11 citations · 16 across the 9 of their papers we have counts for
9 papers
Inertial Asynchronous Computation
Doruk Efe Gökmen, Michel Fruchart, Dmitrii Zendrikov +3
Computation is the controlled evolution of a state. Asynchronous evolutions, where all parts of the state change in their own time without stopping each other, put this control in…
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning
Irene Ambrosini, Ingo Blakowski, Dmitrii Zendrikov +5
Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromo…
Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate
Dmitrii Zendrikov, Alessio Franci, Giacomo Indiveri
Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital c…
Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi +1
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device misma…
Neuromorphic dreaming: A pathway to efficient learning in artificial agents
Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone +1
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn comple…
Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm
A. Linares-Barranco, E. Pinero-Fuentes, S. Canas-Moreno +6
Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in…
Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
Julian Büchel, Dmitrii Zendrikov, Sergio Solinas +2
Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog ci…
A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves
A. V. Paraskevov, D. K. Zendrikov
We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exi…