Near-Maximum Entropy Models for Binary Neural Representations of Natural Images (2008)
Bethge, M., Berens, P., Platt, J. C., Koller, D., Singer, Y., Roweis, S.
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor...
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior (2008)
Gerwinn, S., Macke, J., Seeger, M., Bethge, M., Platt, J. C., Koller, D., ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation...
Receptive Fields without Spike-Triggering (2008)
Macke, J.H., Zeck, G., Bethge, M., Platt, J. C., Koller, D., Singer, Y., ...
Stimulus selectivity of sensory neurons is often characterized by estimating their receptive field properties such as orientation selectivity. Receptive fields are usually derived from the mean (or...
J. Benda, M. Bethge, M. Hennig, K. Pawelzik
Spike-frequency adaptation is a common feature of neural dynamics. Here we present a low-dimensional phenomenological model whose parameters can be easily determined from experimental data. We test...
The Independent Components of Natural Images are Perceptually Dependent (2007)
Bethge, M., Wiecki, T.V., Wichmann, F.A.
The independent components of natural images are a set of linear filters which are optimized for statistical independence. With such a set of filters images can be represented without loss of...
Unsupervised learning of a steerable basis for invariant image representations (2007)
Bethge, M., Gerwinn, S., Macke, J.H.
There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal...
Bayesian Inference for Sparse Generalized Linear Models (2007)
Seeger, M., Gerwinn, S., Bethge, M.
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed...
The Independent Components of Natural Images are Perceptually Dependent (2007)
Bethge, M., Wiecki, T.V., Wichmann, F.A.
The independent components of natural images are a set of linear filters which are optimized for statistical independence. With such a set of filters images can be represented without loss of...
Unsupervised learning of a steerable basis for invariant image representations (2007)
Bethge, M., Gerwinn, S., Macke, J.H.
There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal...
The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information...
Dynamics of population rate codes in ensembles of neocortical neurons (2004)
Silberberg, G., Bethge, M., Markram, H., Pawelzik, K., Tsodyks, M.
Information processing in neocortex can be very fast, indicating that neuronal ensembles faithfully transmit rapidly changing signals to each other. Apart from signal-to-noise issues, population...
Dynamics of Population Rate Codes in Ensembles of Neocortical Neurons (2004)
Silberberg Bethge Markram, G. Silberberg, M. Tsodyks, M. Bethge, M. Bethge, H. Markram, ...
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LETTER Communicated by Kechen Zhang Optimal Short-Term Population Coding: When Fisher (2002)
Information Fails Bethge, M. Bethge, D. Rotermund, K. Pawelzik
this article, we seek to identify optimal encoding strategies (i.e., a set of tuning functions) under the assumption of a Poisson noise model, given a limited number of neurons N and a #nite decoding...
Author Supported By, J. Benda, M. Bethge, M. Hennig, K. Pawelzik, ...
Spike-frequency adaptation is a common feature of neural dynamics. Here we present a low-dimensional phenomenologlwx model whose parameters can be easily determined from experimental data. We test...
Spikefrequency adaptation: Phenomenological model and experimental tests (2001)
J. Benda, M. Bethge, M. Hennig, K. Pawelzik
Spike-frequency adaptation is a common feature of neural dynamics. Here we present a low-dimensional phenomenological model whose parameters can be easily determined from experimental data. We test...
Uebing, A, Fischer, G, Bethge, M, Scheewe, J, Schmiel, F, Stieh, J, ...
Objective: To assess the influence of the pulmonary annulus diameter after reconstruction of the right ventricular (RV) outflow tract at repair of tetralogy of Fallot on pulmonary regurgitation and...