scispace - formally typeset
Open AccessJournal ArticleDOI

A cortical neural prosthesis for restoring and enhancing memory

TLDR
These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.
Abstract
A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

TL;DR: Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.
Journal ArticleDOI

Could a Neuroscientist Understand a Microprocessor

TL;DR: It is shown that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor, suggesting current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data.
Journal ArticleDOI

Towards reliable spike-train recordings from thousands of neurons with multielectrodes

TL;DR: Some of the challenges that must be met to achieve the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms are described.
Book

Brain-Computer Interfacing: An Introduction

TL;DR: This introduction to the field is designed as a textbook for upper- level undergraduate and first year graduate courses in neural engineering or brain- computer interfacing for students from a wide range of disciplines.
Journal ArticleDOI

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

TL;DR: The development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus, and the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time is demonstrated.
References
More filters
Journal ArticleDOI

The Neurobiological Basis of Cognition: Identification by Multi-Input, Multioutput Nonlinear Dynamic Modeling

TL;DR: This work's primary thesis is that the encoding of short-term memories into new, long- term memories represents the collective set of nonlinearities induced by the three or four principal subsystems of the hippocampus, i.e., entorhinal cortex-to-dentate gyrus, dentategyrus- to-CA3 pyramidal cell region, CA3-to_subicular cortex, and CA1-to the subicular cortex.
Journal ArticleDOI

Modeling of neural systems by use of neuronal modes

TL;DR: The authors present and demonstrate a method by which the NMs are determined using the 1stand 2nd-order kernel estimates of the system, obtained from input-output data, which may provide a common methodological framework for modeling a certain class of neural systems.
Journal ArticleDOI

Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

TL;DR: A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons.
Journal ArticleDOI

General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

TL;DR: A general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification is presented.

Nonlinear modeling of causal interrelationships in neuronal ensembles: An application to the rat hippocampus

TL;DR: In this article, a multiple-input/multiple-output (MIMO) model is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons.
Related Papers (5)