Laboratoire de l'Intégration du Matériau au Système

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Overview

The AS2N group, which was founded in 2010, designs silicon neural networks. Two approaches are used for our neuromorphic design activities:

i. An approach called "biomimetic" where the goal is to imitate the electrical activity of biological neural networks by using models from computational neuroscience. These artificial spiking neurons are connected with biological neurons to study living systems and/or to design solutions for rehabilitation in response to a deficit in a population of neurons in a living organism. So far, these biomimetic systems have been implemented in digital platforms based on FPGA.

ii. An approach called "bio-inspired" where we use living organisms to create event-based computing systems. This theme uses more formal neural representations. These neural networks are dedicated to RMS tasks (Recognition, Data Mining and Synthesis) for embedded systems. To achieve very low power consumption, these systems combine the bio-inspired design of analog and mixed integrated circuits and nanotechnologies such as memristors.

More about the AS2N group...

Members
   
Matthieu AMBROISE, Doctorant Timothée LEVI, MCF Sylvain SAÏGHI, MCF HDR, Responsable d'équipe Jean TOMAS, MCF    

Skills

At the interface of neuroscience and electonic system design, we have developped large skills around our hert:

  • Embedded real-time computation
  • Analog and mixed integrated circuit design
  • Spiking neural networks 
  • Computational neuroscience
  • Optimization by metaheuristics 
  • Nanotechnologies in neuromorphic systems 

 

Current projects

Hyrène Project - French National Research Agency 2011-2014

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 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.

Partners: ESIEE, Université Victor Segalen Bordeaux 2

Brainbow Project - EU FET Young explorers 2012-2015

Enhancing recovery of cognitive and motor functions after localized brain injuries which disrupt connections between brain and body is widely recognized as a priority in healthcare. Nowadays, neurological diseases implying severe motor impairment are among the most common causes of adult-onset disability. Millions of people worldwide are affected by paralysis, and this number is likely to increase in coming years, because of the rapidly ageing population. Current assistive technology is still limited since only a minority of survivors with hemiparesis is able to achieve independence in simple activities of daily living. The frequent lack of complete recovery makes a desirable goal the development of novel neurobiological or neurotechnological strategies for brain repair.
Over the last decade Brain-Machine Interfaces (BMIs) and generally neuro-prostheses (Nicolelis, 2003; Hochberg et al., 2006; Nicolelis & Lebedev, 2009; Hochberg et al., 2012) have been object of extensive research and may represent a valid treatment for such disabilities. The development of these devices has and will hopefully have a profound social impact on the quality of life. Nevertheless, modern neural interfaces are mainly devoted to restore lost motor functions, because of injuries at the level of the spinal cord (Collinger et al., 2012; van den Brand et al., 2012), or recover sensorial capabilities, e.g. through artificial retinal or cochlear implants (Chader et al., 2009). However, the majority of motor disabilities are caused by brain diseases, such as stroke and traumatic brain injury - TBI - (33%) and not by spinal cord injury (23%).
Only very recently scientific interest has been devoted to in vivo cognitive neural prostheses. The first ever hippocampal prosthesis improving memory function in behaving rats has been presented in recent papers (Berger et al., 2011; Berger et al., 2012). Lately the same group tested a similar device in primate prefrontal cortex aimed at restoring impaired cognitive functions (Hampson et al., 2012; Opris et al., 2012).
The realization of such prostheses implies that we know how to interact with neuronal cell assemblies, taking into account the intrinsic spontaneous activation of neuronal networks and understanding how to drive them into a desired state in order to produce a specific behaviour. The long-term goal of replacing damaged brain areas with artificial devices requires the development of neural network models to be fed with the recorded electrophysiological patterns to yield the correct brain stimulation aimed at recovering the desired functions. All these issues are extremely difficult to investigate in vivo, due to the inherent complexity and low controllability of the system. On the other hand, we believe that important insights (e.g. structure-dynamics relationship, neural coding) might be gained by using in vitro systems of increasing architectural complexity, which can be easily and wholly accessed, monitored, manipulated, and thus modelled.
This topic is extremely up-to-date and represents one of the most important challenges over the next years in terms of clinical impacts and translational medicine. This is demonstrated, not only by the literature over the past years, but also by new US funding programmes in this specific direction (see e.g. DARPA website: http://www.darpa.mil/default.aspx, programmes REPAIR and REMIND). In particular, the group of T. Berger (University of California at Irvine, CA, USA) published several papers in 2013 regarding the development of hippocampal prosthesis (both on in vitro and in vivo experimental models) for memory enhancement (Deadwyler et al., 2013; Hampson et al., 2013; Hsiao et al., 2013). On December 2013, another very interesting paper came out from the group led by R. Nudo, very active in clinical studies related to stroke and TBI (Guggenmos et al., 2013). In this paper the very first example of a unidirectional ‘neural bridge’ aimed at promoting functional connection between two motor areas (i.e. the premotor cortex and the sensory cortex) in a rat model of TBI was demonstrated. Our project BRAIN BOW is exactly along the same line, but with the goal to make even a step forward with respect to these studies: design a chip for network replacement able to operate in a closed-loop fashion. The preliminary results demonstrating that a biological network and an artificial one are able to communicate and influence, in a bi-directional way, their intrinsic dynamics, constitute one of the most promising results of the second year and represent the basis for the activities of the third and final year.
Read more...

Partners: Istituto Italiano di Tecnologia (Italy), University of Genova (Italy), Tel Aviv University (Israel)

MHANN Project - French National Research Agency P2N 2011-2015

In 2008, researchers at Hewlett-Packard have unveiled a new electronic component, called the memristor. Theorized by Leon Chua in 1971, this electronic device is a non-volatile, non-linear resistor. By applying a voltage, it is possible to vary continuously the resistance of the device, and the device "memorizes" that resistance after the voltage is no longer applied. As such, these memristors offer a wide variety of applications as binary (OFF/ON) or multilevel/analog memories or switches in reconfigurable memories. In addition, memristors intrinsically behave as artificial synapses. Most of the memristor devices proposed up to this day are based on defect-mediated physical effects, for example the electromigration of oxygen ions for the Hewlett-Packard components. To use such devices, the reliability issues due to high operating temperatures, difficulty of a precise control of the switching behavior and potential device deterioration will have to be solved in order to achieve the large endurance and retention times required for operational components.
In 2009, the UMPhi-CNRS and Thales project partners have patented a new component: the “ferroelectric memristors”. This memristor belongs to another class of memristors, called “Electronic effect memories”. The resistance changes are due to purely electronic effects and therefore preserve the materials structure. They are based on a physical concept radically different from the existing solutions: ferroelectricity in tunnel junctions. The resistive switching is based on the intrinsic switching of ferroelectric domains and therefore possesses a fundamental merit over defect-mediated mechanisms to achieve the reliable performance necessary for commercial production.
On the other hand, companies like Intel have insisted that the most important high-performance applications are not scientific computing but the following three categories: Recognition, Mining and Synthesis applications (RMS), the first two categories largely relying on classification, clustering, approximation and optimization algorithms, for which competitive algorithms based on neural networks exist. Due to stringent power consumption constraints, the clock frequency of processors no longer increases or barely, so that a hardware neural network would retain all its performance and power advantage (about two orders of magnitude) compared to the software version run on a processor.
So we can note that there is a convergence of technology, architecture and application trends: hardware artificial neural networks are well suited to tackle an important class of applications while coping with upcoming technology hassles.
Therefore, memristors constitute an ideal and very timely alternative implementation for synapses of hardware artificial neural networks. Memristors would be far denser than current SRAM-based implementations but they would also require far less power since the memristor is a non-volatile memory. Hardware ANNs with an architecture composed of analog circuitry coupled with the aforementioned memristors open the possibility to build high-performance accelerators able to tackle the large computational tasks of RMS applications.
The purpose of this project is to build a medium-scale prototype of such a bio-inspired architecture, by using long-life and nanometric “ferroelectric” memristors. The area, performance and power benefits of this approach will be evaluated to define its interest for embedded systems.
The MHANN project is multi-disciplinary in the sense that it proposes new physical concepts for devices (physics) and aims at integrating them into on-chip bio-inspired architectures (micro-electronics, computer science and architectures).

Partners: UMPhi CNRS-Thales, Thales TRT, INRIA

MIRA Project - French National Research Agency 2015-2019

This project plans to develop a path towards a novel neuromorphic computation paradigm using memristors to process visual information in a highly efficient way and at unprecedented speed. It will make use of a cutting-edge neuromorphic retina sensor that, rather than frames outputs scene-driven, time-encoded visual information at microsecond resolution. The spike output from the camera will feed into an artificial neural network based on memristors and silicon neurons. This approach will provide – for the first time – a way to process visual input at rates up to 100 kHz while at the same time reducing the system’s power consumption. The proposed project is rooted in the emerging field of “neuromorphic engineering”, relying on close interaction of material sciences, neurosciences, mathematics and microelectronics. The MIRA project will contribute new knowledge, techniques and applications to this growing field and the constituent areas.

Partners: UMPhi CNRS-Thales, Institut de la Vision, Chronocam

Publications

Total : 73

Articles in peer-reviewed journal → 18 Show

2022


Brain tumor detection using selective search and pulse-coupled neural network feature extraction
Niepceron, Brad ; Grassia, Filippo ; Nait Sidi Moh, Ahmed
Dans : Computing and Informatics
https://hal-u-picardie.archives-ouvertes.fr/hal-03724185

2020


Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation
Mosbacher, Yossi ; Khoyratee, Farad ; Goldin, Miri ; Kanner, Sivan ; Malakai, Yenehaetra ; Silva, Moises ; Grassia, Filippo ; Simon, Yoav Ben ; Cortes, Jesus ; Barzilai, Ari ; Levi, Timothée ; Bonifazi, Paolo
Dans : Scientific Reports
https://hal.archives-ouvertes.fr/hal-02568187

2019


A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks
Buccelli, Stefano ; Bornat, Yannick ; Colombi, Ilaria ; Ambroise, Matthieu ; Martines, Laura ; Pasquale, Valentina ; Bisio, Marta ; Tessadori, Jacopo ; Nowak, Przemysław ; Grassia, Filippo ; Averna, Alberto ; Tedesco, Mariateresa ; Bonifazi, Paolo ; Difato, Francesco ; Massobrio, Paolo ; Levi, Timothée ; Chiappalone, Michela
Dans : iScience
https://hal.archives-ouvertes.fr/hal-02482383

Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization
Khoyratee, Farad ; Grassia, Filippo ; Saïghi, Sylvain ; Levi, Timothée
Dans : Frontiers in Neuroscience
https://hal.archives-ouvertes.fr/hal-02482394

A Human Induced Pluripotent Stem Cell-Derived Tissue Model of a Cerebral Tract Connecting Two Cortical Regions
Kirihara, Takaaki ; Luo, Zhongyue ; Chow, Siu Yu A. ; Misawa, Ryuji ; Kawada, Jiro ; Shibata, Shinsuke ; Khoyratee, Farad ; Vollette, Carole Anne ; Volz, Valentine ; Levi, Timothée ; Fujii, Teruo ; Ikeuchi, Yoshiho
Dans : iScience
https://hal.archives-ouvertes.fr/hal-02482393

2018


Digital implementation of Hodgkin–Huxley neuron model for neurological diseases studies
Levi, Timothée ; Khoyratee, Farad ; Saïghi, Sylvain ; Ikeuchi, Yoshiho
Dans : Artificial Life and Robotics
https://hal.archives-ouvertes.fr/hal-02482403

2017


Biomimetic neural network for modifying biological dynamics during hybrid experiments
Ambroise, Matthieu ; Buccelli, Stefano ; Grassia, Filippo ; Pirog, Antoine ; Bornat, Yannick ; Chiappalone, Michela ; Levi, Timothée
Dans : Artificial Life and Robotics
https://hal.archives-ouvertes.fr/hal-01567477

Learning through ferroelectric domain dynamics in solid-state synapses
Boyn, Sören ; Grollier, Julie ; Lecerf, Gwendal ; Xu, Bin ; Locatelli, Nicolas ; Fusil, Stéphane ; Girod, Stéphanie ; Carrétéro, Cécile ; Garcia, Karin ; Xavier, Stéphane ; Tomas, Jean ; Bellaiche, Laurent ; Bibes, Manuel ; Barthélémy, Agnès ; Saïghi, Sylvain ; Garcia, Vincent
Dans : Nature Communications
https://hal.archives-ouvertes.fr/hal-02288726

Digital hardware implementation of a stochastic two-dimensional neuron model
Grassia, Filippo ; Kohno, T ; Levi, Timothée
Dans : Journal of Physiology - Paris
https://hal.archives-ouvertes.fr/hal-01562687

2016


Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord Using Intraspinal Electrical Microstimulation Driven by a Digital Neuromorphic CPG
Joucla, Sébastien ; Ambroise, Matthieu ; Levi, Timothée ; Lafon, Thierry ; Chauvet, Philippe ; Saïghi, Sylvain ; Bornat, Yannick ; Lewis, Noëlle ; Renaud, Sylvie ; Yvert, Blaise
Dans : Frontiers in Neuroscience
https://hal.archives-ouvertes.fr/hal-01562686

2015


Digital Spiking Neural Network for closed-loop systems
Levi, Timothée ; Ambroise, Matthieu ; Grassia, Filippo ; Kohno, Takashi ; Saïghi, Sylvain
Dans : Seisan Kenkyu
https://hal.archives-ouvertes.fr/hal-01227591

Plasticity in memristive devices for spiking neural networks
Saïghi, S. ; Mayr, C.G. ; Serrano-Gotarredona, T. ; Schmidt, H. ; Lecerf, G. ; Tomas, J. ; Grollier, J. ; Boyn, S. ; Vincent, A.F. ; Querlioz, D. ; La Barbera, S. ; Alibart, F. ; Vuillaume, Dominique ; Bichler, O. ; Gamrat, C. ; Linares-Barranco, B.
Dans : Frontiers in Neuroscience
https://hal-cea.archives-ouvertes.fr/cea-01846866

2014


Silicon neuron: digital hardware implementation of the quartic model
Grassia, Filippo ; Levi, Timothée ; Saighi, Sylvain ; Kohno, Takashi
Dans : Journal of Artificial Life and Robotics
https://hal.archives-ouvertes.fr/hal-01227599

2013


Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments.
Ambroise, Matthieu ; Levi, Timothée ; Joucla, Sébastien ; Yvert, Blaise ; Saïghi, Sylvain
Dans : Frontiers in Neuroscience
https://hal.archives-ouvertes.fr/hal-00956624

2012


Bifurcation analysis in a silicon neuron
Grassia, Filippo ; Levi, Timothée ; Saïghi, Sylvain ; Kohno, Takashi
Dans : Journal of Artificial Life and Robotics
https://hal.archives-ouvertes.fr/hal-00766340

2011


Automated Parameter Estimation of the Hodgkin-Huxley Model Using the Differential Evolution Algorithm: Application to Neuromimetic Analog Integrated Circuits
Buhry, Laure ; Grassia, Filippo ; Giremus, Audrey ; Grivel, Eric ; Renaud, Sylvie ; Saïghi, Sylvain
Dans : Neural Computation
https://hal.archives-ouvertes.fr/hal-00625448

Tunable neuromimetic integrated system for emulating cortical neuron models.
Grassia, Filippo ; Buhry, Laure ; Levi, Timothée ; Tomas, Jean ; Destexhe, Alain ; Saïghi, Sylvain
Dans : Frontiers in Neuroscience
https://hal.archives-ouvertes.fr/hal-00684091

2010


Real-Time Simulation of Biologically Realistic Stochastic Neurons in VLSI
Chen, Hsin ; Saïghi, Sylvain ; Buhry, Laure ; Renaud, Sylvie
Dans : IEEE Transactions on Neural Networks
https://hal.archives-ouvertes.fr/hal-00551652
Conference with proceedings (national) → 45 Show

2019


Réseaux de neurones artificiels biomimétiques pour de la bio-hybridation
Khoyratee, Farad ; Grassia, Filippo ; Saïghi, Sylvain ; Levi, Timothée
Dans : Journées Francophones de la Recherche JFR 2019, Tokyo (Japan)
https://hal.archives-ouvertes.fr/hal-02893061

Biomimetic Spiking Neural Network (SNN) Systems for ‘In Vitro’ Cells Stimulation
Khoyratee, Farad ; Nishikawa, Stephany Mai ; Zhongyue, Luo ; Kim, Soo Hyeon ; Saïghi, Sylvain ; Fujii, Teruo ; Ikeuchi, Yoshiho ; Aihara, Kazuyuki ; Levi, Timothée
Dans : 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo (Japan)
https://hal.archives-ouvertes.fr/hal-02484011

Low power and massively parallel simulation of oscillatory biochemical networks on FPGA
Le Thanh, Serge ; Lobato-Dauzier, Nicolas ; Khoyratee, Farad ; Beaubois, Romain ; Fujii, Teruo ; Genot, Anthony ; Levi, Timothée
Dans : 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara (Japan)
https://hal.archives-ouvertes.fr/hal-02406013

Learning from slow Dynamic Vision Sensor inputs with a hardware memristor-based Spiking Neural Network
Lewden, Pierre ; Meyer, Charly ; Vincent, Adrien ; Tomas, Jean ; Saïghi, Sylvain
Dans : GDR BioComp, Lille (France)
https://hal.archives-ouvertes.fr/hal-02527452

Hardware Spiking Neural Networks: Slow Tasks Resilient Learning with Longer Term-Memory Bits
Lewden, Pierre ; Vincent, Adrien ; Meyer, Charly ; Tomas, Jean ; Siami, Shidoush ; Saïghi, Sylvain
Dans : 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara (Japan)
https://hal.archives-ouvertes.fr/hal-02487821

Neural Network for ultra-low power and real time computation
Meyer, Charly ; Lewden, Pierre ; Vincent, Adrien ; Tomas, Jean ; Saïghi, Sylvain
Dans : GDR BioComp, Lille (France)
https://hal.archives-ouvertes.fr/hal-02527440

BIOMIMETIC SPIKE-TIMING BASED IONIC MICROSTIMULATION FOR NEURON CULTURE
Nishikawa, Stephany ; Khoyratee, Farad ; Kim, Soo ; Ikeuchi, Yoshiho ; Aihara, Kazuyuki ; Fujii, Teruo ; Levi, Timothée
Dans : ICAROB, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-02484012

2018


NEUROMIMETIC STIMULATION APPLIED ON CEREBRAL ORGANOIDS
Khoyratee, Farad ; Zhongyue, Luo ; Volette, Carole-Anne ; Benneteau, Thomas ; Beaubois, Romain ; Lange, Corentin ; Aihara, Kazuyuki ; Fujii, Teruo ; Ikeuchi, Yoshiho ; Levi, Timothée
Dans : 10th International Symposium on Microchemistry and Microsystems (ISMM 2018), Busan (South Korea)
https://hal.archives-ouvertes.fr/hal-02484019

BIO-HYBRID EXPERIMENTS USING TUNABLE REAL-TIME BIOMIMETIC NEURAL NETWORK
Khoyratee, Farad ; Benneteau, Thomas ; Tixier-Mita, Agnès ; Saïghi, Sylvain ; Levi, Timothée
Dans : 10th International Symposium on Microchemistry and Microsystems (ISMM 2018), Busan (South Korea)
https://hal.archives-ouvertes.fr/hal-02484017

Real-time digital implementation of HH neural network on FPGA: cortical neuron simulation
Khoyratee, Farad ; Saïghi, Sylvain ; Levi, Timothée
Dans : 23th International Conference on Artificial Life and Robotics, ICAROB 2018, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-01709432

Low-power spiking neural network with memristive synapses
Meyer, Charly ; Saïghi, Sylvain ; Tomas, Jean
Dans : GDR BioComp, Bordeaux, 2018., Bordeaux (France)
https://hal.archives-ouvertes.fr/hal-02527460

Verilog-A model of ferroelectric memristors dedicated to neuromorphic design
Meyer, Charly ; Chanthbouala, André ; Boyn, Sören ; Tomas, Jean ; Garcia, Vincent ; Bibes, Manuel ; Fusil, Stéphane ; Grollier, Julie ; Saïghi, Sylvain
Dans : 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Bordeaux (France)
https://hal.archives-ouvertes.fr/hal-02527258

NEURO-HYBRID SYSTEM WITH SPIKING NEURAL NETWORK AND BIOMIMETIC IONIC MICRO-STIMULATION
Nishikawa, Stephany ; Khoyratee, Farad ; Luo, Zhongyue ; Shiraishi, Toshiharu ; Aihara, Kazuyuki ; Ikeuchi, Yoshiho ; Kim, Soo ; Fujii, Teruo ; Levi, Timothée
Dans : MicroTAS, Kaoshiung (Taiwan)
https://hal.archives-ouvertes.fr/hal-02484015

2017


Biomimetic neural network for modifying biological dynamics during hybrid experiments
Ambroise, Matthieu ; Buccelli, Stefano ; Grassia, Filippo ; Pirog, Antoine ; Bornat, Yannick ; Chiappalone, Michela ; Levi, Timothée
Dans : 22th International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-01567497

Spike pattern recognition using artificial neuron and Spike-Timing-Dependent Plasticity implemented on a multi-core embedded platform
Grassia, Filippo ; Levi, Timothée ; Doukkali, E ; Kohno, T
Dans : 22th International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-01567495

FPGA Implementation of the Hodgkin-Huxley Model for Neurological Disease study
Khoyratee, Farad ; Levi, Timothée ; Saïghi, Sylvain
Dans : JJC ICON, Bordeaux (France)
https://hal.archives-ouvertes.fr/hal-01709427

Implémentation du modèle de Hodgkin-Huxley sur FPGA pour l’étude des maladies neurodégénératives
Khoyratee, Farad ; Saighi, Sylvain ; Levi, Timothée
Dans : Journées Francophones de la Recherche JFR 2017, Tokyo (Japan)
https://hal.archives-ouvertes.fr/hal-01709425

FPGA Implementation of the Hodgkin-Huxley Model for Neurological Disease study
Khoyratee, Farad ; Levi, Timothée ; Saïghi, Sylvain
Dans : The 2nd International Symposium on Neuromorphic, non-linear, Neurofluidic Engineering, ISNNE, Bordeaux (France)
https://hal.archives-ouvertes.fr/hal-01567567

Digital implementation of Hodgkin-Huxley neuron model for neurological diseases studies
Levi, Timothée ; Khoyratee, Farad ; Saïghi, Sylvain ; Ikeuchi, Yoshiho
Dans : 22th International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-01567496

Spike pattern recognition using biomimetic Spiking Neural Network
Nanami, Takuya ; Grassia, Filippo ; Blanco, Manuel ; Aihara, Kazuyuki ; Kohno, Takashi ; Levi, Timothée
Dans : SWARM 2017, Kyoto (Japan)
https://hal.archives-ouvertes.fr/hal-01709428

2016


Stimulation strategies for neurons and fibres Connecting biological and artificial neural networks
Buccelli, Stefano ; Tessadori, Jacopo ; Bornat, Yannick ; Pasquale, Valentina ; Ambroise, Matthieu ; Levi, Timothée ; Massobrio, Paolo ; Chiappalone, Michela
Dans : 10th International Meeting on Substrate-Integrated Microelectrode Arrays (MEA 2016), Reutlingen (Germany)
https://hal.archives-ouvertes.fr/hal-01567554

Digital Biomimetic Spiking Neural Network for closed-loop systems
Levi, Timothée ; Ambroise, Matthieu ; Saïghi, Sylvain
Dans : GDR MultiElectrode systems for Neuroscience, Autrans (France)
https://hal.archives-ouvertes.fr/hal-02527547

2014


Leech heartbeat neural network on FPGA
Ambroise, Matthieu ; Levi, Timothée ; Saighi, Sylvain
Dans : Journées NeuroSTIC 2014, Paris (France)
https://hal.archives-ouvertes.fr/hal-01227640

Biomimetic CPG on FPGA for hybrid experiments
Ambroise, Matthieu ; Levi, Timothée ; Saighi, Sylvain
Dans : International Symposium on Neuromorphic and Non-linear Engineering, ISNNE, Tokyo (Japan)
https://hal.archives-ouvertes.fr/hal-01227627

Biorealistic Spiking Neural Network on FPGA
Ambroise, Matthieu ; Levi, Timothée ; Bornat, Yannick ; Saighi, Sylvain
Dans : Information Sciences and Systems (CISS), 2013 47th Annual Conference on, Baltimore (United States)
https://hal.archives-ouvertes.fr/hal-00956630

Silicon neuron: digital hardware implementation of the quartic model
Grassia, Filippo ; Levi, Timothée ; Kohno, Takashi ; Saïghi, Sylvain
Dans : International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-00956628

Silicon neuron dedicated to memristive spiking neural networks
Lecerf, Gwendal ; Tomas, Jean ; Boyn, Sören ; Girod, Stéphanie ; Mangalore, Ashwin ; Grollier, Julie ; Saïghi, Sylvain
Dans : Circuits and Systems (ISCAS), 2014 IEEE International Symposium on, Melbourne (Australia)
https://hal.archives-ouvertes.fr/hal-01093162

Biomimetic neural networks for hybrid experiments
Levi, Timothée ; Ambroise, Matthieu ; Grassia, Filippo ; Malot, Olivia ; Saighi, Sylvain ; Bornat, Yannick ; Tomas, Jean ; Renaud, Sylvie
Dans : International Symposium on Neuromorphic and Non-linear Engineering, ISNNE, Tokyo (Japan)
https://hal.archives-ouvertes.fr/hal-01227638

Biomimetic CPGs for robotic applications
Levi, Timothée ; Ambroise, Matthieu ; Grassia, Filippo ; Saïghi, Sylvain ; Kohno, Takashi ; Kinoshita, H. ; Fujii, T.
Dans : International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-00956627

2013


Leech Heartbeat Neural Network on FPGA
Ambroise, Matthieu ; Levi, Timothée ; Saïghi, Sylvain
Dans : Living Machine, Londres (United Kingdom)
https://hal.archives-ouvertes.fr/hal-00956629

In vitro experimental and theoretical studies to restore lost neuronal functions: the Brain Bow experimental framework
Bonifazi, Paolo ; Massobrio, Paolo ; Levi, Timothée ; Difato, Francesco ; Breschi, Gian L ; Pasquale, V. ; Goldin, Miri ; Ambroise, Matthieu ; Bornat, Yannick ; Tedesco, Mariateresa ; Bisio, Marta ; Frega, Marta ; Tessadori, Jacopo ; Nowak, P. ; Grassia, Filippo ; Kanner, Sivan ; Ronit, G. ; Renaud, Sylvie ; Martinoia, Sergio ; Taverna, Stefano ; Chiappalone, Michela
Dans : 6th International IEEE EMBS Conference on Neural Engineering, (United States)
https://hal.archives-ouvertes.fr/hal-00966013

Bifurcation analysis in a silicon neuron
Grassia, Filippo ; Levi, Timothée ; Saïghi, Sylvain ; Kohno, Takashi
Dans : International Symposium on Artificial Life and Robotics, Beppu (Japan)
https://hal.archives-ouvertes.fr/hal-00775856

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord by Electrical Microstimulations Driven by an Artificial CPG
Joucla, Sébastien ; Ambroise, Matthieu ; Levi, Timothée ; Lafon, Thierry ; Chauvet, P. ; Rousseau, L. ; Lissorgues, Gaelle ; Saïghi, Sylvain ; Bornat, Yannick ; Lewis, Noëlle ; Renaud, Sylvie ; Yvert, Blaise
Dans : GDR multielectrode systems & signal processing for neuroscience, (France)
https://hal.archives-ouvertes.fr/hal-00966014

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord by Electrical Microstimulations Driven by an Artificial CPG
Joucla, Sébastien ; Ambroise, Matthieu ; Levi, Timothée ; Lafon, Thierry ; Chauvet, P. ; Rousseau, L. ; Lissorgues, Gaelle ; Saïghi, Sylvain ; Bornat, Yannick ; Lewis, Noëlle ; Renaud, Sylvie ; Yvert, Blaise
Dans : 6th International IEEE EMBS Conference on Neural Engineering, (United States)
https://hal.archives-ouvertes.fr/hal-00966012

Réseau de Neurones Impulsionnels avec Synapses Memristives
Lecerf, Gwendal ; Tomas, Jean ; Saïghi, Sylvain
Dans : GDR SoC-SiP, Lyon (France)
https://hal.archives-ouvertes.fr/hal-00977884

Conception d'un réseau de neurones du cœur de sangsue
Levi, Timothée ; Ambroise, Matthieu ; Grassia, Filippo ; Kohno, Takashi
Dans : Journées Francophones de la Recherche JFR 2013, (Japan)
https://hal.archives-ouvertes.fr/hal-00966010

2011


A Neuromimetic Spiking Neural Network for Simulating Cortical Circuits
Grassia, Filippo ; Levi, Timothée ; Tomas, Jean ; Renaud, Sylvie ; Saïghi, Sylvain
Dans : 45th Annual Conference on Information Sciences ans Systems, Baltimore (United States)
https://hal.archives-ouvertes.fr/hal-00597648

2009


Automated Tuning of Analog Neuromimetic Integrated Circuits
Buhry, L. ; Saïghi, S. ; Giremus, A. ; Grivel, E. ; Renaud, S.
Dans : Conference on Biomedical Circuits and Systems, Beijing (China)
https://hal.archives-ouvertes.fr/hal-00438269

New variants of the Differential Evolution algorithm: application for neuroscientists
Buhry, Laure ; Giremus, Audrey ; Grivel, Eric ; Saïghi, Sylvain ; Renaud, S.
Dans : European Signal Processing conference, EUSIPCO, Glasgow (United Kingdom)
https://hal.archives-ouvertes.fr/hal-00400824

Adjusting Neuron Models in Neuromimetic ICs using the Differential Evolution Algorithm
Buhry, Laure ; Saïghi, Sylvain ; Ben Salem, Wajdi ; Sylvie Renaud, And
Dans : 4th IEEE EMBS Conference on Neural Engineering, Antalya (Turkey)
https://hal.archives-ouvertes.fr/hal-00381814

2008


Parameter estimation of the Hodgkin-Huxley model using metaheuristics: application to neuromimetic analog integrated circuits
Buhry, Laure ; Saïghi, Sylvain ; Giremus, Audrey ; Grivel, Eric ; Renaud, Sylvie
Dans : Biomedical Circuits and Systems Conference (BIOCAS), Baltimore (United States)
https://hal.archives-ouvertes.fr/hal-00347187

Estimation des paramétres du modéle d'Hodgkin-Huxley par des métaheuristiques
Buhry, Laure ; Saighi, Sylvain ; Giremus, Audrey ; Grivel, Eric ; Renaud, Sylvie
Dans : Deuxième conférence française de Neurosciences Computationnelles, "Neurocomp08", Marseille (France)
https://hal.archives-ouvertes.fr/hal-00331588

Réglage de paramètres neuronaux par des techniques de “voltage-clamp” sur des ICs neuromimétiques
Buhry, Laure ; Saïghi, Sylvain
Dans : JNRDM 2008, (France)
https://hal.archives-ouvertes.fr/hal-00288475

Weights Convergence and Spikes Correlation in an Adaptive Neural Network Implemented on VLSI
Daouzli, Adel ; Saïghi, Sylvain ; Buhry, Laure ; Bornat, Yannick ; Renaud, Sylvie
Dans : Bio-inspired Systems and Signal Processing (BIOSIGNALS), (France)
https://hal.archives-ouvertes.fr/hal-00288431

Adjusting the Neurons Models in Neuromimetic ICs using the Voltage-Clamp Technique
Saïghi, S. ; Buhry, L. ; Bornat, Y. ; N'Kaoua, G. ; Tomas, J. ; Renaud, S.
Dans : InternationaI Symposium on Circuits And Systems 2008 (ISCAS08), Seattle (United States)
https://hal.archives-ouvertes.fr/hal-00288432
Invited lectures → 1 Show

2013


Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks
Lecerf, Gwendal ; Tomas, Jean ; Saïghi, Sylvain
Dans : IEEE International Symposium on Circuits and Systems, (China)
https://hal.archives-ouvertes.fr/hal-00975440
Book chapters → 1 Show

2015


Biomimetic technologies Principles and Applications
Ambroise, Matthieu ; Levi, Timothée ; Saighi, Sylvain
https://hal.archives-ouvertes.fr/hal-01227602
Patents → 1 Show

2013


Organe à neurone artificiel et memristor
Saïghi, Sylvain ; Tomas, Jean ; Lecerf, Gwendal
https://hal.archives-ouvertes.fr/hal-00977874
Doctoral thesis → 7 Show

2021


Conception of neural networks on silicon using memristive synapses : application to image processing
Meyer, Charly
https://tel.archives-ouvertes.fr/tel-03556410

2019


Design of a modular biomimetic neural network for the study of neurodegenerative diseases
Khoyratee, Farad
https://tel.archives-ouvertes.fr/tel-02898185

Design of a modular biomimetic neural network for the study of neurodegenerativediseases
Khoyratee, Farad
https://hal.archives-ouvertes.fr/tel-02527385

2015


Hybridization of neural network: from network design to neuromorphic system interoperability
Ambroise, Matthieu
https://hal.archives-ouvertes.fr/tel-02527419

2014


Development of a silicon spiking neural network with memristives synapses
Lecerf, Gwendal
https://tel.archives-ouvertes.fr/tel-01137492

2013


Silicon neural networks : implementation of cortical cells to improve the artificial-biological hybrid technique
Grassia, Filippo Giovanni
https://tel.archives-ouvertes.fr/tel-00789406

2010


Estimation de paramètres de modèles de neurones biologiques sur une plate-forme de SNN (Spiking Neural Network) implantés "in silico"
Buhry, Laure
https://tel.archives-ouvertes.fr/tel-00561396