The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper investigates spike-timing dependent plasticity (STDP) for recurrently connected weights in a network with fixed external inputs (homogeneous Poisson pulse trains). We use a dynamical system to model the network activity and predict its asymptotic evolution, which turns out to qualitatively depend on the learning parameters and the correlation structure of the inputs. Our predictions are...
Large-scale neural simulation requires high-performance hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. Using a scheme driven entirely by pre-synaptic spike events, we optimize both the data representation and processing for efficiency...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.