Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations

Dynamic recruitment of transcription factors and epigenetic changes on the ER stress response gene promoters

Response to stresses that alter the function of the endoplasmic reticulum is an important cellular function, which relies on the activation of specific genes. Several transcription factors (TFs) are known to affect this pathway. Using RT–PCR and ChIP assays, we studied the recruitment of promoter-specific TFs, general TFs and epigenetic marks in activated promoters. H3-K4 di- and tri-methylation and H3-K79 di-methylation are present before induction. H3 acetylation is generally high before induction, and H4 acetylation shows a promoter-specific increase. Interestingly, there is a depletion of histone H3 under maximal induction, explaining an apparent decrease of H3-K4 tri-methylation and H3-K79 di-methylation. Pol II is found enriched on some promoters under basal conditions, unlike TBP and p300, which are recruited selectively. Most genes are bound by XBP-1 after induction, some before induction, presumably by the inactive isoform. ATF6 and CHOP associate to largely different set of genes. C/EBPβ is selective and binding to the CHOP promoter precedes that of XBP-1, ATF6 and CHOP. Finally, one of the ER-stress inducible genes analyzed, HRD1, is not bound by any of these factors. Among the constitutive TFs, NF-Y, but not Sp1, is found on all genes before induction. Intriguingly, siRNA interference of the NF-YB subunit indicates transcriptional impairment of some, but not all genes. These data highlight a previously unappreciated complexity of TFs binding and epigenetic changes, pointing to different TFs-specific pathways within this broad response

Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures

Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals. As in biology, to minimize power consumption, the silicon neurons' circuits are configured to fire with a limited dynamic range and with maximum firing rates restricted to a few tens or hundreds of Herz. However, biosignals can have a very large dynamic range, so encoding them into spikes without saturating the neuron outputs represents an open challenge. In this work, we present a biologically-inspired strategy for compressing this high-dynamic range in SNN architectures, using three adaptation mechanisms ubiquitous in the brain: spike-frequency adaptation at the single neuron level, feed-forward inhibitory connections from neurons belonging to the input layer, and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons in the output layer. We apply this strategy to input biosignals encoded using both an asynchronous delta modulation method and an energy-based pulse-frequency modulation method. We validate this approach in silico, simulating a simple network applied to a gesture classification task from surface EMG recordings.Comment: 5 pages, 7 figures, to be published in IEEE BioCAS 2023 Proceeding

NEUROTECH - A European Community of Experts on Neuromorphic Technologies

Neuromorphic Computing Technology (NCT) is becoming a reality in Europe thanks to a coordinated effort to unite the EU researchers and stakeholders interested in neuroscience, artificial intelligence, and nanoscale technologies