Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing and actuating. The unique properties of such systems call for new signal processing and control paradigms. The article introduces the mixed feedback organization of excitable neuronal systems, consisting of interlocked positive and negative feedback loops acting in distinct timescales. The principles of biological neuromodulation suggest a methodology for designing and controlling mixed-feedback systems neuromorphically. The proposed design consists of a parallel interconnection of elementary circuit elements that mirrors the organization of biological neur...
Morabito FC, Andreou AG, Chicca E. Neuromorphic Engineering: From Neural Systems to Brain-Like Engin...
Recent work in neuroscience is revealing how the blowfly rapidly detects orientation using neural cir...
Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neuron...
Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biolog...
Neuromorphic engineering attempts to understand the computational properties of neural processing sy...
Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environme...
The 14 papers in this research topic were solicited primarily from attendees to the two most importa...
We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the...
Neuromorphic systems are implementations in silicon of elements of neural systems. The idea of elect...
Continuous improvements in the VLSI domain have enabled the technology to mimic the neuro biological...
Abstract. Neuromorphic systems take inspiration from the principles of biological information proc...
This article reviews a wide spectrum of state‐of‐the‐art neuromorphic systems, ranging from its prin...
Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades ne...
Recent developments in neuromorphic hardware engineering make mixed-signal VLSI neural network model...
Sheik S, Stefanini F, Neftci E, Chicca E, Indiveri G. Systematic configuration and automatic tuning ...
Morabito FC, Andreou AG, Chicca E. Neuromorphic Engineering: From Neural Systems to Brain-Like Engin...
Recent work in neuroscience is revealing how the blowfly rapidly detects orientation using neural cir...
Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neuron...
Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biolog...
Neuromorphic engineering attempts to understand the computational properties of neural processing sy...
Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environme...
The 14 papers in this research topic were solicited primarily from attendees to the two most importa...
We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the...
Neuromorphic systems are implementations in silicon of elements of neural systems. The idea of elect...
Continuous improvements in the VLSI domain have enabled the technology to mimic the neuro biological...
Abstract. Neuromorphic systems take inspiration from the principles of biological information proc...
This article reviews a wide spectrum of state‐of‐the‐art neuromorphic systems, ranging from its prin...
Neuromorphic engineering has just reached its 25th year as a discipline. In the first two decades ne...
Recent developments in neuromorphic hardware engineering make mixed-signal VLSI neural network model...
Sheik S, Stefanini F, Neftci E, Chicca E, Indiveri G. Systematic configuration and automatic tuning ...
Morabito FC, Andreou AG, Chicca E. Neuromorphic Engineering: From Neural Systems to Brain-Like Engin...
Recent work in neuroscience is revealing how the blowfly rapidly detects orientation using neural cir...
Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neuron...