Distributed Neural Interfaces: Challenges and Trends in Scaling Implantable Technology

Szostak, K.; Feng, Peilong; Mazza, Federico; Constandinou, Timothy G.

doi:10.1007/978-981-16-5540-1_11

Cited by 1 publication

(4 citation statements)

References 122 publications

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“…Future BCI applications will collect data across multiple brain sites, and compare histories of stored neural signals across them. Many applications exhibit these needs, including algorithms for neuromuscular rehabilitation and neuropsychiatric disorders [2,4,13,[42][43][44]], but we focus on epileptic seizure propagation and detection of movement intent as they form the bulk of emerging BCI use [2,5,[45][46][47]. In addition, we also consider spike sorting, a crucial kernel widely used in many applications [48,49].…”

Section: B Neural Processing Applications and Algorithmic Kernelsmentioning

confidence: 99%

“…A growing interest in distributed analyses of the brain [1,13,25,26] has motivated the design of rudimentary multi-site BCIs [31,32,101]. Prior studies [31,32] propose microchips that stream sensor data wirelessly to a central hub outside the skull using back-scattering radio techniques.…”

Section: H Ilp Performancementioning

confidence: 99%

“…Instead, this work focuses on implantable BCIs that are surgically embedded directly on, around, and in the brain tissue [13,14]. Implantable BCIs directly record from and stimulate neurons with high fidelity, spatial resolution, and in real time [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…Complicating BCI design further is the emergence of applications that read and process neural activity from many brain sites over time [1,13,25,26]. This is because the brain's functions (and disorders) are ultimately based on physical and functional connectivity between brain regions that evolve over time [1,13,25,26].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

Sriram¹,

Pothukuchi²,

Michał³

et al. 2023

Preprint

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Hull 1 is an accelerator-rich distributed implantable brain-computer interface (BCI) that reads biological neurons at data rates that are 2-3 orders of magnitude higher than the prior art, while supporting many neuroscientific applications. Prior approaches have restricted brain interfacing to tens of megabits per second in order to meet two constraints necessary for effective operation and safe long-term implantation-power dissipation under tens of milliwatts and response latencies in the tens of milliseconds. Hull also adheres to these constraints, but is able to interface with the brain at much higher data rates, thereby enabling, for the first time, BCI-driven research on and clinical treatment of brain-wide behaviors and diseases that require reading and stimulating many brain locations. Central to Hull's power efficiency is its realization as a distributed system of BCI nodes with accelerator-rich compute. Hull balances modular system layering with aggressive cross-layer hardware-software co-design to integrate compute, networking, and storage. The result is a lesson in designing networked distributed systems with hardware accelerators from the ground up.

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