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The main problem about replacing LTP as a memory mechanism has been to find other highly abstract, easily understandable principles for induced plasticity. In this paper we attempt to lay out such a basic mechanism, namely intrinsic…
This paper introduces methods and a novel toolbox that efficiently integrates any high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations of the…
Photoacoustic (PA) imaging of deep tissue tends to employ Q-switched lasers with high pulse energy to generate high optical fluence and therefore high PA signal. Compared to Q-switched lasers, pulsed laser diodes (PLDs) typically generate…
Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these…
Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling. We introduce Neuronal Attention Circuit (NAC), a novel, biologically inspired CT-Attention mechanism that reformulates…
Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tuneable conductivity properties enable the imitation of synaptic behaviour. Multipore nanofluidic memristors have shown their…
Dedicated systems are fundamental for neuroscience experimental protocols that require timing determinism and synchronous stimuli generation. We developed a data acquisition and stimuli generator system for neuroscience research, optimized…
Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of…
We propose and demonstrate a nanoscale vacuum-tube diode triggered by few-cycle near-infrared laser pulses. It represents an ultrafast electronic device based on light fields, exploiting near-field optical enhancement at surfaces of two…
In this work we present numerical results concerning a time-delayed reservoir computing scheme, where its single nonlinear node, is a Quantum-Dot spin polarized Vertical Cavity Surface-Emitting Laser (QD s-VCSEL). The proposed photonic…
In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for brain stimulation. However, there are limitations and challenges with the current technologies. Firstly, the stimulation of…
The exponential time differencing (ETD) method allows using a large time step to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural networks. For pulse-coupled HH networks, the synaptic spike times cannot be…
Time non-locality, or memory, is a non-equilibrium property shared by all physical systems. Here, we show that memory is sufficient to induce a phase of spatial long-range order (LRO) even if the system's primary dynamical variables are…
This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional…
We investigate the dynamics of chiral edge states in topological polariton systems under laser driving. Using a model system comprised of topolgically trivial excitons and photons with a chiral coupling proposed by Karzig et al. [Phys. Rev.…
This study explores the design and control of the behaviour of agents and robots using simple circuits of spiking neurons and Spike Timing Dependent Plasticity (STDP) as a mechanism of associative and unsupervised learning. Based on a…
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth…
Dynamic Pushdown Networks (DPNs) are a model for multithreaded programs with recursion and dynamic creation of threads. In this paper, we propose a temporal logic called NTL for reasoning about the call- and return- as well as thread…
We investigate a possibility of realization of structurally stable chaotic dynamics in neural systems. The considered model of interacting neurons consists of a pair of coupled FitzHugh-Nagumo systems, with the parameters being periodically…
We analyze the dynamics of the FitzHugh-Nagumo (FHN) model in the presence of colored noise and a periodic signal. Two cases are considered: (i) the dynamics of the membrane potential is affected by the noise, (ii) the slow dynamics of the…