Augmenting Learning and Memory through Photothermal Nano-Neuromodulation

Abstract

Controlling selective population of neurons to understand and influence behavior is a grand challenge in systems neuroscience. While the optogenetic techniques and other optical neural control techniques are promising, there are still several limitations associated with these techniques that remain to be addressed. These include- (i) ability to excite neurons that are embedded deep in the tissue; (ii) ability to be widely used in different model organisms with or without a rich repertoire of genetic tools; (iii) graded control of neurons; (iv) ability to control different subset of neurons in a concurrent fashion; (v) reversibility of the proposed approaches to return the controlled neurons to their original configuration; and (vi) more importantly feasibility of developing a non-invasive approach. As a part of the prior AFOSR-supported project (FA9550-19-1-0394), we have demonstrated the reversible modulation of neural activity using photothermal nanostructures. We have observed a complex interplay between nano-neuromodulation and homeostatic regulatory mechanism of the neural network. We have also demonstrated a significant enhancement in the olfaction performance of an invertebrate model with photothermal neuromodulation. Building on our prior work, the ultimate goal of this project is to understand the mechanistic aspects of photothermal nano-neuromodulation and demonstrate the enhancement in the learning and memory capabilities of a model organism through targeted and well-controlled photothermal neuromodulation.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 06, 2024
Source ID
FA95502310461

Entities

People

  • Srikanth Singamaneni

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • Washington University in St. Louis

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Nanoscale Plasmonic Nanotechnology
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Biotechnology
  • Microelectronics