Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Unveil the neural mechanisms behind the adaptation to neurostimulation
In this project we want to unveil mechanisms behind the neural adaptation that occurs during intraneural neuromodulation and to emulate the neural process to prevent, in the real-time system.
Direct nerve stimulation can be used to provide sensory feedback to upper and lower-limb amputees. The sensory feedback allowed them to discriminate, during manipulation through a prosthetic hand (bionic hand), objects of different compliances and shapes or to improve their mobility/agility while walking (bionic leg). The intensity of the elicited sensations can be modulated using the amplitude (Linear Amplitude Modulation - LAM) or frequency (Linear Frequency Modulation - LFM) of the injected stimuli.
One of the drawbacks of the direct nerve stimulation is the adaptation which caused the patients to stop perceiving the restored sensations (even to low-frequency stimulation). In particular, when long-lasting trains of stimulation were delivered, the LFM stimulation (> 500 Hz) generated a fast adaptation (~ s) and can last up to tens of few minutes.
In this project we want to unveil mechanisms behind the neural adaptation that occurs during intraneural neuromodulation and to emulate the neural process to prevent, in the real-time system. Although the adaptation can occur at different levels of the afferent pathway, the project goal is to investigate the neural response to Temporal Pattern stimulation at the network level. The network should simulate/emulate the adaptation times of the experimental data. Initially the network will be simulated in Brian, and afterwards it will be ported into a neuromorphic chip.
Direct nerve stimulation can be used to provide sensory feedback to upper and lower-limb amputees. The sensory feedback allowed them to discriminate, during manipulation through a prosthetic hand (bionic hand), objects of different compliances and shapes or to improve their mobility/agility while walking (bionic leg). The intensity of the elicited sensations can be modulated using the amplitude (Linear Amplitude Modulation - LAM) or frequency (Linear Frequency Modulation - LFM) of the injected stimuli. One of the drawbacks of the direct nerve stimulation is the adaptation which caused the patients to stop perceiving the restored sensations (even to low-frequency stimulation). In particular, when long-lasting trains of stimulation were delivered, the LFM stimulation (> 500 Hz) generated a fast adaptation (~ s) and can last up to tens of few minutes. In this project we want to unveil mechanisms behind the neural adaptation that occurs during intraneural neuromodulation and to emulate the neural process to prevent, in the real-time system. Although the adaptation can occur at different levels of the afferent pathway, the project goal is to investigate the neural response to Temporal Pattern stimulation at the network level. The network should simulate/emulate the adaptation times of the experimental data. Initially the network will be simulated in Brian, and afterwards it will be ported into a neuromorphic chip.
In this project, we want to unveil mechanisms behind the neural adaptation that occurs during intraneural neuromodulation and to emulate the neural process to prevent, in the real-time system, the fast adaptation.
The adaptation can occur at different levels of the afferent pathway, we believe that three different mechanisms concur in the final adaptation:
● Spike frequency adaptation (SFA) in the afferent nerve (in the Dorsal Horn, in the Dorsal Root Ganglion)
● Short time depression (STD), at the level of the first synapses (at the interface between PNS and CNS)
● E-I network balance at level of sensory-motor cortex
The three mechanisms present different time constant, SFA and STD are in the order of ms, the E-I network can reach a time constant for the adaptation of the same order as the one observed in the experiments. Although the leading mechanism is clearly the one due to the E-I network, we believe it’s worth to exploit all the free mechanisms.
Once the network works on simulations, and once the new chip is available, the network will be port into the chip. The new chip, named Dynapse2, is the second generation of the Dynamic Neuromorphic Asynchronous Processors (DYNAP-SE). The new chip enhances the processing and energy-efficiency capabilities of the previous generation with the addition of a bio-signal analog front-end (AFE). It integrates analog circuits that emulate the behavior of biological neurons and synapses, and digital logic circuits for configuration and
In this project, we want to unveil mechanisms behind the neural adaptation that occurs during intraneural neuromodulation and to emulate the neural process to prevent, in the real-time system, the fast adaptation. The adaptation can occur at different levels of the afferent pathway, we believe that three different mechanisms concur in the final adaptation: ● Spike frequency adaptation (SFA) in the afferent nerve (in the Dorsal Horn, in the Dorsal Root Ganglion) ● Short time depression (STD), at the level of the first synapses (at the interface between PNS and CNS) ● E-I network balance at level of sensory-motor cortex The three mechanisms present different time constant, SFA and STD are in the order of ms, the E-I network can reach a time constant for the adaptation of the same order as the one observed in the experiments. Although the leading mechanism is clearly the one due to the E-I network, we believe it’s worth to exploit all the free mechanisms. Once the network works on simulations, and once the new chip is available, the network will be port into the chip. The new chip, named Dynapse2, is the second generation of the Dynamic Neuromorphic Asynchronous Processors (DYNAP-SE). The new chip enhances the processing and energy-efficiency capabilities of the previous generation with the addition of a bio-signal analog front-end (AFE). It integrates analog circuits that emulate the behavior of biological neurons and synapses, and digital logic circuits for configuration and
Dr. Giacomo Valle Postdoc @ NeuroEngLab giacomo.valle@hest.ethz.ch
Dr. Giacomo Valle Postdoc @ NeuroEngLab giacomo.valle@hest.ethz.ch