… to a cell’s response must be controlled to observe the cell’s intrinsic response precision.”
Similarly, Reinagel and Reid (2000) observe high precision in the cat’s LGN when subjecting it …

For a network of spiking neurons that encodes information in the timing of individual spike
times, we derive a supervised learning rule, SpikeProp, akin to traditional error-…

… connections, which skip one or more layer(s) and thus get added unmodified to the output
from … t be the value of time series s at time t. We define the return for s at time t over a one-day …

… Copyright © 2017 van Gerven and Bohte. This is an open-access article distributed under …
The use, distribution or reproduction in other forums is permitted, provided the original author(s…

… The transient impact a spike has on the neuron’s membrane … , εij(s) can be assumed to
have the same form ε(s−dax … • The classes S bb n and S ab n have VC-dimension Θ(n log(…

… h,t , S h,t , u o,t , S o,t }, where S h,t refers to a neuron firing-or-not in a hidden layer and S o,t
to … Bohte, SM Error-backpropagation in networks of fractionally predictive spiking neurons. In …

… As an example of relatively simple but realistic data, we clustered Fisher’s four-dimensional …
Bohte received the MS degree in physics from the University of Amsterdam, Amsterdam, The …

For a network of spiking neurons with reasonable postsynaptic potentials, we derive a
supervised learning rule akin to traditional error-back-propagation, SpikeProp and show how to …

… We make an abstraction from a patient’s physical arrival time and consider the request
time of when the actual request for a CT-scan is made. Medical attributes include whether the …

… and unpredictable nature of the stock price’s volatility. The most significant argument to …
With an additional stochastic process, which is correlated with the asset price process S t , …