Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts. Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts. Main results. We applied our method to high-density multi-electrode recordings from the primate retina in an ex vivo setup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability ( R 2 = 0.951 for human 1 and R 2 = 0.944 for human 2). Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.

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Future high-density and high channel count neural interfaces that enable simultaneous recording of tens of thousands of neurons will provide a gateway to study, restore and augment neural functions. However, building such technology within the bit-rate limit and power budget of a fully implantable device is challenging. The wired-OR compressive readout architecture addresses the data deluge challenge of a high channel count neural interface using lossy compression at the analog-to-digital interface. In this article, we assess the suitability of wired-OR for several steps that are important for neuroengineering, including spike detection, spike assignment and waveform estimation. For various wiring configurations of wired-OR and assumptions about the quality of the underlying signal, we characterize the trade-off between compression ratio and task-specific signal fidelity metrics. Using data from 18 large-scale microelectrode array recordings in macaque retina ex vivo, we find that for an event SNR of 7-10, wired-OR correctly detects and assigns at least 80% of the spikes with at least 50× compression. The wired-OR approach also robustly encodes action potential waveform information, enabling downstream processing such as cell-type classification. Finally, we show that by applying an LZ77-based lossless compressor (gzip) to the output of the wired-OR architecture, 1000× compression can be achieved over the baseline recordings.

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This paper investigates the efficacy of a wired-OR compressive readout architecture for neural recording, which enables simultaneous data compression of action potential signals for high channel count electrode arrays. We consider a range of wiring configurations to assess the trade-offs between compression ratio and various task-specific signal fidelity metrics. We consider the fidelity in threshold crossing detection, spike assignment, and waveform estimation, and find that for an event SNR of 7-10 the readout captures at least 80% of the spike waveforms at ∼150x data compression.@en

Brain-machine interfacesBrain-machine interface (BMIs) of the future will be used to treat diverse neurological disorders and augment human capabilities. However, to realize this futuristic promise will require a major leap forward in how electronic devices interact with the nervous system. Current BMIs provide coarse communicationCommunication with the target neural circuitry, because they fail to respect its cellular and cell-type specificity. Instead, they indiscriminately activate or record many cells at the same time and provide only partial restoration of lost abilities. A future BMI that may pave the path forward is an artificialRetina retinaArtificial retina---a device that can restore vision to people blinded by retinal degeneration. Because the retina is relatively well understood and easily accessible, it is an ideal neural circuit in which to develop a BMI that can approach or exceed the performance of the biological circuitry. This chapter summarizes the basic neuroscienceNeuroscience of vision, identifies the requirements for an effective retinal interface, and describes some of the necessary circuits and systems. Based on these ideas and the lessons from first-generation retinal prosthesesBionic, a novel neuroengineering approach is proposed: the first BMI that will interact with neural circuitry at cellular and cell-type resolution.@en

Neural interfaces of the future will be used to help restore lost sensory, motor, and other capabilities. However, realizing this futuristic promise requires a major leap forward in how electronic devices interface with the nervous system. Next generation neural interfaces must support parallel recording from tens of thousands of electrodes within the form factor and power budget of a fully implanted device, posing a number of significant engineering challenges. In this paper, we exploit sparsity and diversity of neural signals to achieve simultaneous data compression and channel multiplexing for neural recordings. The architecture uses wired-OR interactions within an array of single-slope A/D converters to obtain massively parallel digitization of neural action potentials. The achieved compression is lossy but effective at retaining the critical samples belonging to action potentials, enabling efficient spike sorting and cell type identification. Simulation results of the architecture using data obtained from primate retina ex-vivo with a 512-channel electrode array show average compression rates up to  ∼ 40× while missing less than 5% of cells. In principle, the techniques presented here could be used to design interfaces to other parts of the nervous system.

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This paper describes an architecture for the massively parallel digitization of neural action potentials. The scheme achieves simultaneous data compression and channel multiplexing through wired-OR interactions within an array of single-slope A/D converters. The achieved compression is lossy but effective at retaining the critical samples belonging to action potential spikes. Simulation results using ex-vivo experimental data from a 512-channel array show compression rates up to ∼73x while maintaining ≥90% reconstruction coverage for parasol cells in the primate retina.

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