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).Comment: 16 pages, 10 figure

A walk on the frontier of energy electronics with power ultra-wide bandgap oxides and ultra-thin neuromorphic 2D materials

Altres ajuts: the ICN2 is funded also by the CERCA programme / Generalitat de CatalunyaUltra-wide bandgap (UWBG) semiconductors and ultra-thin two-dimensional materials (2D) are at the very frontier of the electronics for energy management or energy electronics. A new generation of UWBG semiconductors will open new territories for higher power rated power electronics and deeper ultraviolet optoelectronics. Gallium oxide - GaO(4.5-4.9 eV), has recently emerged as a suitable platform for extending the limits which are set by conventional (-3 eV) WBG e.g. SiC and GaN and transparent conductive oxides (TCO) e.g. In2O3, ZnO, SnO2. Besides, GaO, the first efficient oxide semiconductor for energy electronics, is opening the door to many more semiconductor oxides (indeed, the largest family of UWBGs) to be investigated. Among these new power electronic materials, ZnGa2O4 (-5 eV) enables bipolar energy electronics, based on a spinel chemistry, for the first time. In the lower power rating end, power consumption also is also a main issue for modern computers and supercomputers. With the predicted end of the Moores law, the memory wall and the heat wall, new electronics materials and new computing paradigms are required to balance the big data (information) and energy requirements, just as the human brain does. Atomically thin 2D-materials, and the rich associated material systems (e.g. graphene (metal), MoS2 (semiconductor) and h-BN (insulator)), have also attracted a lot of attention recently for beyond-silicon neuromorphic computing with record ultra-low power consumption. Thus, energy nanoelectronics based on UWBG and 2D materials are simultaneously extending the current frontiers of electronics and addressing the issue of electricity consumption, a central theme in the actions against climate chang

Quantum materials for energy-efficient neuromorphic computing

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems

A hybrid optoelectronic Mott insulator

The coupling of electronic degrees of freedom in materials to create "hybridized functionalities" is a holy grail of modern condensed matter physics that may produce versatile mechanisms of control. Correlated electron systems often exhibit coupled degrees of freedom with a high degree of tunability which sometimes lead to hybridized functionalities based on external stimuli. However, the mechanisms of tunability and the sensitivity to external stimuli are determined by intrinsic material properties which are not always controllable. A Mott metal-insulator transition (MIT) is technologically attractive due to the large changes in resistance, tunable by doping, strain, electric fields, and orbital occupancy but not, in and of itself, controllable with light. Here, an alternate approach is presented to produce optical functionalities using a properly engineered photoconductor/strongly correlated hybrid heterostructure. This approach combines a photoconductor, which does not exhibit an MIT, with a strongly correlated oxide, which is not photoconducting. Due to the intimate proximity between the two materials, the heterostructure exhibits giant volatile and nonvolatile, photoinduced resistivity changes with substantial shifts in the MIT transition temperatures. This approach can be extended to other judicious combinations of strongly correlated materials

Molecular Layer Functionalized Neuroelectronic Interfaces: From Sub-Nanometer Molecular Surface Functionalization to Improved Mechanical and Electronic Cell-Chip Coupling

The interface between electronic components and biological objects plays a crucial role for the success of bioelectronic devices. Since the electronics typically include different elements such as an insulating substrate in combination with conducting electrodes, an important issue of bioelectronics involves tailoring and optimizing the interface for any envisioned application. In this work, we present a method of functionalizing insulating substrates (SiO2) and metallic electrodes (Pt) simultaneously with a stable monolayer of organic molecules ((3-aminopropyl)triethoxysilane (APTES)). This monolayer is characterized by various techniques like atomic force microscope (AFM), ellipsometry, time-of-flight secondary ion mass spectrometry (ToF-SIMS), surface plasmon resonance (SPR), and streaming potential measurements. The molecule layers of APTES on both substrates, Pt and SiO2, show a high molecule density, a coverage of ~ 50 %, a long-term stability (at least one year), a positive surface net charge, and the characteristics of a self-assembled monolayer (SAM). In the electronical characterization of the functionalized Pt electrodes via impedance spectroscopy measurements, the static properties of the electronic double layer could be separated from the diffusive part using a specially developed model. It could be demonstrated that compared to cleaned Pt electrodes the double layer capacitance is increased by an APTES coating and the charge transfer resistance is reduced, which leads to a total increase of the electronic signal transfer of ~13 %. In the final cell culture measurements, it could be shown that an APTES coating facilitates a conversion of bio-unfriendly Pt surfaces into biocompatible surfaces which allows cell growth (neurons) on both functionalized components (SiO2 and Pt) comparable to that of reference samples coated with poly-L-lysine. Furthermore, APTES coating leads to an improved mechanical coupling, which increases the sealing resistance and reduces losses. These increases were finally confirmed by electronic measurements on neurons, which showed action potential signals in the mV regime compared to signals of typically 200 – 400 µV obtained for reference measurements on PLL coated samples. Therefore, the functionalization with APTES molecules seems to be able to greatly improve the electronic cell-chip coupling (here by ~1 500 %). This significant increase of the electronic and mechanical cell-chip coupling might represent an important step for the improvement of neuroelectronic sensor and actuator devices