Controlling the speed of synfire chains

Author(s):  
Thomas Wennekers ◽  
Günther Palm
Keyword(s):  
2004 ◽  
Vol 2004 (230) ◽  
pp. tw155-tw155
Keyword(s):  

2010 ◽  
Vol 68 ◽  
pp. e211
Author(s):  
Chris Trengove ◽  
Cees van Leeuwen ◽  
Markus Diesmann

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.


1999 ◽  
Vol 80 (6) ◽  
pp. 433-447 ◽  
Author(s):  
Hans-Martin R. Arnoldi ◽  
Karl-Hans Englmeier ◽  
Wilfried Brauer

2018 ◽  
Author(s):  
Marcelo Matheus Gauy ◽  
Johannes Lengler ◽  
Hafsteinn Einarsson ◽  
Florian Meier ◽  
Felix Weissenberger ◽  
...  

AbstractThe hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.


2012 ◽  
Vol 206 (1) ◽  
pp. 54-64 ◽  
Author(s):  
George L. Gerstein ◽  
Elizabeth R. Williams ◽  
Markus Diesmann ◽  
Sonja Grün ◽  
Chris Trengove
Keyword(s):  

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