Dynamical origin of the effective storage capacity in the brain's working memory.

The capacity of working memory (WM), a short-term buffer for information in the brain, is limited. We suggest a model for sequential WM that is based upon winnerless competition amongst representations of available informational items. Analytical results for the underlying mathematical model relate WM capacity and relative lateral inhibition in the corresponding neural network. This implies an upper bound for WM capacity, which is, under reasonable neurobiological assumptions, close to the "magical number seven."

[1]  Paul M Bays,et al.  Dynamic Shifts of Limited Working Memory Resources in Human Vision , 2008, Science.

[2]  R. Miles,et al.  Variation in strength of inhibitory synapses in the CA3 region of guinea‐pig hippocampus in vitro. , 1990, The Journal of physiology.

[3]  M. Hasselmo,et al.  Graded persistent activity in entorhinal cortex neurons , 2002, Nature.

[4]  H. Markram,et al.  Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. , 1997, The Journal of physiology.

[5]  Jeffrey N Rouder,et al.  An assessment of fixed-capacity models of visual working memory , 2008, Proceedings of the National Academy of Sciences.

[6]  Klaus Oberauer,et al.  A formal model of capacity limits in working memory , 2006 .

[7]  Adam Gazzaley,et al.  Neural Suppression of Irrelevant Information Underlies Optimal Working Memory Performance , 2009, The Journal of Neuroscience.

[8]  Nancy B. Carlisle,et al.  Working memory guidance of attention depends on memory's relevance for search , 2010 .

[9]  Klaus Oberauer,et al.  Interference between storage and processing in working memory: Feature overwriting, not similarity-based competition , 2009, Memory & cognition.

[10]  G. Mangun.  Neural mechanisms of visual selective attention. , 1995, Psychophysiology.

[11]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[12]  R. Huerta,et al.  Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model. , 2008, Chaos.

[13]  R Huerta,et al.  Dynamical encoding by networks of competing neuron groups: winnerless competition. , 2001, Physical review letters.

[14]  Jesper Tegnér,et al.  Mechanism for top-down control of working memory capacity , 2009, Proceedings of the National Academy of Sciences.

[15]  D. Saari.  Basic Geometry of Voting , 1995 .

[16]  Ramón Huerta,et al.  Transient Cognitive Dynamics, Metastability, and Decision Making , 2008, PLoS Comput. Biol..

[17]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[18]  V. Zhigulin,et al.  On the origin of reproducible sequential activity in neural circuits. , 2004, Chaos.

[19]  Arnold B. Urken,et al.  Classics of social choice , 1995 .

[20]  M. Tsodyks,et al.  Synaptic Theory of Working Memory , 2008, Science.

[21]  Gilles Laurent,et al.  Transient Dynamics for Neural Processing , 2008, Science.

[22]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[23]  N. Cowan.  Attention and Memory: An Integrated Framework , 1995 .

[24]  Ramón Huerta,et al.  Dynamical encoding by networks of competing neuron groups: winnerless competition. , 2001 .

[25]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[26]  Christian Bick,et al.  On the occurrence of stable heteroclinic channels in Lotka–Volterra models , 2010 .

[27]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .