Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

Vannucci, Lorenzo; Falotico, Egidio; Laschi, Cecilia

doi:10.3389/fnins.2017.00341

Cited by 12 publications

(19 citation statements)

References 38 publications

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“…Ia afferents directly provide excitatory inputs to the α-motoneurons (monosynaptic stretch reflex mechanism), while the II afferents output is mediated by a set of interneurons before reaching the α-motoneurons, creating a disynaptic reflex. The muscle spindles are implemented using the model from Vannucci et al ( 2017 ). All other neurons are modeled as leaky integrate and fire neurons.…”

Section: Modelsmentioning

confidence: 99%

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Mascaro

Falotico

Petkoski

et al. 2020

Front. Syst. Neurosci.

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Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

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Section: Modelsmentioning

confidence: 99%

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Mascaro

Falotico

Petkoski

et al. 2020

Front. Syst. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ia afferents directly provide excitatory inputs to the α -motoneurons (monosynaptic stretch reflex mechanism), while the II afferents output is mediated by a set of interneurons before reaching the α -motoneurons, creating a disynaptic reflex. The muscle spindles are implemented using the model from (Vannucci et al, 2017). All other neurons are modeled as leaky integrate and fire neurons.…”

Section: Modelsmentioning

confidence: 99%

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

Mascaro

Falotico

Petkoski

et al. 2020

Preprint

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2Being able to replicate real experiments with computational simulations is a unique opportunity 3 to refine and validate models with experimental data and redesign the experiments based on 4 simulations. However, since it is technically demanding to model all components of an experiment, 5 traditional approaches to modeling reduce the experimental setups as much as possible. In this 6 1 Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationsstudy, our goal is to replicate all the relevant features of an experiment on motor control and 7 motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous 8 integration of new experimental data into a computational modeling framework. First, results show 9 that we could reproduce experimental object displacement with high accuracy via the simulated 10 embodiment in the virtual world by feeding a spinal cord model with experimental registration of the 11 cortical activity. Second, by using computational models of multiple granularities, our preliminary 12 results show the possibility of simulating several features of the brain after stroke, from the 13 local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies 14 are proposed to merge the two pipelines. We further suggest that additional models could be 15 integrated into the framework thanks to the versatility of the proposed approach, thus allowing 16 many researchers to achieve continuously improved experimental design. 17 18 response of the environment itself, in that the output of the brain is relevant only if it has the ability to 19 impact the future and hence the input the brain receives. This "closed-loop" can be simulated in a virtual 20 world, where simulated experiments reproduce actions (output from the brain) that have consequences 21 (future input to the brain) (Zrenner et al., 2016). To the aim of reproducing in silico the complexity of real 22 experiments, different levels of modeling shall be integrated. However, since modeling all components of an 23 experiment is very difficult, traditional approaches of computational neuroscience reduce the experimental 24 setups as much as possible. An "Embodied brain" (or "task dynamics", see Zrenner et al. (2016)) approach Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationscould overcome these limits by associating the modelled brain activity with the generation of behavior 25 within a virtual or real environment, i.e. an entailment between an output of the brain and a feedback 26 signal into the brain (Tessadori et al., 2012; DeMarse et al., 2001; Reger et al., 2000). The experimenter 27 can interfere with the flow of information between the neural system and environment on the one hand 28 and the state and transition dynamics of the environment on the other. Closing the loop can be performed 29 effectively by (i) validating the models on experimental data, and (ii) designing new experiments based on 30 the hypotheses f...

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“…Nevertheless, the question is how neural mechanisms tolerate speed dependency to achieve stable texture discrimination. Perhaps, human sensory fusion can construct multifaceted sensory perception during active touch; information from muscle spindles 9 and Ruffini endings contribute to estimating scanning speed as the proprioceptive feedback 14,24 . Skeletal muscles play a vital role in maintaining posture and balance 25 .…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the biological mechanisms in neuroscience, neuromorphic engineering is an interdisciplinary field that aims to emulate neuronal activities with the aid of electronic devices to encode external stimuli as a sequence of action potentials (spike trains) 7,8 . In particular, a neuromorphic system translates sensory information into a pattern of spikes for the communication of sensors with the brain model simulations 9 . The representation of the sensor's data by spikes possesses an anti-noise capability and offers a reliable propagation 10 .…”

mentioning

confidence: 99%

“…These bio-inspired systems attempt to provide the skin-like sensory capability to prosthetic/robotic hands to deliver valuable information about joints (proprioception) and estimate realistic force to grasp objects 12 . In other words, advances in neuromorphic research allow the development of artificial mechanoreceptors to produce spike trains 9 . Prior research studies have discriminated roughness using artificial tactile sensors or artificial fingertips 10,13 .…”

mentioning

confidence: 99%

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Texture recognition based on multi-sensory integration of proprioceptive and tactile signals

Rostamian

Koolani

Abdollahzade

et al. 2022

Sci Rep

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The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors’ outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.

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Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

Cited by 12 publications

References 38 publications

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

Texture recognition based on multi-sensory integration of proprioceptive and tactile signals

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