By Topic

How delays affect neural dynamics and learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. Baldi ; Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA ; A. F. Atiya

We investigate the effects of delays on the dynamics and, in particular, on the oscillatory properties of simple neural network models. We extend previously known results regarding the effects of delays on stability and convergence properties. We treat in detail the case of ring networks for which we derive simple conditions for oscillating behavior and several formulas to predict the regions of bifurcation, the periods of the limit cycles and the phases of the different neurons. These results in turn can readily be applied to more complex and more biologically motivated architectures, such as layered networks. In general, the main result is that delays tend to increase the period of oscillations and broaden the spectrum of possible frequencies, in a quantifiable way. Simulations show that the theoretically predicted values are in excellent agreement with the numerically observed behavior. Adaptable delays are then proposed as one additional mechanism through which neural systems could tailor their own dynamics. Accordingly, we derive recurrent backpropagation learning formulas for the adjustment of delays and other parameters in networks with delayed interactions and discuss some possible applications

Published in:

IEEE Transactions on Neural Networks  (Volume:5 ,  Issue: 4 )