Biomedical subjects

Klaus Obermayer

Publications and source records attributed to Klaus Obermayer.

3 recordsLinked to original sources

PubMed2003

Soft learning vector quantization.

Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of "soft" LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.

Costs and Cost Analysis↗

PubMed2002

Rapid adaptation to internal states as a coding strategy in visual cortex?

Adaptation is a prominent feature of biological neuronal systems. A common interpretation of adaptation in terms of function is that it provides flexibility for a neuronal system to perform well under varying external conditions, for example by adjusting the input/output relation of a sensory system with reference to the ensemble of stimuli the organism currently perceives. This interpretation, however, only applies if the time-scale of adaptation is slower than the time-scale at which the environment changes. Experimentally it is observed, however, that adaptation can be very rapid. Spike-frequency adaptation of cortical neurons, for example, occurs on a time-scale of approximately 100 ms. Here we show that those rapid adaptation processes can also be understood within the framework of information theory. We start with the hypothesis that neuronal codes are designed to optimize the information a neuronal representation conveys about an input stimulus for any increasing time window beginning with stimulus onset, and we show that this implies a rapid adaptation of the neuronal code on the time-scale of stimulus presentation. Adaptation, however, does not occur because the state of the environment changes. Rather it is a reaction to changes of the organisms own internal state, e.g. the level of noise in the neuronal representation. We apply this approach to a model of an orientation hypercolumn in the primary visual cortex, and predict that inter-columnar interactions should adapt on the time-scale of a typical fixation period ( approximately 300 ms).

Adaptation, Psychological↗

PubMedDate not supplied

Rapid adaptation and efficient coding.

Rapid adaptation is a prominent feature of biological neuronal systems. From a functional perspective the adaptation of neuronal properties, namely the input-output relation of sensory neurons, is usually interpreted as an adaptation of the sensory system to changing environments as characterized by their stimulus statistics. Here we argue that this interpretation is only applicable as long as the adaptation processes are slower than the time-scale at which the stimulus statistics change. We present a definition of optimality of a neuronal code which still captures the idea of efficient coding, but which can also explain rapid adaptation without referring to an adaptation to different sensory environments. Finally, we apply our new idea to a simple model of an orientation hypercolumn in the primary visual cortex and predict that the interactions between orientation columns should adapt at the time-scale of a single stimulus presentation.

Adaptation, Biological↗