Role of Noise in the Neuronal Ability to Encode and Classify Signals
Location
CSU Ballroom
Start Date
12-4-2022 10:00 AM
End Date
12-4-2022 11:30 AM
Student's Major
Physics and Astronomy
Student's College
Science, Engineering and Technology
Mentor's Name
Jorge Mendez
Mentor's Department
Physics and Astronomy
Mentor's College
Science, Engineering and Technology
Description
The brain is an intrinsically noisy environment. Neurons and networks are able to detect and classify different natural signals. The specific role played by noise in the codification of information is broadly unknown. The common picture of noise as a factor that can only cause a deterioration of the information in the signal is coming from linear systems. Most of the neurons in the brain and the mathematical models to represent them operate as excitable systems, which are nonlinear systems. Once nonlinearities are considered, noise can improve the codification of the signals. These improvements are usually called stochastic resonances. The first described stochastic resonance was obtained as an improvement in the response of a nonlinear system to a periodic input signal. This idea was later extended to more naturalistic relevant signals (aperiodic input signals), and referred as aperiodic stochastic resonance. In this study, we explore the significance of aperiodic stochastic resonance for the codification and classification of chaotic signals by a single neuron modeled using well established neuronal mathematical models. We analyze different metrics to quantify stochastic resonances, and the connection to a machine learning classification problem.
Role of Noise in the Neuronal Ability to Encode and Classify Signals
CSU Ballroom
The brain is an intrinsically noisy environment. Neurons and networks are able to detect and classify different natural signals. The specific role played by noise in the codification of information is broadly unknown. The common picture of noise as a factor that can only cause a deterioration of the information in the signal is coming from linear systems. Most of the neurons in the brain and the mathematical models to represent them operate as excitable systems, which are nonlinear systems. Once nonlinearities are considered, noise can improve the codification of the signals. These improvements are usually called stochastic resonances. The first described stochastic resonance was obtained as an improvement in the response of a nonlinear system to a periodic input signal. This idea was later extended to more naturalistic relevant signals (aperiodic input signals), and referred as aperiodic stochastic resonance. In this study, we explore the significance of aperiodic stochastic resonance for the codification and classification of chaotic signals by a single neuron modeled using well established neuronal mathematical models. We analyze different metrics to quantify stochastic resonances, and the connection to a machine learning classification problem.
Recommended Citation
Zakariya, Mohamed. "Role of Noise in the Neuronal Ability to Encode and Classify Signals." Undergraduate Research Symposium, Mankato, MN, April 12, 2022.
https://cornerstone.lib.mnsu.edu/urs/2022/poster-session-01/17
