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A cortical neural prosthesis for restoring and enhancing memory

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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.

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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.
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Could a Neuroscientist Understand a Microprocessor

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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
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TL;DR: MK-801 blocked selectively the N-methyl-D-aspartate receptor-mediated component of synaptic transmission, which can be recorded in response to single shock stimulation of the Schaffer collateral-commissural pathway in the absence of added Mg2+ to the perfusate.
Journal ArticleDOI

Nonlinear Dynamic Modeling of Spike Train Transformations for Hippocampal-Cortical Prostheses

TL;DR: This paper has formulated the transformational process of multi-site propagation of spike activity between two subregions of the hippocampus as the identification of a multiple-input, multiple-output (MIMO) system, and proposed that it can be decomposed into a series of multiple- input, single- output (MISO) systems.
Journal ArticleDOI

Ensemble Activity and Behavior—What's the Code?

TL;DR: Recent results that illustrate how the brain may code behavior in ensembles of neurons are described, which can now be monitored by multielectrode arrays in behaving animals.
Journal ArticleDOI

A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

TL;DR: In this article, a simple learning rule that can reproduce the effect of motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters was proposed. But it does not require extrinsic information to separate noise from signal.
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