Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully
parallel neuromorphic hardware, but existing training methods that convert conventional …

One of the most important resources used in today's world is image. Medical images can
play an essential role in hel** diagnose diseases. Doctors and specialists use medical …

Event-based vision sensors encode local pixel-wise brightness changes in streams of
events rather than full image frames and yield sparse, energy-efficient encodings of scenes …

Many computer vision tasks address the problem of scene understanding and are naturally
interrelated eg object classification, detection, scene segmentation, depth estimation, etc …

Although deep feedforward neural networks share some characteristics with the primate
visual system, a key distinction is their dynamics. Deep nets typically operate in serial stages …

Now diagnostic methods with the help of machine learning have been able to help doctors
in this field. One of the most important of these methods is deep learning, which has gotten …

Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still
differ significantly from the mechanisms of real biological neural networks and face problems …

From image recognition to automated driving, machine learning nowadays is all around us
and impacts various aspects of our daily lives. This disruptive technology is rapidly evolving …