Scalable 49-Channel Neural Recorder with an Event-Driven Ramp ADC and PCA Compression in 28 nm CMOS
원문 초록
Neural interfaces advance neuroscience research and therapeutic innovations by accurately measuring neuronal activity. However, recording raw data from numerous neurons results in substantial amount of data and poses challenges for wireless transmission. While conventional neural recorders consume energy to digitize and process the full neural signal, only a fraction of this data carries essential spiking information. Leveraging on this signal sparsity, this paper introduces a neural recording integrated circuit in TSMC 28nm CMOS. It features an event-driven ramp analog-to-digital converter, and a spike compression module based on principal component analysis. The circuit consists of 49 channels, each occupying an on-chip area of 50 $\times$ 60 $\mu$m$^2$. The circuit measures 1370 $\times$ 1370 $\mu$m$^2$ and consumes 534 $\mu$W. Compression testing on a synthetic dataset demonstrated an 8.8-fold reduction compared to raw spikes and a 328-fold reduction relative to the raw signal. This compression approach maintained a spike sorting accuracy of 74.9%, compared to the 79.5% accuracy obtained with the raw signal. The paper details the architecture and performance outcomes of the neural recording circuit and its compression module.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders
Short-and-Sparse Deconvolution -- A Geometric Approach
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Neural Clustering Processes
토론
이 논문과 연결된 포럼 글이 아직 없습니다.