Deciphering neural representations of sensory information by cracking the neural phase code
Abstract
How information, such as sensory information about the external environment, is represented in neural activity is a fundamental question of neuroscience. We hypothesize that significant information is encoded by the timing of neuron s electrical signals relative to widespread fluctuations in neural activity (phase coding). Indeed, previous investigations have revealed the existence of such phase-coded information relative to the frequency-filtered local field potential in a variety of brain areas; however, the amount of information measured in this type of code has been relatively small. This may be because local field potentials as recorded from the brain reflect a combination of multiple sources. We propose that the novel application of mathematical signal decomposition methods to this data can reveal the true magnitude of phase-coded information beyond what has previously been reported. Here we show that using mathematical methods to break down the recorded signal can reveal additional phase-coded information. We develop and use these methods to build a decoder of neural activity exploiting this phase-coded information to read out sensory information from neural recordings. We will test the generalizability of those techniques and the strength of phase coding across multiple tasks and visual areas. The results of these experiments will fundamentally advance our understanding of the neural code by which the brain represents and transmits information. Accurately measuring phase-coded information will allow us to better understand the computations being performed across many tasks and brain areas. It will also have numerous technological applications, dramatically improving our ability to read out information from brain activity in brain-machine interfaces, providing more accurate substitutes for natural neural signals in neural prostheses, and enhancing the capabilities of autonomous and intelligent systems by enabling brain-like sensation, control and adaptation.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410235
Entities
People
- Neda Nategh
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Utah