In this contribution we present a new biology-inspired neuron model and its real-time realization using a dedicated neural hardware emulator. The biological neuron model overcomes the limitations of classical neuron models by including dynamic features such as adaptive synaptic delays. The emulator used for its realization is based on a special communication processor optimized for the global exchange of pulse messages between neuron processors. The use of the model together with the emulator in real-time adaptive signal processing will be shown using an example in the field of fault-tolerant adaptive beamforming
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critic...
Many sounds of ecological importance, such as communication calls, are characterized by time-varying...
This work presents an emulator that has been developed for real-time algorithm and architecture expl...
This work describes a parallel neural network emulator which combines use of application-specific VL...
Future development of neural networks and their applications will be strongly affected by the availa...
Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for...
Real-time simulations of biological neural networks (BNNs) provide a natural platform for applicatio...
This work has been supported by the European FACETS project. Within this project, we contribute in d...
Algorithmically and energetically efficient computational architectures that operate in real time ar...
Closed-loop experiments involving biological and artificial neural networks would improve the unders...
The exploration of the dynamics of bioinspired neural networks has allowed neuroscientists to unders...
Kaulmann T, Ferber M, Witkowski U, Rückert U. Analog VLSI Implementation of Adaptive Synapses in Pul...
Neurological research has revealed that neurons encode information in the timing of spikes. Spiking ...
In this paper, we present two versions of a hardware processing architecture for modeling large netw...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critic...
Many sounds of ecological importance, such as communication calls, are characterized by time-varying...
This work presents an emulator that has been developed for real-time algorithm and architecture expl...
This work describes a parallel neural network emulator which combines use of application-specific VL...
Future development of neural networks and their applications will be strongly affected by the availa...
Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for...
Real-time simulations of biological neural networks (BNNs) provide a natural platform for applicatio...
This work has been supported by the European FACETS project. Within this project, we contribute in d...
Algorithmically and energetically efficient computational architectures that operate in real time ar...
Closed-loop experiments involving biological and artificial neural networks would improve the unders...
The exploration of the dynamics of bioinspired neural networks has allowed neuroscientists to unders...
Kaulmann T, Ferber M, Witkowski U, Rückert U. Analog VLSI Implementation of Adaptive Synapses in Pul...
Neurological research has revealed that neurons encode information in the timing of spikes. Spiking ...
In this paper, we present two versions of a hardware processing architecture for modeling large netw...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critic...
Many sounds of ecological importance, such as communication calls, are characterized by time-varying...