title: A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.
abstract: A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.
  
  
  
  Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field.
authors.affiliation: Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 10044 Stockholm, Sweden.|Institute for Adaptive and Neural Computation, Edinburgh University, EH8 9AB Edinburgh, Scotland.
authors.forename: Florian
authors.identifier: 0000-0002-7314-8562
authors.initials: F
authors.lastname: Fiebig
authors.affiliation: Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 10044 Stockholm, Sweden, ala@kth.se.|Department of Numerical Analysis and Computer Science, Stockholm University, 10691 Stockholm, Sweden, and.
authors.forename: Anders
authors.identifier: 0000-0002-2358-7815
authors.initials: A
authors.lastname: Lansner
mesh_terms: D003198:Computer Simulation; D055724:Electrophysiological Phenomena / Q000502:physiology; D006801:Humans; D008570:Memory, Short-Term / Q000502:physiology*; D011939:Mental Recall; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D017966:Pyramidal Cells / Q000502:physiology; D014706:Verbal Learning
keywords: Hebbian plasticity; primacy; recency; short-term potentiation; word list learning; working memory