Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale
computation remains a difficult challenge. To address this, we show how the distributed …

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal
dynamics are getting wide attention and are being applied to many relevant problems using …

Traditional neuromorphic hardware architectures rely on event-driven computation, where
the asynchronous transmission of events, such as spikes, triggers local computations within …

Event-based neuromorphic systems promise to reduce the energy consumption of deep
learning tasks by replacing expensive floating point operations on dense matrices by low …

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is
motivating the search for high-performance and efficient spiking neural networks to run on …

Neuromorphic computer architectures utilize an event-based paradigm to operate in low-
power environments and achieve high-throughput with massive parallelism. The integrate …

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of
complex cognitive and motor tasks, due to their rich temporal dynamics and sparse …

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 …

Dynamical systems are universal computers. They can perceive stimuli, remember, learn
from feedback, plan sequences of actions, and coordinate complex behavioural responses …

Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are
well-suited for hardware implementation in low-power neuromorphic hardware. However …