References of "Dethier, Julie"
     in
Bookmark and Share    
See detailMon cerveau, un ordinateur ?!
Dethier, Julie ULg; Noirhomme, Quentin; Van Calster, Laurens

Conference given outside the academic context (2014)

Dôter les ordinateur d'un cerveau ou faire assister nos cerveaux par des ordinateurs ? Trois chercheurs abordaient ces thématiques lors d'un Doc'Café mi-homme, mi-machine.

Detailed reference viewed: 11 (4 ULg)
See detailModulation of beta oscillations by dopamine: novel insights from a STN-GPe network model
Dethier, Julie ULg

Scientific conference (2013, September 06)

Detailed reference viewed: 20 (6 ULg)
See detailOscillations in the basal ganglia: illustration of a cellular effect at the network level
Dethier, Julie ULg; Drion, Guillaume; Franci, Alessio et al

Poster (2013, June)

Parkinson’s disease (PD) is a neuro-degenerative pathology affecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of ... [more ▼]

Parkinson’s disease (PD) is a neuro-degenerative pathology affecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of the striatum, al- tering information patterns along movement-related ganglia-mediated path- ways in the brain. Severe motor symptoms result from the pathological state: tremor at rest, bradykinesia, akinesia, and rigidity. The transition to the disease state correlates with a switch in the firing mode of the neurons in the BG, from tonic pacemaker activity to burst firing. At the network level, macro-electrode recordings reveal excessive oscillations in the beta (8- 30Hz) frequency band. The oscillations generation mechanism and their functional role remain under debate. We propose a network model where a cellular mechanism controls the dynamical state of the network. In our model, the oscillatory state impacts the neural information processing prop- erties of the network. The network model predicts that a single decrease of the dopaminergic level in the parkinsonnian condition switches the network into an abnormal oscillatory dynamical and globally insensitive state. The brief dopaminergic increase prior to voluntary movements suppresses beta oscillations to drive the network to a conductive state to sensory processing and cognition. [less ▲]

Detailed reference viewed: 38 (6 ULg)
Full Text
See detailImpacts of a unicellular mechanism on network behaviors
Dethier, Julie ULg; Drion, Guillaume; Franci, Alessio et al

Conference (2013, March 26)

Parkinson’s disease (PD) is a neurodegenerative disorder af- fecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of ... [more ▼]

Parkinson’s disease (PD) is a neurodegenerative disorder af- fecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of the striatum—the input stage of the BG—altering information patterns along movement- related ganglia-mediated pathways in the brain. Severe mo- tor symptoms result from the pathological state: tremor at rest, bradykinesia—the slowness and impaired scaling of voluntary movement—and akinesia—the poverty of volun- tary movements. It is still unclear how dopamine depletion causes those motor symptoms. Experimental studies have shown that abnormally synchronized oscillatory activities— rhythmic bursting activity at the unicellular level and beta frequency band (from 8 to 30Hz) oscillations at the network level—emerge in PD at multiple levels of the BG-cortical loops and correlate with motor symptoms. The mechanisms underlying these pathological beta oscillations remain elu- sive. We propose that a cellular mechanism generates burst- ing activities and beta band oscillations at the network level. [less ▲]

Detailed reference viewed: 65 (11 ULg)
Full Text
Peer Reviewed
See detailDesign and validation of a real-time spiking-neural-network decoder for brain–machine interfaces
Dethier, Julie ULg; Nuyujukian, Paul; Ryu, Stephen I et al

in Journal of Neural Engineering (2013), 10(3),

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to ... [more ▼]

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain–machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF. [less ▲]

Detailed reference viewed: 26 (8 ULg)
See detailSingle-cell activity and local field potentials in the ventral tegmental area of awake, freely moving rats
Stanislav, Koulchitsky; Dethier, Julie ULg; Bullinger, Eric et al

Poster (2012, October 16)

The ventral tegmental area (VTA) is known to be involved in reward analysis and detection of salience of events. It contains dopaminergic (DA), GABAergic and perhaps other neurons. Much is known about the ... [more ▼]

The ventral tegmental area (VTA) is known to be involved in reward analysis and detection of salience of events. It contains dopaminergic (DA), GABAergic and perhaps other neurons. Much is known about the activity of DA neurons in anaesthetized animals and brain slices. However, there is a clear lack of data on their dynamic regulation in awake animals, although their reactivity to drugs of abuse, for example, is dramatically different in these different conditions (Koulchitsky et al., 2012). Moreover, little is known about the network activity of the VTA. <br /><br />Using a telemetric recording system and 8-microelectrode-arrays, spanning most of the extent of the VTA, we observed both single cell activity and local field potentials (LFPs), as recently described by others (Fujisawa and Buzsáki, 2011). Neurons were considered as being DA when their firing rate was decreased by more than 50% by an i.p. injection of 100 µg/kg of quinpirole. <br /><br />The firing rate of individual DA neurons was very variable over time, as was the spectrum of LFPs. In many cases, sharp changes in the firing rate/bursting of some DA neurons correlated with an increase in the amplitude of the theta band (~5-8 Hz). They were also coherent to the locomotor activity of the animals. However, the extent to which the firing rate of individual neurons within a rat correlated with the increased theta rhythm was very variable, suggesting that the functional connectivity of different DA neurons at a given moment is quite heterogenous. We are currently attempting to use our recordings to infer a “functional topography” of the VTA. [less ▲]

Detailed reference viewed: 84 (21 ULg)
See detailA unicellular mechanism to switch a network behavior from tonic activity to synchronous oscillations
Dethier, Julie ULg; Drion, Guillaume; Franci, Alessio et al

Poster (2012, October 08)

Parkinson’s disease (PD) is a neurodegenerative disorder affecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of ... [more ▼]

Parkinson’s disease (PD) is a neurodegenerative disorder affecting the basal ganglia (BG), a set of small subcortical nervous system nuclei. The hallmark of the disease is a dopaminergic denervation of the input stage of the BG, altering information patterns along movement-related ganglia-mediated pathways in the brain, inducing therefore movement disorders such as tremor at rest, bradykinesia, akinesia, and rigidity. It is still unclear how dopamine depletion causes those motor symptoms. Experimental studies have shown that abnormally synchronized oscillatory activities- rhythmic bursting activity at the neurocellular level and beta frequency band oscillations at the network level-emerge in PD at multiple levels of the BG-cortical loops and are correlated with motor symptoms. We propose a computational model of the BG using a novel unicellular mechanism to explain the induction of bursting activity and beta band oscillations in the network. We show how a single change in the dopaminergic level at the input stage of the BG can switch the model from its physiological state to the pathological state. This computational model also proposes a simple mechanism for high-frequency deep brain stimulations. [less ▲]

Detailed reference viewed: 44 (13 ULg)
See detailRhythms in Neuromorphic Reinforcement Learning
Dethier, Julie ULg; Ernst, Damien; Sepulchre, Rodolphe

Poster (2012, May 28)

Living organisms are able to successfully perform challenging tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic ... [more ▼]

Living organisms are able to successfully perform challenging tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic engineering uses neurophysiological models of information processing in biological systems to emulate their functions. Our focus lies in the basal ganglia (BG) and specifically on their involvement in action selection and reinforcement learning (RL). The BG are a group of interconnected subcortical nuclei that participate in cortical-­‐ and sub-­‐cortical loops for limbic, associative, and sensorimotor functions. These loops are topographically organized in relatively discrete channels that loop back, via appropriate thalamic relays, to the same area of cortex they originated from. The action selection mechanism comes directly from the BG architecture: the parallel channels compete for behavioral resources, conveying phasic excitatory signals–bids for selection–to the input nuclei. Through comparison of input magnitudes, the tonic inhibitory output is withdrawn from selected channels and maintained or increased on non-­‐selected channels, releasing or preventing action, respectively. This action selection model can be exploited in Cognitive Pattern Generators, analogue to the motor system's Central Pattern Generators, rhythm generators that operate to organize cognition. The BG play also a critical role in reward and RL circuits. Phasic firing in midbrain dopaminergic neurons complies with the reward prediction error signal of contemporary learning theories. This mechanism could explain cognitive functions, e.g. conditioning memory, and dysfunctions, e.g. Parkinson’s and schizophrenia. Modeling rhythms at the neurocellular level could introduce the rhythmic component required at the network level for both action selection and RL. The first step in this project is the modeling of the BG and their parallel processing loops with this rhythmic component, a subject of ongoing research. [less ▲]

Detailed reference viewed: 44 (8 ULg)
Full Text
See detailNeuromorphic reinforcement learning
Dethier, Julie ULg; Ernst, Damien; Sepulchre, Rodolphe

Conference (2012, March 29)

Living organisms are able to successfully perform challeng- ing tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic ... [more ▼]

Living organisms are able to successfully perform challeng- ing tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic engineering uses neurophysiological models of perception and information processing in biological sys- tems to emulate their functions but also resemble their struc- ture [1]. In this abstract, we focus on the basal ganglia (BG), brain region in control of primitive functions of the nervous system, and specifically on their involvement in action selec- tion and reinforcement learning (RL). We hypothesize that neuromorphic-inspired systems will greatly benefit the RL community. [less ▲]

Detailed reference viewed: 78 (19 ULg)
Full Text
See detailA Brain-Machine Interface with an Innovative Spiking Neural Network Decoder
Dethier, Julie ULg; Nuyujukian, Paul; Elassaad, Shauki A. et al

Poster (2011, December 02)

Motor prostheses aim to restore functions lost to neurological disease and injury by translating neural signals into control signals for prosthetic limbs. Despite compelling proof of concept systems ... [more ▼]

Motor prostheses aim to restore functions lost to neurological disease and injury by translating neural signals into control signals for prosthetic limbs. Despite compelling proof of concept systems, barriers to clinical translation—mainly strict power dissipation constraints—still remain. The proposed solution is to use the ultra-low-power neuromorphic approach to potentially meet these constraints. [less ▲]

Detailed reference viewed: 57 (24 ULg)
Full Text
Peer Reviewed
See detailA Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Dethier, Julie ULg; Nuyujukian, Paul; Eliasmith, Chris et al

in Advances in Neural Information Processing Systems (NIPS) 24 (2011, December)

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully ... [more ▼]

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neu- romorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses. [less ▲]

Detailed reference viewed: 316 (41 ULg)
See detailA Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Dethier, Julie ULg; Nuyujukian, Paul; Elassaad, Shauki A. et al

Poster (2011, November 29)

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully ... [more ▼]

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses. [less ▲]

Detailed reference viewed: 27 (12 ULg)
Full Text
See detailSpiking Neural Network Decoder for Brain‐Machine Interfaces
Dethier, Julie ULg; Nuyujukian, Paul; Elassaad, Shauki .A. et al

Scientific conference (2011, November 28)

We used a spiking neural network (SNN) to decode neural data recorded from two 96-­electrode arrays in premotor and motor cortex while a rhesus monkey performed a point-­to-­point reaching arm movement ... [more ▼]

We used a spiking neural network (SNN) to decode neural data recorded from two 96-­electrode arrays in premotor and motor cortex while a rhesus monkey performed a point-­to-­point reaching arm movement task. We mapped a Kalman­‐filter neural prosthetic decode algorithm developed to predict the arm’s velocity on to the SNN using the Neural Engineering Framework and tested it in brain-­‐machine interface (BMI) experiments with a rhesus monkey. A 2,000­‐neuron embedded Matlab SNN implementation runs in real­‐time and its closed­‐loop performance is quite comparable to that of the standard Kalman filter. The success of this closed­‐loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses. [less ▲]

Detailed reference viewed: 18 (7 ULg)
Full Text
See detailPourquoi le génie biomédical?
Dethier, Julie ULg

Learning material (2011)

Detailed reference viewed: 25 (9 ULg)
Full Text
Peer Reviewed
See detailSpiking Neural Network Decoder for Brain-Machine Interfaces
Dethier, Julie ULg; Gilja, Vikash; Nuyujukian, Paul et al

in Proceedings of the 5th International IEEE EMBS Conference on Neural Engineering (2011, May)

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We ... [more ▼]

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm’s velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder’s prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations—neuromorphic chips—may offer power savings, essential for realizing fully-implantable cortically controlled prostheses. [less ▲]

Detailed reference viewed: 39 (10 ULg)
Full Text
See detailKalman-filter in a neural network
Dethier, Julie ULg

Report (2010)

Detailed reference viewed: 22 (1 ULg)
Full Text
See detailKalman-filter based decoder in spiking neural networks
Dethier, Julie ULg

Report (2010)

The initial focus of the project is on implementing an existing Kalman-filter based decoder algorithm that controls a 2D computer cursor [GIL2010] into Neurogrids 2D silicon- neuron arrays. Neurogrid ... [more ▼]

The initial focus of the project is on implementing an existing Kalman-filter based decoder algorithm that controls a 2D computer cursor [GIL2010] into Neurogrids 2D silicon- neuron arrays. Neurogrid realizes such functions at negligible energetic cost [BOA2010]. This document gives the necessary background for a Kalman-filter based decoder implementation in a spiking neurl network (SNN). [less ▲]

Detailed reference viewed: 53 (5 ULg)