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- Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass
- PLoS Computational Biology
- 2013

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical… (More)

- Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass
- PLoS Computational Biology
- 2011

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic… (More)

- Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
- NIPS
- 2009

The principles by which spiking neurons contribute to the astounding computational power of generic cortical microcircuits, and how spike-timing-dependent plasticity (STDP) of synaptic weights could generate and maintain this computational function, are unknown. We show here that STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit,… (More)

- David Kappel, Bernhard Nessler, Wolfgang Maass
- PLoS Computational Biology
- 2014

In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but… (More)

- Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
- NIPS
- 2008

Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models for Bayesian decision making typically require data-structures that are hard to implement in neural networks. This article shows that even the simplest and experimentally best… (More)

- Michael Pfeiffer, Bernhard Nessler, Rodney J. Douglas, Wolfgang Maass
- Neural Computation
- 2010

We introduce a framework for decision making in which the learning of decision making is reduced to its simplest and biologically most plausible form: Hebbian learning on a linear neuron. We cast our Bayesian-Hebb learning rule as reinforcement learning in which certain decisions are rewarded and prove that each synaptic weight will on average converge… (More)

- Stefan Habenschuss, Johannes Bill, Bernhard Nessler
- NIPS
- 2012

Recent spiking network models of Bayesian inference and unsupervised learning frequently assume either inputs to arrive in a special format or employ complex computations in neuronal activation functions and synaptic plasticity rules. Here we show in a rigorous mathematical treatment how homeostatic processes, which have previously received little attention… (More)

- Johannes Bill, Lars Buesing, Stefan Habenschuss, Bernhard Nessler, Wolfgang Maass, Robert Legenstein +1 other
- PloS one
- 2015

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these… (More)

- Christoph Hartmann, Andreea Lazar, Bernhard Nessler, Jochen Triesch
- PLoS Computational Biology
- 2015

Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1.… (More)

0 0 t post − t pre ∆ w ki c · e −wki-1 σ-1 Simple STDP curve Complex STDP curve A B Figure 1: A) Architecture for learning with STDP in a WTA-network of spiking neurons. B) Learning curve for the two STDP rules that were used (with σ = 10ms, c = e 5). The red curve results from a theoretically optimal approximation of EM within this architecture, whereas… (More)

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