A cortical neural prosthesis for restoring and enhancing memory

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.

Figure 1

Top, diagram of DNMS task (clockwise from top). Sample phase starts with single lever extended in one position. SR encoding strength (recorded via array electrodes in CA3 and CA1, center right) predicts delay-sensitive (red arrow) nonmatch response (left). Following random delay, both levers are extended in nonmatch phase. Nonmatch decision requires animal to press lever opposite to SR. Spatiotemporal patterns of ensemble firing are illustrated in color contour maps showing differential firing to left versus right SRs which occurred for 3.0–5.0 s following sample lever presentation (SP). Maps depict firing rate of neurons (vertical axis) by time (horizontal axis) for same 5.0 s period. Overall feedback loop for trials is closed by behavioral outcome based on correct or error recall of sample lever position. Bottom left: performance (mean percentage correct ± SEM) curve illustrates sensitivity of behavior to duration of delay interval under control conditions (n = 23). A unilateral cannula located in CA3 is shown next to array to indicate the site of drug infusion to alter task-related firing (figure 4).

Figure 2

Closed loop feedback using MIMO identification of SR encoding strength to control DNMS performance. Top: CA3 and CA1 neuronal firing (contour, left) analyzed via MIMO model (center) predicts CA1 firing patterns (contour, right). Strength of SR code in CA1 during sample phase predicts behavioral outcome (nonmatch decision) based on prior correlation of firing pattern with success. Closed loop (light blue arrow) reduces (CL-weak codes), or lengthens (CL-strong codes) the delay duration on the same trial to validate MIMO model prediction of strength of the SR code. Bottom right: mean strong and weak code patterns of probability of neural firing at each neuron position (array recording site) and time relative to SR. Color contours scaled from blue (<10%) to red (>60%) according to probability of neural firing on a single trial. Bottom left: mean DNMS performance across animals (n = 15) compares performance on closed loop MIMO model-detected strong (CL-strong codes) and weak (CL-weak codes) SR codes to non-closed loop trials (control). Performance on non-closed loop trials with detected weak codes is displayed as well (weak codes). Performance on trials in which MIMO coefficients were scrambled (scrambled coefficients) is also shown.

Figure 3

MIMO model controlled stimulation codes. Top: prediction of ‘weak’ SR encoding in CA1 via MIMO model results in CA1 electrical stimulation with spatio-temporal patterns corresponding to strong SR code (green raster ‘substitution’, top right) predicted by prior closed loop MIMO model applications (figure 2). CA1 stimulation consisted of biphasic electrical pulses (0.5–2.0 V, 10–50 μV, 0.5 s duration) delivered no more than once per 50 ms per channel, delivered on trials with delay durations of 15–50s. Bottom: comparison of mean DNMS performance (% correct ±SEM) across animals (n = 23) on trials with interposed CA1 stimulation patterns derived from MIMO generated strong SR codes (stim MIMO model) vs. trials on which no stimulation (no stim) was delivered. Trials in which stimulation at the same intensity was generated from scrambled MIMO model CA1 coefficients (figure 2) are also shown.

Figure 4

MIMO model repair of hippocampal encoding with previously effective CA1 stimulation patterns delivered at the time of the SR. Top: intrahippocampal infusion (see figure 1, inset center right, cannula shown entering CA3 next to electrode array on right) of glutamatergic antagonist MK801 for 14 days suppressed MIMO derived ensemble firing in CA1 and CA3 (red ‘X’, left). Strong SR code patterns derived from previous DNMS trials were delivered bilaterally to the same CA1 locations when the SR occurred in the sample phase. Bottom: mean percentage correct (±SEM) performance (n = 5 animals) summed over all conditions, including vehicle-infusion (control) without stimulation, MK801 infusion (MK801) without stimulation, and MK801 infusion with the CA1 stimulation (substitute) pattern (MK801 + Stim).

Figure 5

Reverse stimulation to verify specificity of closed loop MIMO model stimulation. Top: validity of CA1 ‘strong SR code’ patterns determined by delivering stimulation patterns appropriate for the opposite type of DNMS trial (‘reversed’) or by varying the time at which stimulation was delivered relative to the occurrence of the SR (‘delayed’). Bottom left: DNMS trials in which the SR code for the opposite lever was delivered (reversed stim) and compared with trials within the same sessions in which SR codes were delivered appropriate for the lever presented (normal stim) as well as control (no stimulation) trials. Bottom right: DNMS delay curves for animals stimulated with MIMO model generated strong SR codes either at the time of the SR (stim at SR) or commencing 3–5 s after the SR (stim late).