Mathematical learning models and neuronal networks
โ Scribed by E. Pfaffelhuber
- Publisher
- Elsevier Science
- Year
- 1973
- Tongue
- English
- Weight
- 801 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0022-5193
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
This paper proposes a direct synaptic weight training technique for a class of additive dynamic auto-associative neural networks based on the Cohen}Grossberg neuronal activation model. The proposed technique is based on the Jurdjevic}Quinn stabilization method for control a$ne systems. Asymptotic st
Transfer of methods between physics and biophysics based on a formal similarity between nuclear data and random nerve spikes, is shown. The following examples are described: on-line measurement of biophysical signals using the methhods of nuclear pulse spectrometry; Monte Carlo model of nerve-muscle
A learning model for coupled oscillators is proposed. The proposed learning rule takes a simple form by which the intrinsic frequencies of the component oscillators and the coupling strength between them are changed according to the effects of the input signals on the dynamics of the oscillator. In