Balanced cortical microcircuitry for maintaining information in working memory

Nature Neuroscience

Volumn 16, Issue 9, 2013, Pages 1306-1314

  Lim, Sukbin ^a,b,c   Goldman, Mark S ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

4 AMINOBUTYRIC ACID A RECEPTOR; ALPHA AMINO 3 HYDROXY 5 METHYL 4 ISOXAZOLEPROPIONIC ACID; AMPA RECEPTOR; N METHYL DEXTRO ASPARTIC ACID; N METHYL DEXTRO ASPARTIC ACID RECEPTOR;

ARTICLE; BRAIN REGION; ELECTROENCEPHALOGRAM; FEEDBACK SYSTEM; MEMORY CONSOLIDATION; MEMORY DISORDER; NEGATIVE FEEDBACK; NEOCORTEX; NERVE CELL EXCITABILITY; NERVE CELL INHIBITION; NERVE CELL NETWORK; POSITIVE FEEDBACK; PRIORITY JOURNAL; SHORT TERM MEMORY; WORKING MEMORY;

ACTION POTENTIALS; ANIMALS; AUTOMATIC DATA PROCESSING; CEREBRAL CORTEX; HUMANS; MEMORY, SHORT-TERM; MODELS, NEUROLOGICAL; NERVE NET; NEURONS; SYNAPSES;

EID: 84883447531     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.3492     Document Type: Article

References (54)

1. Jonides, J. et al. The mind and brain of short-term memory. Annu. Rev. Psychol. 59, 193-224 (2008).
2. Fuster, J.M. & Alexander, G.E. Neuron activity related to short-term memory. Science 173, 652-654 (1971).
3. Major, G. & Tank, D. Persistent neural activity: prevalence and mechanisms. Curr. Opin. Neurobiol. 14, 675-684 (2004).
4. Durstewitz, D., Seamans, J.K. & Sejnowski, T.J. Neurocomputational models of working memory. Nat. Neurosci. 3, 1184-1191 (2000).
5. Wang, X.J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455-463 (2001).
6. Brody, C.D., Romo, R. & Kepecs, A. Basic mechanisms for graded persistent activity: discrete attractors, continuous attractors and dynamic representations. Curr. Opin. Neurobiol. 13, 204-211 (2003).
7. Seung, H.S. How the brain keeps the eyes still. Proc. Natl. Acad. Sci. USA 93, 13339-13344 (1996).
8. Machens, C.K., Romo, R. & Brody, C.D. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307, 1121-1124 (2005).
9. Wang, X.J. Decision making in recurrent neuronal circuits. Neuron 60, 215-234 (2008).
10. Haider, B. & McCormick, D.A. Rapid neocortical dynamics: cellular and network mechanisms. Neuron 62, 171-189 (2009).
11. Wang, H., Stradtman, G.G., Wang, X.J. & Gao, W.J. A specialized NMDA receptor function in layer 5 recurrent microcircuitry of the adult rat prefrontal cortex. Proc. Natl. Acad. Sci. USA 105, 16791-16796 (2008).
12. Wang, H.X. & Gao, W.J. Cell type-specific development of NMDA receptors in the interneurons of rat prefrontal cortex. Neuropsychopharmacology 34, 2028-2040 (2009).
13. Rotaru, D.C., Yoshino, H., Lewis, D.A., Ermentrout, G.B. & Gonzalez-Burgos, G. Glutamate receptor subtypes mediating synaptic activation of prefrontal cortex neurons: relevance for schizophrenia. J. Neurosci. 31, 142-156 (2011).
14. Wang, M. et al. NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron 77, 736-749 (2013).
15. Softky, W.R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334-350 (1993).
16. Compte, A. et al. Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J. Neurophysiol. 90, 3441-3454 (2003).
17. Haider, B., Duque, A., Hasenstaub, A.R. & McCormick, D.A. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci. 26, 4535-4545 (2006).
18. Shu, Y., Hasenstaub, A. & McCormick, D.A. Turning on and off recurrent balanced cortical activity. Nature 423, 288-293 (2003).
19. Murphy, B.K. & Miller, K.D. Balanced amplification: a new mechanism of selective amplification of neural activity patterns. Neuron 61, 635-648 (2009).
20. Lisman, J.E., Fellous, J.M. & Wang, X.J. A role for NMDA-receptor channels in working memory. Nat. Neurosci. 1, 273-275 (1998).
21. Wang, X.J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587-9603 (1999).
22. Koulakov, A.A., Raghavachari, S., Kepecs, A. & Lisman, J.E. Model for a robust neural integrator. Nat. Neurosci. 5, 775-782 (2002).
23. Goldman, M.S., Levine, J.H., Major, G., Tank, D.W. & Seung, H.S. Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. Cereb. Cortex 13, 1185-1195 (2003).
24. Nikitchenko, M. & Koulakov, A. Neural integrator: a sandpile model. Neural Comput. 20, 2379-2417 (2008).
25. Shen, L. Neural integration by short term potentiation. Biol. Cybern. 61, 319-325 (1989).
26. Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534-542 (2006).
27. Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543-1546 (2008).
28. Barbieri, F. & Brunel, N. Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex? Front. Neurosci. 2, 114-122 (2008).
29. Vogels, T.P., Rajan, K. & Abbott, L.F. Neural network dynamics. Annu. Rev. Neurosci. 28, 357-376 (2005).
30. van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724-1726 (1996).
31. Knill, D.C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712-719 (2004).
32. Boerlin, M. & Deneve, S. Spike-based population coding and working memory. PLoS Comput. Biol. 7, e1001080 (2011).
33. Romo, R., Brody, C.D., Hernandez, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470-473 (1999).
34. Roitman, J.D. & Shadlen, M.N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475-9489 (2002).
35. Robinson, D.A. Integrating with neurons. Annu. Rev. Neurosci. 12, 33-45 (1989).
36. Cannon, S.C., Robinson, D.A. & Shamma, S. A proposed neural network for the integrator of the oculomotor system. Biol. Cybern. 49, 127-136 (1983).
37. Shadlen, M.N., Britten, K.H., Newsome, W.T. & Movshon, J.A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486-1510 (1996).
38. Shadlen, M.N. & Newsome, W.T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569-579 (1994).
39. Destexhe, A., Rudolph, M. & Pare, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739-751 (2003).
40. Renart, A., Moreno-Bote, R., Wang, X.J. & Parga, N. Mean-driven and fluctuation-driven persistent activity in recurrent networks. Neural Comput. 19, 1-46 (2007).
41. Roudi, Y. & Latham, P.E. A balanced memory network. PLoS Comput. Biol. 3, 1679-1700 (2007).
42. Major, G., Polsky, A., Denk, W., Schiller, J. & Tank, D.W. Spatiotemporally graded NMDA spike/plateau potentials in basal dendrites of neocortical pyramidal neurons. J. Neurophysiol. 99, 2584-2601 (2008).
43. Liu, G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat. Neurosci. 7, 373-379 (2004).
44. Tao, H.W. & Poo, M.M. Activity-dependent matching of excitatory and inhibitory inputs during refinement of visual receptive fields. Neuron 45, 829-836 (2005).
45. Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C. & Gerstner, W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569-1573 (2011).
46. Xie, X. & Seung, H.S. Spike-based learning rules and stabilization of persistent neural activity. in Advances in Neural Information Processing Systems Vol. 12 (eds. Solla, S.A., Leen, T.K. & Müller, K.-R.) 199-205 (2000).
47. Csete, M.E. & Doyle, J.C. Reverse engineering of biological complexity. Science 295, 1664-1669 (2002).
48. Ganguli, S. et al. One-dimensional dynamics of attention and decision making in LIP. Neuron 58, 15-25 (2008).
49. Coyle, J.T., Tsai, G. & Goff, D. Converging evidence of NMDA receptor hypofunction in the pathophysiology of schizophrenia. Ann. NY Acad. Sci. 1003, 318-327 (2003).
50. Wilson, H.R. Spikes, Decisions and Actions (Oxford University Press, 1999).
51. McCormick, D.A., Connors, B.W., Lighthall, J.W. & Prince, D.A. Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 54, 782-806 (1985).
52. Salin, P.A. & Prince, D.A. Spontaneous GABAA receptor-mediated inhibitory currents in adult rat somatosensory cortex. J. Neurophysiol. 75, 1573-1588 (1996).
53. Xiang, Z., Huguenard, J.R. & Prince, D.A. GABAA receptor-mediated currents in interneurons and pyramidal cells of rat visual cortex. J. Physiol. (Lond.) 506, 715-730 (1998).
54. Hansel, D., Mato, G., Meunier, C. & Neltner, L. On numerical simulations of integrate-and-fire neural networks. Neural Comput. 10, 467-483 (1998).