A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

Hogri, Roni; Bamford, Simeon; Taub, Aryeh H.; Magal, Ari; Giudice, Paolo Del; Mintz, Matti

doi:10.1038/srep08451

Cited by 25 publications

(21 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study, inspired by a previous work ( 55 ), implemented a hybrid interaction ( 56 ) between the cerebellum of a rat and an SNN implemented on FPGA. Their model involved 10k LIF neurons and did not integrate other biomimetic behaviors, such as axonal delay, short-term plasticity and synaptic noise, unlike the IZH neurons implemented in our system.…”

Section: Discussionmentioning

confidence: 99%

A neuroprosthetic system to restore neuronal communication in modular networks

Buccelli

Bornat

Colombi

et al. 2019

Preprint

View full text Add to dashboard Cite

48Recent advances in neurotechnology allow neurological impairments to be treated or 49 reduced by brain machine interfaces and neuroprostheses. To develop energy-efficient and 50 3 real-time capable devices, neuromorphic computing systems are envisaged as the core of 51 next-generation 'neurobiohybrid' systems for brain repair. We demonstrate here the first 52 exploitation of a neuromorphic prosthesis to restore bidirectional interactions between two 53 neuronal populations, even when one is damaged or completely missing. We used in vitro 54 modular cell cultures to mimic the mutual interaction between neuronal assemblies and 55 created a focal lesion to functionally disconnect the two populations. Then, we employed 56 our neuromorphic prosthesis for two specific applications with future clinical 57 implications: bidirectional bridging to artificially reconnect two disconnected neuronal 58 modules and hybrid bidirectional bridging to replace the activity of one module with a 59 neuromorphic spiking neural network. Our neuroprosthetic system opens up new avenues 60 for the development of novel bioelectrical therapeutics for human applications. 61 62 65 the greatest impact carried by stroke (1) and traumatic brain injury (2), brain disorders are among 66 the leading causes of disabilities worldwide. Due to recent advances in neural and neuromorphic 67 engineering, direct interfacing of artificial circuits with large neuronal networks is possible to 68 develop novel 'neurobiohybrid' systems (such as neuroprostheses (3)), which are envisaged as 69 potentially interesting clinical applications for brain lesions (4). In this paper, we introduce an 70 innovative neuroprosthetic system based on a neuromorphic real-time interface that can re-71 establish the communication between two disconnected neuronal assemblies. 72 Neural interfaces are promising solutions for brain repair (5). Modern neural interfaces are mainly 73 designed to restore lost motor functions in only one direction, i.e., from the brain to the body (6) 74 or from the body to the brain (7). Additionally, recent neuroprosthetic developments have shown 75 4 the enormous potential of neural interfaces to aid and accelerate functional recovery (8, 9). 76 However, a major obstacle in developing novel neuroprosthetic devices for bidirectional 77 communication with and within the brain is the complex nature of interactions among different 78 brain areas, which in turn presents a challenge for the development of appropriate stimulation 79 protocols as well as for testing such devices using in vivo models (10). 80 Despite very recent technological progress (11, 12), in vivo models still have two main 81 bottlenecks. The first bottleneck is the technical challenge to faithfully reproduce specific/focal 82 network lesions (mainly due to their complexity) that the neuroprosthesis aims to treat, whereas 83 the second is the difficulty in disentangling the actual effect of the adopted electrical therapy from 84 the complex activity of a brain constantly pr...

show abstract

Section: Discussionmentioning

confidence: 99%

A neuroprosthetic system to restore neuronal communication in modular networks

Buccelli

Bornat

Colombi

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The relevance of neuromorphic technology in the design of brain-machine interfaces is demonstrated by the flourishing work in this domain (see Dethier et al, 2013; Barsakcioglu et al, 2014; Hogri et al, 2015, as non-exhaustive examples). The main features of neuromorphic implementations are low power consumption, real-time operation, adaptability, and compactness.…”

Section: Discussionmentioning

confidence: 99%

“…The main features of neuromorphic implementations are low power consumption, real-time operation, adaptability, and compactness. Simulations show that hardware Spiking Neural Networks can successfully decode the activity of neurons for closed-loop cortical implants (Dethier et al, 2013) and an ad-hoc working prototype is able to substitute a cerebellar learning function in the rat (Hogri et al, 2015). Our work extends this approach in proposing a modular and reconfigurable scheme whereby the neuromorphic chip can be exploited for implementing different algorithms and BMI functions; in particular, we demonstrated this approach by using the chip as neural decoder.…”

Section: Discussionmentioning

confidence: 99%

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

“…Similarly, the biomimetic model can be used to replace and/or rehabilitate a damaged brain region [12–15]. To achieve this, the biomimetic model can be connected to the remaining brain regions and tuned to reproduce healthy neural activity and stimulate the damaged region, restoring normal brain function.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Durá-Bernal¹,

Neymotin²,

Kerr³

et al. 2017

IBM J. Res. & Dev.

View full text Add to dashboard Cite

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

show abstract

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

Cited by 25 publications

References 60 publications

A neuroprosthetic system to restore neuronal communication in modular networks

A neuroprosthetic system to restore neuronal communication in modular networks

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Contact Info

Product

Resources

About