A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals

Angotzi, Gian Nicola; Boi, Fabio; Zordan, Stefano; Bonfanti, Andrea; Vato, Alessandro

doi:10.1038/srep05963

Cited by 34 publications

(30 citation statements)

References 61 publications

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“…This structure makes easier and more reliable both the implementation of the single module and its integration in the complete system. Parallel development of components could also accelerate the ultimate realization of a device compact and powerful enough to be used as clinical tool able to transfer data between the brain and external devices wirelessly through an implanted interface (Azin et al, 2011; Fan et al, 2011; Borton et al, 2013; Angotzi et al, 2014). In this work, we also demonstrated that the modular architecture does not affect BMI performances, showing results comparable with the ones achieved in Vato et al (2012); this result suggests that BMI systems developed in other labs could also be re-implemented in a modular manner.…”

Section: Discussionmentioning

confidence: 99%

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

et al. 2016

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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.

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Section: Discussionmentioning

confidence: 99%

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

et al. 2016

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“…134 Alessandro Vato et al demonstrated bidirectional BMI in rodents; early validation showed it capable of stimulating the brain upon detection of some target activity. 159 Vato et al also demonstrated a bidirectional BMI algorithm that maps the state of an external device (e.g., position) into a series of electrical impulses while transforming recordings from the motor cortex into a tunable output force. Such a paradigm could conceivably be applied toward the operation of an artificial limb.…”

Section: Current Interfaces In the Cnsmentioning

confidence: 99%

The Evolution of Neuroprosthetic Interfaces

Adewole¹,

Serruya

Harris

et al. 2016

Crit Rev Biomed Eng

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The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.

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“…PennBMBI also provided two channels of stimulation with a stimulation compliance voltage of ±12 V. In addition to neural signal recording and stimulation, inertial, temperature and force measurements can be reported via a body sensor network. In [12], a 16-channel (eight channels for recording and eight channels for stimulation) wireless portable system with both recording and stimulation capabilities was presented. More recently, a wireless headstage with 32-channel neural signal recording and up to 32-channel optical stimulation has been presented in [13].…”

Section: Introductionmentioning

confidence: 99%

“…While the electrode arrays and sense electronics are implanted, almost all computational functions are performed by external components. Further, according to [12], one must find a balance among the limited resources due to the small form factor. The studies mentioned in the above literature either lack the ability to integrate all functional components into a reasonably small-sized enclosure or have a satisfactory size, but not fully-bidirectional communication.…”

Section: Introductionmentioning

confidence: 99%

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

Routhu

Moon

et al. 2016

Sensors

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All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

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A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals

Cited by 34 publications

References 61 publications

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

The Evolution of Neuroprosthetic Interfaces

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

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