Cortical Neural Prosthesis Performance Improves When …

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.

T1 - Cortical neural prosthesis performance improves when eye position is monitored

The above demonstrations show that neural prostheses employing connections between different neuronal assemblies can be applied to accurately assess and impose effective spatiotemporal task-related firing patterns which can be used for (1) closed loop control of performance (), (2) electrical stimulation of the same structures in the manner that mimics output of successful task-related signals to facilitate and override instances in which less effective ensemble firing occurs (), (3) recovery of function when the same ensembles are no longer operative () and finally (4) validating whether the facilitative patterns of stimulation are specific to encoding of task-related events necessary for successful performance within specific contexts (). This type of utility provided by the MIMO model and applied to a well-characterized set of ensemble firings provides one of the first demonstrations in which deciphered nonlinear activation patterns across cell assemblies can be utilized to deliver facilitative electrical stimulation to those same brain regions (). Such an approach takes advantage of the fact that input–output relations are all that are required to make the predictions, irrespective of whether those relations reflect mono- or polysynaptic connections. Therefore, even if hippocampal CA3 connectivity to CA1 were not present, the MIMO model could be applied by characterizing input–output relations between dentate gyrus (and/or entorhinal cortex) and CA1 outputs if the former structures remained operative when CA3 was damaged. In addition, by reversing the encoding patterns delivered for critical behavioral events within the task (), it was possible to test the specificity of the effectiveness of the neural prosthesis for replacing damaged or destroyed connections (, ), as shown in . While some factors remain to be determined as to the basis for the success of this approach (i.e. range of effective stimulation parameters, etc.), and even though opportunities for negative results were provided, the consistency of the outcomes of each of the above test conditions confirm the basic assumption of predictive ensemble encoding as performed by the MIMO model. It was not necessary to understand the basis for firing patterns in CA1 predicted by CA3 in terms of encoded information, even though in the past each structure has been implicated independently in performance of the task from many different perspectives, including hippocampal removal, drug effects and time course of behavioral acquisition (, , ). In addition, previous offline assessments of data from the same DNMS task employing the exact same MIMO model (, ) provide substantial computational support for the online predictions of CA1 output firing patterns based on CA3 cell input firing to control online delivery of facilitative SR stimulation patterns ( and ).


Cortical neural prosthesis performance improves when …

This paper describes the initial demonstration of a cortical neural prosthesis applied to information processing of two subregions of the hippocampus involved in the formation of long-term memory. These memories were essential for successful performance of a cognitive task requiring encoding and retrieval of information on a trial-by-trial basis. The neural structure tested by the device, the rodent hippocampus, has been extensively characterized with respect its role in performance of a delayed-nonmatch-to-sample (DNMS) working memory task (, , , , , ) and offline computational assessment of firing relationships during that performance (, ). Such characterization provided the substrate for online application of the device to extract critical functional processing from ensembles of hippocampal neurons required for the performance of the task (, ), and to establish when not present, information processing necessary for successful completion of the task (, ). Here we demonstrate how this prototype of a cortical prosthesis is capable of (1) monitoring the input pattern to hippocampus during the information encoding phase of the task, (2) predicting accurately the associated hippocampal output pattern and the degree of success related to such encoding, and (3) delivering stimulation with electrical pulses during the same phase of the task in a pattern that conforms to the normal firing of the hippocampal output region on successful trials. The utility of this cortical prosthesis is further demonstrated by the capacity to substitute successful encoding stimulation when hippocampal ensembles are compromised and cannot generate the necessary codes to perform the task successfully.


People – Laboratory of Neural Prosthetic Research

Objective. In order to move forward with the development of a cortical vision prosthesis, the critical issues in the field must be identified. Approach. To begin this process, we performed a brief review of several different cortical and retinal stimulation techniques that can be used to restore vision. Main results. Intracortical microelectrodes and epicortical macroelectrodes have been evaluated as the basis of a vision prosthesis. We concluded that an important knowledge gap necessitates an experimental in vivo performance evaluation of microelectrodes placed on the surface of the visual cortex. A comparison of the level of vision restored by intracortical versus epicortical microstimulation is necessary. Because foveal representation in the primary visual cortex involves more cortical columns per degree of visual field than does peripheral vision, restoration of foveal vision may require a large number of closely spaced microelectrodes. Based on previous studies of epicortical macrostimulation, it is possible that stimulation via surface microelectrodes could produce a lower spatial resolution, making them better suited for restoring peripheral vision. Significance. The validation of epicortical microstimulation in addition to the comparison of epicortical and intracortical approaches for vision restoration will fill an important knowledge gap and may have important implications for surgical strategies and device longevity. It is possible that the best approach to vision restoration will utilize both epicortical and intracortical microstimulation approaches, applying them appropriately to different visual representations in the primary visual cortex.