Navigation auf zora.uzh.ch

Search ZORA

ZORA (Zurich Open Repository and Archive)

A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro (2016). A bidirectional brain-machine interface featuring a neuromorphic hardware decoder. Frontiers in Neuroscience, 10:563.

Abstract

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > General Neuroscience
Language:English
Date:2016
Deposited On:26 Jan 2017 12:46
Last Modified:16 Oct 2024 01:39
Publisher:Frontiers Research Foundation
Number of Pages:1
ISSN:1662-453X
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fnins.2016.00563
PubMed ID:28018162
Project Information:
  • Funder: FP7
  • Grant ID: 284553
  • Project Title: SI-CODE - Towards new Brain-Machine Interfaces: state-dependent information coding
  • Funder: FP7
  • Grant ID: 257219
  • Project Title: NEUROP - Neuromorphic processors: event-based VLSI models of cortical circuits for brain-inspired computation
Download PDF  'A bidirectional brain-machine interface featuring a neuromorphic hardware decoder'.
Preview
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

Metadata Export

Statistics

Citations

Dimensions.ai Metrics
51 citations in Web of Science®
57 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

88 downloads since deposited on 26 Jan 2017
10 downloads since 12 months
Detailed statistics

Authors, Affiliations, Collaborations

Similar Publications