Closing the loop for memory prosthesis: Detecting the role of hippocampal neural ensembles using nonlinear models

Robert E. Hampson, Dong Song, Rosa H.M. Chan, Andrew J. Sweatt, Mitchell R. Riley, Anushka V. Goonawardena, Vasilis Z. Marmarelis, Greg A. Gerhardt, Theodore W. Berger, Sam A. Deadwyler

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatio-temporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the "strength" of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary "normal" encoding as a means of understanding how neural ensembles can be "tuned" to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry.

Original languageEnglish
Article number6179544
Pages (from-to)510-525
Number of pages16
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume20
Issue number4
DOIs
StatePublished - 2012

Bibliographical note

Funding Information:
Manuscript received August 07, 2011; revised November 30, 2011; accepted January 17, 2012. Date of publication April 06, 2012; date of current version July 03, 2012. This work was supported in part by the Defense Advanced Research Projects Agency under Contract N66601-09-C-2080 and Contract N66601-09-C-2081, in part by the National Science Foundation under Grant EEC-0310723, in part by the National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering (NIH/NIBIB) under Grant P41-EB001978, in part by the Biomedical Simulations Resource at USC, and in part by the NIH under Grant R01DA07625. The views, opinions, and/or findings contained in this paper are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.

Keywords

  • Closed-loop control
  • hippocampal ensembles
  • nonlinear math model
  • relation to normal encoding patterns
  • short-term memory task

ASJC Scopus subject areas

  • Rehabilitation
  • General Neuroscience
  • Internal Medicine
  • Biomedical Engineering

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