arXiv subjects

Stefano Vassanelli

Publications and source records attributed to Stefano Vassanelli.

7 recordsLinked to original sources

arXiv2025

Implantable CMOS probes for high resolution Electrical Imaging of Local Field Potentials Across the Rat Barrel Cortex in vivo

High-resolution recordings of extracellular potentials are fundamental for the study of neuronal networks, the basis of neuronal coding and transmission. Here we present an innovative method for an in vivo electrical imaging of Local Field Potentials using novel implantable neural interfaces with a high-density array of 256 recording sites and a spatial resolution of 15 μm. Thanks to this technology, we analyze the propagation of whisker-evoked activity in a single barrel-column of the rat somatosensory cortex.

q-bio.NC↗

arXiv2022

Disentangling the critical signatures of neural activity

The critical brain hypothesis has emerged as an attractive framework to understand neuronal activity, but it is still widely debated. In this work, we analyze data from a multi-electrodes array in the rat's cortex and we find that power-law neuronal avalanches satisfying the crackling-noise relation coexist with spatial correlations that display typical features of critical systems. In order to shed a light on the underlying mechanisms at the origin of these signatures of criticality, we introduce a paradigmatic framework with a common stochastic modulation and pairwise linear interactions inferred from our data. We show that in such models power-law avalanches that satisfy the crackling noise relation emerge as a consequence of the extrinsic modulation, whereas scale-free correlations are solely determined by internal interactions. Moreover, this disentangling is fully captured by the mutual information in the system. Finally, we show that analogous power-law avalanches are found in more realistic models of neural activity as well, suggesting that extrinsic modulation might be a broad mechanism for their generation.

cond-mat.stat-mech↗

arXiv2018

Applications of Deep Learning and Reinforcement Learning to Biological Data

Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. The growth in computational power accompanied by faster and increased data storage and declining computing costs have already allowed scientists in various fields to apply these techniques on datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

cs.LG↗

arXiv2017

A geographically distributed bio-hybrid neural network with memristive plasticity

Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired and bio-hybrid architectures are just beginning to materialise. The use of memristive technologies in brain-inspired architectures for computing or for sensing spiking activity of biological neurons8 are only recent examples, however interlinking brain and electronic neurons through plasticity-driven synaptic elements has remained so far in the realm of the imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where memristors work as "synaptors" between rat neural circuits and VLSI neurons. The two fundamental synaptors, from artificial-to-biological (ABsyn) and from biological-to- artificial (BAsyn), are interconnected over the Internet. The bNN extends across Europe, collapsing spatial boundaries existing in natural brain networks and laying the foundations of a new geographically distributed and evolving architecture: the Internet of Neuro-electronics (IoN).

cs.ET↗

arXiv2016

Sub 100nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes

Advanced neural interfaces mediate a bio-electronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require on-line processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multi-electrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

cs.ET↗

arXiv2015

Enhancement of Kv1.3 Potassium Conductance by Extremely Low Frequency Electromagnetic Field

Theoretical and experimental evidences support the hypothesis that extremely low-frequency electromagnetic fields can affect voltage-gated channels. Little is known, however, about their effect on potassium channels. Kv1.3, a member of the voltage-gated potassium channels family originally discovered in the brain, is a key player in important biological processes including antigen-dependent activation of T-cells during the immune response. We report that Kv1.3 expressed in CHO-K1 cells can be modulated in cell subpopulations by extremely low frequency and relatively low intensity electromagnetic fields. In particular, we observed that field exposure can cause an enhancement of Kv1.3 potassium conductance and that the effect lasts for several minutes after field removal. The results contribute to put immune and nervous system responses to extremely low-frequency electromagnetic fields into a new perspective, with Kv1.3 playing a pivotal molecular role. Keywords: immunotherapy, immunomodulation, potassium channels, gating, electromagnetic fields

q-bio.SC↗

arXiv2015

Memristive integrative sensors for neuronal activity

The advent of advanced neuronal interfaces offers great promise for linking brain functions to electronics. A major bottleneck in achieving this is real-time processing of big data that imposes excessive requirements on bandwidth, energy and computation capacity; limiting the overall number of bio-electronic links. Here, we present a novel monitoring system concept that exploits the intrinsic properties of memristors for processing neural information in real time. We demonstrate that the inherent voltage thresholds of solid-state TiOx memristors can be useful for discriminating significant neural activity, i.e. spiking events, from noise. When compared with a multi-dimensional, principal component feature space threshold detector, our system is capable of recording the majority of significant events, without resorting to computationally heavy off-line processing. We also show a memristive integrating sensing array that discriminates neuronal activity recorded in-vitro. We prove that information on spiking event amplitude is simultaneously transduced and stored as non-volatile resistive state transitions, allowing for more efficient data compression, demonstrating the memristors' potential for building scalable, yet energy efficient on-node processors for big data.

cs.ET↗