Alexander Mikhailov

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks.

The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons.

Coherent chaos-based communication is a developing technique for secure data transmission based on synchronization of chaotic oscillators at the transmitter and receiver sides, which is treated as a more secure method than non-coherent communication, chaotic symbolic dynamics, and other approaches. Nowadays, digital implementation of such systems allows high precision in parameter matching and sophisticated message recovery algorithms, though challenges remain: first, in adapting chaotic signals for non-ideal physical media, e.g., acoustic channels with frequency-dependent attenuation and noise, while, second, still providing the high level of security. The current study provides the implementation of a coherent chaotic communication system based on the Sprott Case S chaotic oscillator that meets these challenges. We utilize the modulation technique, minimizing changes in chaotic dynamics that may be captured by an intruder, propose an optimization of chaotic oscillator parameters to match channel characteristics and establish a signal normalization procedure to neutralize attenuation at the receiver side. Applying spectral and return map attacks, we show that the measures taken to counteract distortion in the path do not reduce the security of the transmission. In an experiment with a physical acoustic path, we demonstrate the practical operability of our approach.

In this article, we introduce a novel approach to numerical integration based on a modified composite diagonal (CD) method, which is a variation of the semi-implicit Euler–Cromer method. This approach enables the finite-difference scheme to maintain the dynamic regime of the solution while adjusting the integration time step. This makes it possible to implement variable-step integration. We present a variable-step MCD (VS-MCD) version with a simple and stable Hairer step size controller. We show that the VS-MCD method is capable of changing the dynamics of the solution by changing the symmetry coefficient (reflecting the ratio between two internal steps within the composition step), which is useful for tuning the dynamics of the obtained discrete model, with no influence of the appropriate step size. We illustrate the practical application of the developed method by constructing a direct chaotic communication system based on the Sprott Case S chaotic oscillator, demonstrating high values in the largest Lyapunov exponent (LLE). The tolerance parameter of the step size controller is used as the modulation parameter to insert a message into the chaotic time series. Through numerical experiments, we show that the proposed modulation scheme has competitive robustness to noise and return map attacks in comparison with those of modulation methods based on fixed-step solvers. It can also be combined with them to achieve an extended key space.