Stable and rapid recurrent processing in realistic autoassociative memories
Neural Computation
On numerical simulations of integrate-and-fire neural networks
Neural Computation
Neural modeling and functional brain imaging: an overview
Neural Networks - Special issue on the global brain: imaging and modelling
Journal of Cognitive Neuroscience
''What''—Then—''Where'' in Visual Working Memory: An Event-Related fMRI Study
Journal of Cognitive Neuroscience
FROST: A Distributed Neurocomputational Model of Working Memory Maintenance
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Stochastic Dynamics in the Brain and Probabilistic Decision-Making
Creating Brain-Like Intelligence
Modelling working memory through attentional mechanisms
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
The computational neuroscience of visual cognition: attention, memory and reward
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Modeling the BOLD correlates of competitive neural dynamics
Neural Networks
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Single-neuron recordings, functional magnetic resonance imaging (fMRI) data, and the effects of lesions indicate that the prefrontal cortex (PFC) is involved in some types of working memory and related cognitive processes. Based on these data, two different models of the topographical and functional organization of the PFC have been proposed: organization-by-stimulus-domain, and organization-by-process. In this article, we utilize an integrate-and-fire network to model both single-neuron and fMRI data on short-term memory in order to understand data obtained in topologically different parts of the PFC during working memory tasks. We explicitly model the mechanisms that underlie workingmemory-related activity during the execution of delay tasks that have a ''what''-then-''where'' design (with both object and spatial delayed responses within the same trial). The model contains different populations of neurons (as found experimentally) in attractor networks that respond in the delay period to the stimulus object, the stimulus position, and to combinations of both object and position information. The pools are arranged hierarchically and have global inhibition through inhibitory interneurons to implement competi tion. It is shown that a biasing attentional input to define the current relevant information (object or location) enables the system to select the correct neuronal populations during the delay period in what is a biased competition model of attention. The processes occurring at the AMPA and NMDA synapses are dynamically modeled in the integrate-and-fire implementation to produce realistic spiking dynamics. It is shown that the fMRI data characteristic of the dorsal PFC and linked to spatial processing and manipulation of items can be reproduced in the model by a high level of inhibition, whereas the fMRI data characteristic of the ventral PFC and linked to object processing can be produced by a lower level of inhibition, even though the network is itself topographically homogeneous with no spatial topology of the neurons. This article, thus, not only presents a model for how spatial versus object short-term memory could be implemented in the PFC, but also shows that the fMRI BOLD signal measured during such tasks from different parts of the PFC could reflect a higher level of inhibition dorsally, without this dorsal region necessarily being primarily spatial and the ventral region object-related.