On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing (2009)
Benjamin Schrauwen, Lars Büsing, Robert Legenstein
Randomly connected recurrent neural circuits have proven to be very powerful models for online computations when a trained memoryless readout function is appended. Such Reservoir Computing (RC)...
Reinforcement learning on complex visual stimuli (2009)
Wilbert, Niko, Legenstein, Robert, Franzius, Mathias, Wiskott, Laurenz
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing (2009)
Schrauwen, Benjamin, Büsing, Lars, Legenstein, Robert
Randomly connected recurrent neural circuits have proven to be very powerful models for online computations when a trained memoryless readout function is appended. Such Reservoir Computing (RC)...
Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of...
Improved neighborhood-based algorithms for large-scale recommender systems. (2008)
Töscher, Andreas, Jahrer, Michael, Legenstein, Robert
Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the...
Thomas Natschläger, Nils Bertschinger, Robert Legenstein
In this paper we analyze the relationship between the computational capabilities of randomly connected networks of threshold gates in the timeseries domain and their dynamical properties. In...
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity (2008)
Robert Legenstein, Wolfgang Maass
We investigate under what conditions a neuron can learn by experimentally supported rules for spike timing dependent plasticity (STDP) to predict the arrival times of strong “teacher inputs ” to...
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity (2008)
Robert Legenstein, Dejan Pecevski, Wolfgang Maass
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally...
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity (2007)
Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally...
Klampfl, Stefan, Legenstein, Robert, Maass, Wolfgang
Independent Component Analysis (or blind source separation) is assumed to be an essential component of sensory processing in the brain and could provide a less redundant representation about the...
Edge of Chaos and Prediction of Computational Performance for Neural Circuit Models (2007)
Legenstein, Robert, Maass, Wolfgang
We analyze in this article the significance of the edge of chaos for real time computations in neural microcircuit models consisting of spiking neurons and dynamic synapses. We find that the edge of...
On the classification capability of sign-constrained perceptrons. (2007)
Legenstein, Robert, Maass, Wolfgang
The perceptron (also referred to as Mc Culloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a biological neuron....
Information bottleneck optimization and independent component extraction with spiking neurons (2007)
Stefan Klampfl, Robert Legenstein, Wolfgang Maass
The extraction of statistically independent components from high-dimensional multi-sensory input streams is assumed to be an essential component of sensory processing in the brain. Such independent...
Information bottleneck optimization and independent component extraction with spiking neurons (2006)
Klampfl, Stefan, Legenstein, Robert, Maass, Wolfgang
We show that information bottleneck optimization and extraction of independent components can be implemented with stochastic spiking neurons with refractoriness. The new learning rule that achieves...
Maass, Wolfgang, Legenstein, Robert, Bertschinger, Nils
What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of...
Natschlaeger, Thomas, Bertschinger, Nils, Legenstein, Robert
In this paper we analyze the relationship between the computational capabilities of randomly connected networks of threshold gates in the timeseries domain and their dynamical properties. In...
What makes a dynamical system computationally powerful? (2005)
Legenstein, Robert, Maass, Wolfgang
We review methods for estimating the computational capability of a complex dynamical system. The main examples that we discuss are models for cortical neural microcircuits with varying degrees of...
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity (2005)
Legenstein, Robert, Maass, Wolfgang
We investigate under what conditions a neuron can learn by experimentally supported rules for spike timing dependent plasticity (STDP) to predict the arrival times of strong “teacher inputs” to...
What can a Neuron Learn with Spike-Timing-Dependent Plasticity? (2005)
Legenstein, Robert, Näger, Christian, Maass, Wolfgang
Spiking neurons are very flexible computational modules, which can implement with different values of their adjustable synaptic parameters an enormous variety of different transformations F from...
Edge of chaos and prediction of computational power for neural microcircuit models (2005)
Legenstein, Robert, Maass, Wolfgang
What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of...
Wolfgang Maass, Robert Legenstein, Nils Bertschinger
What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of...
Wolfgang Maass, Robert Legenstein, Nils Bertschinger
What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of...
A new approach towards vision suggested by biologically realistic neural microcircuit models (2002)
Wolfgang Maass, Wolfgang Maass, Robert Legenstein, Robert Legenstein, Henry Markram, ...
markram.html 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...
Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of...