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- Claudia Clopath, Lars Büsing, Eleni Vasilaki, Wulfram Gerstner
- Nature neuroscience
- 2010

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing-dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane… (More)

- 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)

- Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Buesing, Wulfram Gerstner
- PLoS Computational Biology
- 2008

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow… (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)

- Eilif Müller, Lars Buesing, Johannes Schemmel, Karlheinz Meier
- Neural Computation
- 2007

We propose a Markov process model for spike-frequency adapting neural ensembles that synthesizes existing mean-adaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unified and tractable framework that goes beyond renewal and mean-adaptation theories by accounting for correlations between subsequent interspike… (More)

- Lars Buesing, Benjamin Schrauwen, Robert A. Legenstein
- Neural Computation
- 2010

Reservoir computing (RC) systems are powerful models for online computations on input sequences. They consist of a memoryless readout neuron that is trained on top of a randomly connected recurrent neural network. RC systems are commonly used in two flavors: with analog or binary (spiking) neurons in the recurrent circuits. Previous work indicated a… (More)

Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time. A few highly-structured… (More)

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations , their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variational op-timisation of the posterior… (More)

Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in… (More)

- Dejan Pecevski, Lars Buesing, Wolfgang Maass
- PLoS Computational Biology
- 2011

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable… (More)