A neural network for position invariant pattern recognition combining spiking neurons with the Fourier-transform.
We present an approach for position invariant recognition of individual objects in composite scenes, combining neural networks and algorithmic methods. A dynamic network of spiking neurons is used to generate object definition and figure/ground separation via temporal signal correlations. A shift invariant representation of the network spike activity distribution is subsequently realized via the amplitude spectrum of the Fourier-transform. Objects and their transformed representations are therefore linked in the time domain. The model segregates scenes and classifies individual patterns independent of their position in the input scene.
Stoecker
M
M
Applied Physics & Neurophysics Department, Philipps-University, Marburg, Germany.
Reitboeck
H J
HJ
D000465:Algorithms; D003198:Computer Simulation; D005583:Fourier Analysis; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated