Role of the hippocampus in memory formation: restorative encoding memory integration neural device as a cognitive neural prosthesis.

Remind, which stands for "restorative encoding memory integration neural device," is a Defense Advanced Research Projects Agency (DARPA)-sponsored program to construct the first-ever cognitive prosthesis to replace lost memory function and enhance the existing memory capacity in animals and, ultimately, in humans. Reaching this goal involves understanding something fundamental about the brain that has not been understood previously: how the brain internally codes memories. In developing a hippocampal prosthesis for the rat, we have been able to demonstrate a multiple-input, multiple- output (MIMO) nonlinear model that predicts in real time the spatiotemporal codes for specific memories required for correct performance on a standard learning/memory task, i.e., delayed-nonmatch-to-sample (DNMS) memory. The MIMO model has been tested successfully in a number of contexts; most notably, in animals with a pharmacologically disabled hippocampus, we were able to reinstate long-term memories necessary for correct DNMS behavior by substituting a MIMO model-predicted code, delivered by electrical stimulation to the hippocampus through an array of electrodes, resulting in spatiotemporal hippocampal activity that is normally generated endogenously. We also have shown that delivering the same model-predicted code to electrode-implanted control animals with a normally functioning hippocampus substantially enhances animals memory capacity above control levels. These results in rodents have formed the basis for extending the MIMO model to nonhuman primates; this is now underway as the last step of the REMIND program before developing a MIMO-based cognitive prosthesis for humans.

A hippocampal cognitive prosthesis: multi-input, multi-output nonlinear modeling and VLSI implementation.

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the "core" of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate 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. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals.

VLSI implementation of a nonlinear neuronal model: a "neural prosthesis" to restore hippocampal trisynaptic dynamics.

We are developing a biomimetic electronic neural prosthesis to replace regions of the hippocampal brain area that have been damaged by disease or insult. We have used the hippocampal slice preparation as the first step in developing such a prosthesis. The major intrinsic circuitry of the hippocampus consists of an excitatory cascade involving the dentate gyrus (DG), CA3, and CA1 subregions; this trisynaptic circuit can be maintained in a transverse slice preparation. Our demonstration of a neural prosthesis for the hippocampal slice involves: (i) surgically removing CA3 function from the trisynaptic circuit by transecting CA3 axons, (ii) replacing biological CA3 function with a hardware VLSI (very large scale integration) model of the nonlinear dynamics of CA3, and (iii) through a specially designed multi-site electrode array, transmitting DG output to the hardware device, and routing the hardware device output to the synaptic inputs of the CA1 subregion, thus by-passing the damaged CA3. Field EPSPs were recorded from the CA1 dendritic zone in intact slices and "hybrid" DG-VLSI-CA1 slices. Results show excellent agreement between data from intact slices and transected slices with the hardware-substituted CA3: propagation of temporal patterns of activity from DG-->VLSI-->CA1 reproduces that observed experimentally in the biological DG-->CA3-->CA1 circuit.

An algorithm for real-time extraction of population EPSP and population spike amplitudes from hippocampal field potential recordings.

A new method is presented for extracting the amplitude of excitatory post synaptic potentials (EPSPs) and spikes in real time. It includes a low pass filter (LPF), a differentiator, a threshold function, and an intelligent integrator. It was applied to EPSP and population spike data recorded in the Dentate Gyrus and the CA1 hippocampus in vitro. The accuracy of the extraction algorithm was evaluated via the extraction normalized mean square error (eNMSE) and was found to be very high (eNMSE < 5%). The preservation of neuronal information was confirmed using the Volterra-Poisson modeling approach. Volterra-Poisson kernels were computed using amplitudes extracted with both proposed and traditional methods. The accuracy of the computed kernels and the resulting model was evaluated via the prediction normalized mean square error (pNMSE) and was found to be very high (pNMSE < 5%). The similarity between the kernels computed when the proposed method was used to extract the field potential amplitude and their counterparts when the traditional method was used to extract the field potential amplitude confirms the preservation of the neuronal dynamics. The proposed method represents a new class of real time field potential amplitude extraction algorithms with complexity that can be included in hardware implementations.