A zinc oxide (ZnO)–reduced graphene oxide (rGO) composite thin film memristive device is reported. Further, it has been shown that it is possible to implement Hebbian learning rules like, the spike‐timing‐dependent plasticity, using this device. Furthermore, a circuit on PCB is developed; this circuit can imitate the biological spike firing scheme and activate the memristor synapse. The fabricated device along with the custom made circuit can be extended for developing future neuromorphic circuit applications.

In this paper, a work on spiking neural networks based on a model of a kind of Leaky Integrate-and-Fire (LIF) neuron with latency is presented. Efficient simulations are carried out through an ad hoc event-driven approach, highlighting some particular effects of synchrony in a simple feedforward network topology. These results are consistent with literature results and, thanks to the implementation of the biologically plausible latency effect in the model, new results have emerged from the simulations. The authors plan to apply these results in the near future to applications in which this kind of neural networks and Digital Signal Processing (DSP) applications can be merged to obtain powerful nonlinear DSP techniques. In the plan of the authors is also the definition of a hardware prototype of the network based on analog/digital techniques.

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus, and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate model’s performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level.