Conference proceeding
Learning Spiking Neural Network Models of Drosophila Olfaction
International Conference on Neuromorphic Systems 2020, pp 1-5
28 Jul 2020
Abstract
We present research in the modeling of neurons within Drosophila (fruit fly) olfaction. We describe the process from data collection, to model creation, and spike generation. Our approach utilizes computational elements such as spiking neural networks that employ leaky integrate-and-fire neurons with adaptive firing behavior that more closely mimick biological neurons. We describe the methods of several learning implementations in both software and hardware. Finally, we present both quantitative and qualitative results on learning spiking neural network models.
Metrics
25 Record Views
5 citations in Scopus
Details
- Title
- Learning Spiking Neural Network Models of Drosophila Olfaction
- Creators
- John Carter - Drexel UniversityJocelyn Rego - Drexel UniversityDaniel Schwartz - Drexel UniversityVikas Bhandawat - Drexel UniversityEdward Kim - Drexel University
- Publication Details
- International Conference on Neuromorphic Systems 2020, pp 1-5
- Series
- ICONS 2020
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science; School of Biomedical Engineering, Science, and Health Systems
- Scopus ID
- 2-s2.0-85091494864
- Other Identifier
- 991019173445404721