2013 - Member of Academia Europaea
His main research concerns Artificial neural network, Artificial intelligence, Algorithm, Computation and Neuroscience. His Artificial neural network research is multidisciplinary, relying on both Spike, Markov chain and Information processing. His Artificial intelligence study incorporates themes from Machine learning, Biological neural network, Synapse and Stimulus.
His studies in Algorithm integrate themes in fields like Function, Perceptron, Learning rule and Feed forward. His Computation study which covers Theoretical computer science that intersects with Computer engineering and Computational neuroscience. His work carried out in the field of Neuroscience brings together such families of science as Hebbian theory and Communication.
His primary areas of study are Artificial intelligence, Artificial neural network, Algorithm, Computation and Discrete mathematics. His work in Artificial intelligence addresses issues such as Biological neural network, which are connected to fields such as Synapse. His Artificial neural network study combines topics in areas such as Theoretical computer science, Spike and Feed forward.
His work in the fields of Algorithm, such as Boolean function, overlaps with other areas such as Coding. He regularly ties together related areas like Neuroscience in his Computation studies. The study incorporates disciplines such as Combinatorics, Turing machine, Bounded function, Upper and lower bounds and NSPACE in addition to Discrete mathematics.
His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Spiking neural network, Neuroscience and Computation. As a member of one scientific family, Wolfgang Maass mostly works in the field of Artificial intelligence, focusing on Spike and, on occasion, Gradient descent. His Artificial neural network research includes themes of Energy consumption, Motor control, Structure, Probabilistic logic and Adaptation.
His work deals with themes such as Neuromorphic engineering, Learning rule and Pattern recognition, which intersect with Spiking neural network. His studies deal with areas such as Long short term memory and Component as well as Neuroscience. His Computation study integrates concerns from other disciplines, such as Calculus, Calculus, Learning theory and Hebbian theory.
Artificial intelligence, Artificial neural network, Neuromorphic engineering, Recurrent neural network and Deep learning are his primary areas of study. His study in the field of Backpropagation through time and Task is also linked to topics like Function and Process. Wolfgang Maass performs multidisciplinary study on Artificial neural network and Key in his works.
His Neuromorphic engineering research incorporates themes from Distributed computing, Representation, Central nervous system, Robustness and Spiking neural network. His Spiking neural network study deals with Time domain intersecting with Computation. His work investigates the relationship between Deep learning and topics such as Pruning that intersect with problems in Benchmark.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Real-time computing without stable states: a new framework for neural computation based on perturbations
Wolfgang Maass;Thomas Natschläger;Henry Markram.
Neural Computation (2002)
Pulsed neural networks
Wolfgang Maass;Christopher M. Bishop.
Approximation schemes for covering and packing problems in image processing and VLSI
Dorit S. Hochbaum;Wolfgang Maass.
Journal of the ACM (1985)
State-dependent computations: spatiotemporal processing in cortical networks
Dean V. Buonomano;Wolfgang Maass.
Nature Reviews Neuroscience (2009)
Threshold circuits of bounded depth
András Hajnal;András Hajnal;Wolfgang Maass;Wolfgang Maass;Pavel Pudlák;Pavel Pudlák;György Turán;György Turán.
Journal of Computer and System Sciences (1993)
2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models
Robert Legenstein;Wolfgang Maass.
Neural Networks (2007)
Threshold circuits of bounded depth
Andras Hajnal;Wolfgang Maass;Pavel Pudlak;Mario Szegedy.
foundations of computer science (1987)
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
Lars Buesing;Johannes Bill;Bernhard Nessler;Wolfgang Maass.
PLOS Computational Biology (2011)
On the Computational Power of Winner-Take-All
Neural Computation (2000)
Lower bounds for the computational power of networks of spiking neurons
Neural Computation (1996)
Profile was last updated on December 6th, 2021.
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