This article presents a bioinspired, event-driven neuromorphic sensing system (NSS)
capable of performing on-chip feature extraction and “send-on-delta” pulse-based …

Modern brain-computer interfaces and neural implants allow interaction between the tissue,
the user and the environment, where people suffer from neurodegenerative diseases or …

Neuroscience and neurotechnology are currently being revolutionized by artificial
intelligence (AI) and machine learning. AI is widely used to study and interpret neural …

This paper introduces the processing element architecture of the second generation
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …

Introduction Multi-channel electrophysiology systems for recording of neuronal activity face
significant data throughput limitations, hampering real-time, data-informed experiments …

As neural-recording devices get denser and generate more data due to ever higher channel
counts, on-chip and online neural signal processor (NSP) becomes crucial to reduce the …

As neural-recording devices get denser and generate more data [1], on-chip and online
neural-signal processing becomes crucial to reduce the data-transmission power and …

The potential low-energy feature of the spiking neural network (SNN) engages the attention
of the AI community. Only CPU-involved SNN processing inevitably results in an inherently …

Over the past decade, there has been a growing interest in the development of intelligent
neural interface systems-on-chip (SoCs) for a range of neurological disorders and emerging …

Spike detection plays a central role in neural data processing and brain-machine interfaces
(BMIs). A challenge for future-generation implantable BMIs is to build a spike detector that …