4 citations · 5 across the 8 of their papers we have counts for
8 papers
An Asynchronous Mixed-Signal Resonate-and-Fire Neuron
Giuseppe Leo, Paolo Gibertini, Irem Ilter +3
Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its…
A Neuromodulable Current-Mode Silicon Neuron for Robust and Adaptive Neuromorphic Systems
Loris Mendolia, Chenxi Wen, Elisabetta Chicca +4
Neuromorphic engineering makes use of mixed-signal analog and digital circuits to directly emulate the computational principles of biological brains. Such electronic systems offer…
High-resolution spatial memory requires grid-cell-like neural codes
Madison Cotteret, Christopher J. Kymn, Hugh Greatorex +3
Continuous attractor networks (CANs) are widely used to model how the brain temporarily retains continuous behavioural variables via persistent recurrent activity, such as an anima…
A scalable event-driven spatiotemporal feature extraction circuit
Hugh Greatorex, Michele Mastella, Ole Richter +4
Event-driven sensors, which produce data only when there is a change in the input signal, are increasingly used in applications that require low-latency and low-power real-time sen…
Event-based vision for egomotion estimation using precise event timing
Hugh Greatorex, Michele Mastella, Madison Cotteret +2
Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods…
Fused-MemBrain: a spiking processor combining CMOS and self-assembled memristive networks
Davide Cipollini, Hugh Greatorex, Michele Mastella +2
In an era characterized by the rapid growth of data processing, developing new and efficient data processing technologies has become a priority. We address this by proposing a nove…
TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
Hugh Greatorex, Ole Richter, Michele Mastella +10
Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains be…
Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware
Madison Cotteret, Hugh Greatorex, Alpha Renner +6
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weig…