Biomedical subjects

R Hahnloser

Publications and source records attributed to R Hahnloser.

3 recordsLinked to original sources

PubMed1999

Feedback interactions between neuronal pointers and maps for attentional processing.

Neural networks combining local excitatory feedback with recurrent inhibition are valuable models of neocortical processing. However, incorporating the attentional modulation observed in cortical neurons is problematic. We propose a simple architecture for attentional processing. Our network consists of two reciprocally connected populations of excitatory neurons; a large population (the map) processes a feedforward sensory input, and a small population (the pointer) modulates location and intensity of this processing in an attentional manner dependent on a control input to the pointer. This pointer-map network has rich dynamics despite its simple architecture and explains general computational features related to attention/intention observed in neocortex, making it interesting both theoretically and experimentally.

Attention↗

PubMed1999

Integrating neuromorphic action-oriented perceptual inputs to generate a navigation behaviour for a robot.

We use neural networks with pointer map architectures to provide simple attentional processing in a robotic task. A pointer map comprises a map of neurons that encode a stimulus. Besides global feedback inhibition, the map receives feedback excitation via a small group of pointer neurons that encode the location of a salient stimulus on the map as a vectorial representation. The pointer neurons are able to apply selective processing to a particular region of the network. The robot uses these properties to manoeuver in relation to an attended object. We implemented a controller composed of two pointer maps, and a motor map. The first pointer map reports the direction of a salient obstacle in a one-dimensional map of distance derived from infrared sensors. The second pointer map reports the direction to potential obstacles in a two-dimensional edge-enhanced image derived from a forward looking CCD-camera. These outputs are applied to a motor map, where they bias the motor control signals issued to the robots wheels, according to navigational intentions.

Avoidance Learning↗

PubMed1998

Learning algorithms based on linearization.

The aim of this article is to investigate a mechanical description of learning. A framework for local and simple learning algorithms based on interpreting a neural network as a set of configuration constraints is proposed. For any architectural design and learning task, unsupervised and supervised algorithms can be derived, optionally using unconstrained and hidden neurons. Unlike algorithms based on the gradient in weight space, the proposed tangential correlation (TC) algorithms move along the gradient in state space. This results in optimal scaling properties and simple expressions for the weight updates. The number of synapses is much larger than the number of neurons. A constraint for neural states does not impose a unique constraint for synaptic weights. Which weights to assign credit to can be selected from a parametrization of all weight changes equivalently satisfying the state constraints. At the heart of the parametrization are minimal weight changes. Two supervised algorithms (differing by their parametrizations) operating on a three-layer perceptron are compared with standard backpropagation. The successful training of fixed points of recurrent networks is demonstrated. The unsupervised learning of oscillations with variable frequencies is performed on standard and more sophisticated recurrent networks. The results presented here can be useful both for the analysis and for the synthesis of learning algorithms.

Algorithms↗