Hybrid Neural Networks for rehabilitation – HYRENE

The technical approach defined for HYRENE is to couple live large-scale neural networks and artificial neural networks embedded in analog and mixed integrated electronics and endowed with adaptive capabilities (synaptic plasticity). This hybrid coupling will use dedicated microelectrode arrays to record and electrically stimulate live neural networks, with a specific emphasis on stimulation localization.

Results after 1 year in the project are detailed in the deliverables :
- 3D MEA design , optimized for focal stimulation
- Identification of a supraspinal stimulation zone triggering descending motor commands
- Interface hardware/software from input pulse trains towards artificial neural networks

The system including the artificial and living neural networks will form a closed loop with a regulated feedback. The artificial neural networks will implement conductance-based neuron and synapse models, controlled by plasticity rules like STDP (spike-timing dependent plasticity). Dedicated integrated electronics will be designed to implement the communication channels between the living and artificial networks: signal conditioning for the biological signals (from living to artificial) and adapted coding of the artificial neurons events (from artificial to living).

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HYRENE is a fundamental research project aiming at the development of innovative technologies : hybrid systems connecting artificial and biological neural networks. One goal of this project is to couple a whole organ (mouse spinal cord) with a hardware networks in order to restore the organ functional activity after a lesion. Further perspectives are the development of smart “neuroelectronic” interfaces for functional rehabilitation.

Population aging all around the world raises a societal issue due to the associated increase in neurodegenerative diseases. One therapeutic approach to treat resulting functional deficiencies is to propose neural prosthesis based on neuro-electronic implants. In recent years, technological advances in the field of micro- and nano-electronics has led to the development of new instrumentation tools for the exploration of the central nervous system, making use of dedicated interfaces between microelectronics and live neural networks. This field of research has strongly developed since 2000, especially with the
emergence of brain-machine interfaces. These interfaces, which are now tested in humans, process brain signals recorded with microelectrode arrays to turn them into command signals for the control of external devices (robotic arms, computers…). However, to date, such interfaces remain mainly monodirectional, with no information delivered back to the network. The current challenge is to achieve bidirectional neuro-electronic interfaces, establishing a true dynamic communication between live neural networks and electronic
systems. Especially, electronic systems connected to neural networks with existing technologies do not include embedded intelligence.
The technical approach defined for HYRENE is to couple live large-scale neural networks and artificial neural networks embedded in analog and mixed integrated electronics and endowed with adaptive capabilities (synaptic plasticity). This hybrid coupling will use dedicated microelectrode arrays to record and electrically stimulate live neural networks, with a specific emphasis on stimulation localization.
The system including the artificial and living neural networks will form a closed loop with a regulated feedback. The artificial neural networks will implement conductance-based neuron and synapse models, controlled by plasticity rules like STDP (spike-timing dependent plasticity). Dedicated integrated electronics will be designed to implement the communication channels between the living and artificial networks: signal conditioning for the biological signals (from living to artificial) and adapted coding of the artificial neurons events (from artificial to living).
In this project, integration between physics (electronic engineering, Microsystems) and biology (integrative neuroscience) is mandatory. All 3 partners rely on their large experience in multi-disciplinary collaborative projects at national and international levels.
This project is expected to generate scientific advances:
- in the field of information science: by the design of embedded self-organized artificial neural networks, able to communicate in real time with entire biological networks.
- in the field of life science : a tool to develop and test efficient strategies for spinal cord rehabilitation.