A self-organizing neural network model for the development of complex cells
โ Scribed by Takashi Nagano; Koji Kurata
- Publisher
- Springer-Verlag
- Year
- 1981
- Tongue
- English
- Weight
- 444 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0340-1200
No coin nor oath required. For personal study only.
โฆ Synopsis
On the basis of recent neurophysiological findings on the mammalian visual cortex, a selforganizing neural network model is proposed for the understanding of the development of complex cells. The model is composed of two kinds of connections from LGN cells to a complex cell. One is direct excitatory connections and the other is indirect inhibitory connections via simple cells. Inhibitory synapses between simple cells and complex cells are assumed to be modifiable. The model was simulated on a computer to confirm its behavior.
๐ SIMILAR VOLUMES
Mutual inhibition between neurons combined with a learning principle similar to that proposed by Hebb is shown to secure a powerful self-organizing property for neural networks. Numerical analysis reveals that the system investigated always organizes itself into the same final state from any arbitra
A neural network model is proposed to explain the development of direction selectivity of cortical cells. The model is constructed under the following three hypotheses that are very plausible from recent neurophysiological findings. (1) Direction selectivity is developed by modifiable inhibitory syn
Figure 1 The highlighted arrow is the response of the tuned neuron to the image.