A Traveling Wave Basis for Coding Touch- Unraveling recurrent and translaminar circuit contributions to sensory-evoked traveling waves

Abstract

Traveling waves (TWs) of neural activity have recently been discovered to occur in single regions of cortex during normal sensory processing. For example, in visual or somatosensory cortex, sensory stimuli evoke a wave of activity traveling outward from the point of maximal input. The circuits underlying TWs and their relevance to behavior, however, remain unmapped. One hypothesis is that the mechanism for TWs is fiber delays due to long-range axons traversing superficial layers in cortex. However, translaminar cortical circuits receive long-range inputs that are critical for sensory processing and prediction-driven anticipatory coordination. The dynamics and accompanying functional cellular landscape, as well as the contribution of translaminar circuits to TW generation remains unknown. Using our custom-fabricated 3D transparent NanoNeedle electrode array, we propose to perform electrical recordings of extracellular local-field-potentials and concomitant two-photon (2P) optical imaging across the barrel cortex of awake mice to test the mechanisms underlying touch-evoked travelling waves. Preliminary results reveal both an early feed-forward driven wave and late (~100-400 ms) reverberating wave upon whisker touch. This wave is observed to be accompanied by a sparse L2-3 ensemble structure and precisely timed L5 firing. A large-scale spiking network model recapitulates this sparse structure theory of wave propagation. Given the delays involved, we hypothesize that recurrent and top-down feedback inputs work together to open a window of time that enhances the integration of feedforward tactile information with higher-order context. We posit that internally generated sensory predictions in the barrel cortex could facilitate tactile integration via precisely timed TWs elicited by top-down inputs. Our objectives are focused on teasing apart the circuit mechanisms underlying this wave dynamic under goal-directed processing.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 14, 2024
Source ID
FA95502310701

Entities

People

  • Krishna Jayant

Organizations

  • Air Force Office of Scientific Research
  • Purdue University
  • United States Air Force

Tags

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Neural Network Machine Learning.
  • Neuroscience