Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain–computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.

Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.

Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila central nervous system and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

This article presents a networked wireless telemetry for 384-sensor-pixel neural data acquisition, designed to support high throughput, low power consumption, and meter-scale communication distance. The proposed event-based Address-Event- $\mu $ -Packet (AE $\mu $ P) encoder with differential binary phase shift keying (DBPSK) modulation addresses the challenging throughput-power trade-off for neural telemetry without compromising transmission range and neural signal fidelity. The encoder compresses data and minimizes protocol overhead as well as memory access. In addition, a duty-cycling impulse-radio ultra-wideband (IR-UWB) transmitter is proposed as a communication link for the event-based encoder. To support the multiaccess operation, a duty-cycling frequency-locked loop (FLL) is adopted to provide fast-settling packet-level frequency control for frequency division multiplexing (FDM). The FLL achieves warm-start lock within $1~\mu $ s with 2.73-MHz rms variation, meeting the frequency accuracy required for DBPSK at a 125-MHz symbol rate. The measured three-access-node spectra comply with Federal Communications Commission (FCC)/ETSI masks in 6–8.5GHz. The neural signal integrity is validated by evaluating the rms error between the reconstruction signals and the original in vivo data captured by a high-pixel-count Neuropixels probe. The presented AE $\mu $ P achieves an $11.52\times $ compression ratio. Fabricated in 65nm CMOS with an active area of 0.12mm², the presented telemetry is capable of transmitting 384 pixels of neural data with low compression error (3.6- $\mu $ Vrms), at record-low power of 1mW, and with a 6.6-m air communication range.

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge due to the constrained operational environment, requiring low latency and high energy efficiency. Previous benchmarks have provided limited insights into energy efficiency and latency. This paper, however, introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primateâ\euro™s finger kinematics from the motor cortex, and explores the suitability for low latency and low compute neural decoding. The study finds that ANN-based decoders provide superior decoding accuracy, requiring a high latency and many operations to decode neural signals effectively. Spiking neural networks emerge as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of the computational resources. These distinctive advantages of neuromorphic spiking neural networks, positions them as highly suitable for the challenging environment of closed-loop neural modulation. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intracortical human-machine interaction.

Intracortical brain-computer interfaces (iBCIs) hold promise for restoring motor, sensory, and cognitive functions, including applications in paralysis treatment and speech decoding. High-density microelectrode arrays (MEAs) provide fine spatial and temporal discrimination of neural activity, but the considerable upsurge in data generation poses challenges for wireless transmission from miniaturized implants due to constraints on power, bandwidth, heat dissipation, and device size. To address these constraints, we propose a two-stage wireless iBCI architecture comprising a transdural galvanic-coupled body channel communication (BCC) link from a free-floating MEA to an intracranial unit, followed by a transcutaneous link to an external unit. This study focuses on the transdural BCC telemetry system, which provides compact, wideband, and energy-efficient data transmission. Phantom tests, and ex vivo experiments using a human cadaveric head specimen validate the system, demonstrating wireless transmission up to 500 Mbps with 20% duty cycling and bit error rates below 10⁻⁵. The system incorporates the send-on-delta encoder (SODA), achieving up to 11.4× data compression and reducing thermal load for meeting the safety guidelines. Safety is further examined using brain-on-a-chip models, which demonstrate that the system does not evoke unintended neural activity, supporting the platform’s long-term viability for high-resolution iBCIs.