Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

Hyperparameter optimization of spiking neural networks (SNNs) is a difficult task which has
not yet been deeply investigated in the literature. In this work, we designed a scalable …

The ever-increasing demands of computationally expensive and high-dimensional problems
require novel optimization methods to find near-optimal solutions in a reasonable amount of …

Despite significant efforts to enhance the resilience of computer systems against malware
attacks, the abundance of exploitable vulnerabilities remains a significant challenge. While …

The IEEE CASS/EDS Chapter of the North Jersey Section has been awarded the IEEE
CASS 2024 Regions 1-7 Chapter of the Year Award for its outstanding contributions …

Spiking Neural Network (SNN) are peculiar networks based on the dynamics of timed spikes
between fully asynchronous neurons. Their design is complex and differs from usual artificial …

In parallel with the continuously increasing parameter space dimensionality, search and
optimization algorithms should support distributed parameter evaluations to reduce …

Spiking Neural Networks (SNNs) executed on neuro-morphic hardware (NmC) have shown
great potential to perform a class of learning and inference tasks with low latency and high …

Every neuron in traditional spiking neural networks (SNNs) only transmits temporal
information, namely spiking time, to its postsynaptic neurons while all other underlying …