title: Synaptic Correlates of Working Memory Capacity.
abstract: Psychological studies indicate that human ability to keep information in readily accessible working memory is limited to four items for most people. This extremely low capacity severely limits execution of many cognitive tasks, but its neuronal underpinnings remain unclear. Here we show that in the framework of synaptic theory of working memory, capacity can be analytically estimated to scale with characteristic time of short-term synaptic depression relative to synaptic current time constant. The number of items in working memory can be regulated by external excitation, enabling the system to be tuned to the desired load and to clear the working memory of currently held items to make room for new ones.
authors.affiliation: Brain Science Center, Institute of Basic Medical Sciences, Beijing 100850, China; Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Neuroscience, Columbia University, New York, NY 10032, USA.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Mi
authors.affiliation: Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Katkov
authors.affiliation: Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Neuroscience, Columbia University, New York, NY 10032, USA. Electronic address: misha@weizmann.ac.il.
authors.forename: Misha
authors.initials: M
authors.lastname: Tsodyks
mesh_terms: D006801:Humans; D008570:Memory, Short-Term / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D013569:Synapses / Q000502:physiology*
keywords: attractor neural networks; integrate and fire model; modeling; neural networks; rate model; short-term memory; short-term synaptic plasticity; synaptic depression; synaptic facilitation