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As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron. A standard flux memory cell…
Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses…
The highly parallel process in the neuron networks is mediated through a mass of synaptic interconnections. Mimicking single synapse behaviors and highly paralleled neural networks has become more and more fascinating and important. Here,…
A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among various…
Homeostatic plasticity is a stabilizing mechanism commonly observed in real neural systems that allows neurons to maintain their activity around a functional operating point. This phenomenon can be used in neuromorphic systems to compensate…
Nature inspired neuromorphic architectures are being explored as an alternative to imminent limitations of conventional complementary metal-oxide semiconductor (CMOS) architectures. Utilization of such architectures for practical…
Biological cells in soft materials can be modeled as anisotropic force contraction dipoles. The corresponding elastic interaction potentials are long-ranged ($\sim 1/r^3$ with distance $r$) and depend sensitively on elastic constants,…
Neuromorphic computing offers a pathway toward energy-efficient processing of data, yet hardware platforms combining nanoscale integration and multimodal functionality remain scarce. Here we demonstrate a gallium-phosphide…
The unprecedented dissemination of edge devices is accompanied by a growing demand for neuromorphic chips that can process time-series data natively without cloud support. Echo state network (ESN) is a class of recurrent neural networks…
One of the major limitation of standard top-down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, we show how bipolar electropolymerization can be used to engineer 3D…
Wearable human activity sensors developed in the past decade show a distinct trend of becoming thinner and more imperceptible while retaining their electrical qualities, with graphene e-tattoos, as the ultimate example. A persistent…
Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the…
Using molecular dynamics simulations, we reveal emergent properties of hydrated electrode interfaces that while molecular in origin are integral to the behavior of the system across long times scales and large length scales. Specifically,…
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depends on their specific…
Active metasurfaces incorporating electro-optic (EO) materials enable high-speed free-space optical modulators that show great promise for a wide range of emerging applications, including free-space optical communication, light detection…
Short-term changes in efficacy have been postulated to enhance the ability of synapses to transmit information between neurons, and within neuronal networks. Even at the level of connections between single neurons, direct confirmation of…
The dynamics of memristive device in response to neuron-like signals and coupling electronic neurons via memristive device has been investigated theoretically and experimentally. The simplest experimental system consists of electronic…
Binary stochastic neurons (BSNs) are excellent hardware accelerators for machine learning. A popular platform for implementing them are low- or zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g. circular disks or…
Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through…
Synaptic plasticity is the capacity of a preexisting connection between two neurons to change in strength as a function of neural activity. Because synaptic plasticity is the major candidate mechanism for learning and memory, the…