Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Hi-index | 0.00 |
Multi-stage decision tasks require the determination of intermediate results in order to perform consecutive decision steps. Electrophysiological recordings in sensory, parietal, and pre-frontal cortical areas have demonstrated that different response characteristics and timings at the neuron level provide key mechanisms to implement characteristic functionalities. We propose a hybrid neural model architecture that accounts for such findings and quantitatively reproduces the timing of such responses. We demonstrate by numerical simulations how the model accounts for feature-dependent decisions and how these are sequentialized during mutual interactions of pools of neurons in different cortical areas. Feedback from higher-level areas to early sensory stages of processing establishes a link between mechanisms involved in response integration and target selection to representations of sensory input.