• Publications
  • Influence
A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields
  • Martin Rehn, F. Sommer
  • Computer Science, Medicine
  • Journal of Computational Neuroscience
  • 15 February 2007
Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptiveExpand
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Memory Capacities for Synaptic and Structural Plasticity
TLDR
We introduce fair measures for information-theoretic capacity in associative memory that also provide a theoretical benchmark. Expand
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Feedforward Excitation and Inhibition Evoke Dual Modes of Firing in the Cat's Visual Thalamus during Naturalistic Viewing
Thalamic relay cells transmit information from retina to cortex by firing either rapid bursts or tonic trains of spikes. Bursts occur when the membrane voltage is low, as during sleep, because theyExpand
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Information capacity in recurrent McCulloch-Pitts networks with sparsely coded memory states
TLDR
A new access to the asymptotic analysis of autoassociation properties in recurrent McCulloch-Pitts networks in the range of low activity is proposed. Expand
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Data Sharing for Computational Neuroscience
TLDR
A workshop on data sharing in neuroscience was held in 2007, bringing together experimental and theoretical neuroscientists, computer scientists, legal experts and governmental observers. Expand
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Associative memory in networks of spiking neurons
TLDR
We develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel for associative memory . Expand
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Iterative retrieval of sparsely coded associative memory patterns
TLDR
We investigate the pattern completion performance of neural auto-associative memories composed of binary threshold neurons for sparsely coded binary memory patterns. Expand
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Associative Data Storage and Retrieval in Neural Networks
TLDR
Associative storage and retrieval of binary random patterns in various neural net models with one-step threshold-detection retrieval and local learning rules are the subject of this chapter. Expand
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Spatially Distributed Local Fields in the Hippocampus Encode Rat Position
Extracting Spatial Information The location of a rat can be deciphered from hippocampal activity by detecting the firing of individual place-selective neurons. In contrast, the local field potentialExpand
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When Can Dictionary Learning Uniquely Recover Sparse Data From Subsamples?
TLDR
Sparse coding or sparse dictionary learning has been widely used to recover underlying structure in many kinds of natural data. Expand
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