Closing the Loop for Memory Prosthesis: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

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 spatiotemporal 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.

Model-based asessment of an in-vivo predictive relationship from CA1 to CA3 in the rodent hippocampus

“…This is not surprising given that the primary output of both regions is provided by principal cells. In fact, the set of highly predictive principal cells may correspond to the so-called functional cell types which selectively fire in response to a specific type of stimulus in the DNMS task (ie left-nonmatch) (Hampson et al, 1999, 2012a,b; Goonawardena et al, 2010). Given that these cells are found both in CA3 and CA1 it is clear that these cells would have predictive power over each other.…”

Extraction of Cortical Modularity Patterns for Neural Prosthetics

“…19.4d) as a function of trial difficulty (number of images) on MIMO stimulation versus nonstimulation trials. The controls associated with these results are found in Hampson et al (2012b).…”

“…In prior studies (Berger et al 2011;Hampson et al 2012bOpris et al 2013), it has been shown that a multi-input multi-output model (MIMO) nonlinear dynamic model applied to spatiotemporal patterns of multiple recordings from synaptically connected neurons is capable of extracting patterns of firing related to successful task performance of this same task shown in Fig. 19.1a.…”

Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ9-tetrahydrocannabinol administration

“…The nonlinear multi-input, multi-output model (MIMO) is an effective predictor of hippocampal memory encoding (Hampson et al, 2012), and it has been successfully used as a neuroprosthetic to improve memory performance in both rats (Hampson et al, 2012) and nonhuman primates (Hampson et al, 2013). FCTs have been shown to significantly contribute to the MIMO hippocampal model (Hampson et al, 2012) suggesting that behavioral correlates of individual hippocampal neurons exhibit nonlinear and possibly fractal features in their spike trains.…”

“…FCTs have been shown to significantly contribute to the MIMO hippocampal model (Hampson et al, 2012) suggesting that behavioral correlates of individual hippocampal neurons exhibit nonlinear and possibly fractal features in their spike trains. Hippocampal neuronal ensembles have previously been pre-screened using z -score based FCT identification, but since this method employs subjective decisions during computation, it is hypothesized that multifractal analysis could provide an objective measure of the information processing capacity of hippocampal neurons.…”