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- W2566935216 endingPage "e0183710" @default.
- W2566935216 startingPage "e0183710" @default.
- W2566935216 abstract "Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed." @default.
- W2566935216 created "2017-01-06" @default.
- W2566935216 creator A5016275776 @default.
- W2566935216 creator A5020584182 @default.
- W2566935216 creator A5063875367 @default.
- W2566935216 creator A5066826284 @default.
- W2566935216 date "2017-08-28" @default.
- W2566935216 modified "2023-09-24" @default.
- W2566935216 title "Latching dynamics in neural networks with synaptic depression" @default.
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