Inferring Spike Trains From Local Field Potentials (2008)
Rasch, M.J., Gretton, A., Murayama, Y., Maass, W., Logothetis, N.K.
We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found...
On learnability and predicate logic (Extended Abstract) (2007)
) W. Maass Gy. Tur'an y 1 Introduction Several applications of learning in artificial intelligence use a predicate logic formalism. The theoretical study of efficient learnability in this area,...
Invited Review Correcting for the Sampling Bias Problem in Spike Train Information Measures (2007)
Stefano Panzeri, Riccardo Senatore, Marcelo A. Montemurro, Rasmus S, M. J. Rasch, A. Gretton, ...
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Coding and Learning of behavioral sequences (2004)
Melamed, O., Gerstner, W., Maass, W., Tsodyks, M., Markram, H.
Computational Models for Generic Cortical Microcircuits (2004)
Maass, W., Natschläger, T., Markram, H.
The human nervous system processes a continuous stream of multi-modal input from a rapidly changing environment. A key challenge for neural modeling is to explain how the neural microcircuits...
Fading memory and kernel properties of generic cortical microcircuit models (2004)
Maass, W., Natschlager, T., Markram, H.
It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle...
Effect of seed and interface layers on the magnetic properties of laminated Fe-Al-N films (2003)
Maass, W., Rohrmann, H., Langer, J., Mattheis, R., Senz, S., ...
A Model for Real-Time Computation in Generic Neural Microcircuits (2003)
Maass, W., Natschläger, T., Markram, H.
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of...
Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons (2003)
Legenstein, R., Markram, H., Maass, W.
Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical...
Computer models and analysis tools for neural microcircuits (2003)
Natschläger, T., Markram, H., Maass, W.
This chapter surveys web resources regarding computer models and analysis tools for neural microcircuits. In particular it describes the features of a new website (www.lsm.tugraz.at) that facilitates...
Häusler, S., Markram, H., Maass, W.
We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information...
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models (2002)
Maass, W., Legenstein, R. A., Markram , H.
We propose an alternative paradigm for processing time-varying visual inputs, in particular for tasks involving temporal and spatial integration, which is inspired by hypotheses about the...
Synapses as dynamic memory buffers (2002)
This article throws new light on the possible role of synapses in information transmission through theoretical analysis and computer simulations. We show that the internal dynamic state of a synapse...
Maass, W., Natschlager, T., Markram, H.
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of...
The "Liquid Computer": A Novel Strategy for Real-Time Computing on Time Series (2002)
Natschläger, T., Maass, W., Markram, H.
We will discuss in this survey article a new framework for analysing computations on time series and in particular on spike trains, introduced in (Maass et. al. 2002). In contrast to common...
Perspectives of Current Research about the Complexity of Learning on Neural Nets (1994)
This paper discusses within the framework of computational learning theory the current state of knowledge and some open problems in three areas of research about learning on feedforward neural nets:...