Throughout the last decades, neuromorphic circuits have incited the interest of scientists, as they are potentially a powerful tool for the treatment of neurological diseases. To this end, it is essential to consider the biological principles of the CNS and develop the appropriate area- and power-efficient circuits. Motivated by studies that outline the indispensable role of astrocytes in the dynamic regulation of synaptic transmission and their active contribution to neural information processing in the CNS, in this work we propose a digital implementation of neuron–astrocyte bidirectional interactions. In order to describe the neuronal dynamics and the astrocytes’ calcium dynamics, a modified version of the original Izhikevich neuron model was combined with a linear approximation of the Postnov functional neural–glial interaction model. For the implementation of the neural–glial computation core, only three pipeline stages and a 10.10 fixed point representation were utilized. Regarding the results obtained from the FPGA implementation and the comparisons to other works, the proposed neural–glial circuit reported significant savings in area requirements (from 22.53% up to 164.20%) along with considerable savings in total power consumption of 28.07% without sacrificing output computation accuracy. Finally, an RMSE analysis was conducted, confirming that this particular implementation produces more accurate results compared to previous studies.

This paper proposes a novel image encryption and decryption scheme, called Square Block Division-Fibonacci (SBD-Fibonacci), which dynamically partitions any input image into optimally sized square blocks to enable efficient encryption without resizing or distortion. The proposed encryption scheme can dynamically adapt to the image dimensions and ensure compatibility with images of varying and high resolutions, while it serves as a yardstick for any symmetric-key image encryption algorithm. An optimization model, combined with the Lagrange Four-Square theorem, minimizes trivial block sizes, strengthening the encryption structure. Encryption keys are generated using the direct sum of generalized Fibonacci matrices, ensuring key matrix invertibility and strong diffusion properties and security levels. Experimental results on widely-used benchmark images and a comparative analysis against State-of-the-Art encryption algorithms demonstrate that SBD-Fibonacci achieves high entropy, strong resistance to differential and statistical attacks, and efficient runtime performance—even for large images.

The creation of a framework in which traditional Machine Learning and neuromorphic algorithms compete to solve a shared Reinforcement Learning environment is presented in this work. In addition, this configuration allows the exploitation of modern and widely-used Machine Learning libraries. The PyTorch framework is used to investigate the expanded capabilities and potential of training an action-critic network pair comprised of specialised units using a custom learning algorithm. The policy and value networks utilised in this context are fully interconnected MultiLayer Perceptrons. The training procedure employs two distinct algorithms: an algorithm inspired by Reward Modulated Spiked Timing Dependent Plasticity and the conventional Back Propagation technique. A comparative evaluation and analysis of the findings is performed.