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In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.

 

Abstract Human cortical organoids, three-dimensional neuronal cultures, are emerging as powerful tools to study brain development and dysfunction. However, whether organoids can functionally connect to a sensory network in vivo has yet to be demonstrated. Here, we combine transparent microelectrode arrays and two-photon imaging for longitudinal, multimodal monitoring of human cortical organoids transplanted into the retrosplenial cortex of adult mice. Two-photon imaging shows vascularization of the transplanted organoid. Visual stimuli evoke electrophysiological responses in the organoid, matching the responses from the surrounding cortex. Increases in multi-unit activity (MUA) and gamma power and phase locking of stimulus-evoked MUA with slow oscillations indicate functional integration between the organoid and the host brain. Immunostaining confirms the presence of human-mouse synapses. Implantation of transparent microelectrodes with organoids serves as a versatile in vivo platform for comprehensive evaluation of the development, maturation, and functional integration of human neuronal networks within the mouse brain. 

Real-time spike sorting with large data throughput is essential for studying neural dynamics and brain-machine interfaces. Neural recordings from high-density multi-electrode arrays that consist of hundreds of electrodes impose stringent demands on spike sorting hardware regarding data transmission bandwidth and computation complexity. That leads to an urgent need for specialized hardware with high throughput, low power, and latency. Here, we present a real-time spike sorting processor that utilizes high-density BEOL-integrable CuO x resistive crossbars to perform in-memory spike segregation. We experimentally demonstrate, for the first time, efficient hardware implementation of spike sorting from in vivo extracellular recordings with high accuracy. Our neuromorphic interface promises substantial performance gains ( ∼1000×less area,∼200×less power,4.8 μs latency for sorting 100 channels) for in vivo real-time spike sorting.