Future neural interfaces will read and write population neural activity with high spatial and temporal resolution, for diverse applications. For example, an artificial retina may restore vision to the blind by electrically stimulating retinal ganglion cells. Such devices must tune their function, based on stimulating and recording, to match the function of the circuit. However, existing methods for characterizing the neural interface scale poorly with the number of electrodes, limiting their practical applicability. This work tests the idea that using prior information from previous experiments and closed-loop measurements may greatly increase the efficiency of the neural interface. Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina. Three key calibration steps were optimized: spike sorting in the presence of stimulation artifacts, response modeling, and adaptive stimulation. For spike sorting, exploiting the similarity of electrical artifact across electrodes and experiments substantially reduced the number of required measurements. For response modeling, a joint model that captures the inverse relationship between recorded spike amplitude and electrical stimulation threshold from previously recorded retinas resulted in greater consistency and efficiency. For adaptive stimulation, choosing which electrodes to stimulate based on probability estimates from previous measurements improved efficiency. Similar improvements resulted from using either non-adaptive stimulation with a joint model across cells, or adaptive stimulation with an independent model for each cell. Finally, image reconstruction revealed that these improvements may translate to improved performance of an artificial retina.

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network.