Analog VLSI and neural systems
Analog VLSI and neural systems
The computational brain
The effect of synchronized inputs at the single neuron level
Neural Computation
Circuits of the mind
Fast sigmoidal networks via spiking neurons
Neural Computation
On the complexity of learning for a spiking neuron (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Information and Computation
Pulsed neural networks
Emulation of Hopfield networks with spiking neurons in temporal coding
CNS '97 Proceedings of the sixth annual conference on Computational neuroscience : trends in research, 1998: trends in research, 1998
Self-organizing maps of spiking neurons using temporal coding
CNS '97 Proceedings of the sixth annual conference on Computational neuroscience : trends in research, 1998: trends in research, 1998
Dynamic stochastic synapses as computational units
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On the complexity of learning for spiking neurons with temporal coding
Information and Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Analogue Neural VLSI: A Pulse Stream Approach
Analogue Neural VLSI: A Pulse Stream Approach
Silicon Implementation of Pulse Coded Neural Networks
Silicon Implementation of Pulse Coded Neural Networks
Computing Functions with Spiking Neurons in Temporal Coding
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
VC Dimension in Circuit Complexity
CCC '96 Proceedings of the 11th Annual IEEE Conference on Computational Complexity
A Model for Fast Analog Computation Based on Unreliable Synapses
Neural Computation
Vc dimension of an integrate-and-fire neuron model
Neural Computation
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Computing Information in Neuronal Spikes
Neural Processing Letters
Delay learning and polychronization for reservoir computing
Neurocomputing
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We discuss models for computation in biological neural systems that are based on the current state of knowledge in neurophysiology. Differences and similarities to traditional neural network models are highlighted. It turns out that many important questions regarding computation and learning in biological neural systems cannot be adequately addressed in traditional neural network models. In particular, the role of time is quite different in biologically more realistic models, and many fundamental questions regarding computation and learning have to be rethought for this context. Simultaneously, a somewhat related new generation of VLSI-chips is emerging ("pulsedVLSI") where new ideas about computing and learning with temporal coding can be tested in an engineering context. Articles with details to models and results that are sketched in this article can be found at http://www.tu-graz.ac.at/igi/maass/. We refer to Maass and Bishop(Eds.,Pulsed Neural Network, MIT Press, Cambridge, MA, 1999) for a collection of survey articles that contain further details and references.