Summary: Journal of Computational Neuroscience 13, 111124, 2002
c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
Computation by Ensemble Synchronization in Recurrent Networks
with Synaptic Depression
ALEX LOEBEL AND MISHA TSODYKS
Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel
Received July 23, 2001; Revised May 2, 2002; Accepted May 16, 2002
Action Editor: Carson C. Chow
Abstract. While computation by ensemble synchronization is considered to be a robust and efficient way for
information processing in the cortex (C. Von der Malsburg and W. Schneider (1986) Biol. Cybern. 54: 2940; W.
Singer (1994) Inter. Rev. Neuro. 37: 153183; J.J. Hopfield (1995) Nature 376: 3336; E. Vaadia et al. (1995)
Nature 373: 515518), the neuronal mechanisms that might be used to achieve it are yet to be uncovered. Here we
analyze a neural network model in which the computations are performed by near coincident firing of neurons in
response to external inputs. This near coincident firing is enabled by activity dependent depression of inter-neuron
connections. We analyze the network behavior by using a mean-field approximation, which allows predicting the
network response to various inputs. We demonstrate that the network is very sensitive to temporal aspects of
the inputs. In particular, periodically applied inputs of increasing frequency result in different response profiles.
Moreover, applying combinations of different stimuli lead to a complex response, which cannot be easily predicted
from responses to individual components. These results demonstrate that networks with synaptic depression can