neural circuits with memory-transistor-driven memristive synapses.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2013
Low-Temperature Fabrication of Spiking Soma Circuits Using Nanocrystalline-Silicon TFTs.
IEEE Trans. Neural Networks Learn. Syst., 2013

2012
Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors.
IEEE Trans. Neural Networks Learn. Syst., 2012

Noise effects in field-effect transistor biological sensor detection circuits.
Proceedings of the 55th IEEE International Midwest Symposium on Circuits and Systems, 2012


  Loading...