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Discrete working memory

Discrete working memory. Discrete working memory. Delay. Discrete working memory. Discrete working memory. Discrete working memory. Green cue. Delay. Red cue. Delay. 27. 27. Spike rate (Hz). Spike rate (Hz). 0. 0. 15. 30. -15. 0. -15. 0. 15. 30. Time (sec). Time (sec).

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Discrete working memory

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  1. Discrete working memory

  2. Discrete working memory Delay

  3. Discrete working memory

  4. Discrete working memory

  5. Discrete working memory Green cue Delay Red cue Delay 27 27 Spike rate (Hz) Spike rate (Hz) 0 0 15 30 -15 0 -15 0 15 30 Time (sec) Time (sec) Data from inferotemporal cortex Fuster and Jervey, Science (1981)

  6. Spatial working memory

  7. Spatial working memory Direction of monkey's gaze

  8. Spatial working memory

  9. Spatial working memory Delay

  10. Spatial working memory

  11. Spatial working memory

  12. Data from Funahashi et al. (1989) J. Neurophysiol. 61:331

  13. Parametric Working Memory and Sequential Discrimination Experiments by group of R. Romo et al., UNAM Nature 399:470 (1999), Cereb. Cort. 13:1196 (2003)

  14. Choose f1 > f2 f1 f2

  15. or f2 > f1 f1 f2

  16. base delay f1(Hz) 10 14 18 22 26 30 34 Rastergram: 30 Trial-averaged firing rate Firing rate (Hz) 0 0.5 Time (sec) 3.5 18 Romo et al. Nature 1999 Tuning curve of memory activity Firing rate (Hz) (from Miller et al. Cerebral Cortex 2003) 5 10 Stimulus, f1 (Hz) 34

  17. Delay activity in PFC ties the task together Number of tuned neurons Firing rates Primary somatosensory cortex: Secondary somatosensory cortex: Premotor cortex: Prefrontal cortex: Romo et al. Philos Trans Roy Soc: Biol, 2002

  18. Network model: firing rate curve (mean-field theory)

  19. Rate=f(I) I Rate I=f(Rate) Network model: firing rates with weak feedback Firing rate curve Feedback current Rate

  20. I(app) Rate=f(I) I Rate I=f(Rate) Network model: firing rates with weak feedback Firing rate curve Feedback current Rate

  21. Network model: firing rates with strong feedback Firing rate curve Feedback current

  22. Network model: Bistability from strong feedback

  23. I(app) Rate=f(I) I Rate I=f(Rate) Network model: firing rates with strong feedback Firing rate curve Total current

  24. Network model: Bistability from strong feedback

  25. Network model: recurrent excitation = pool of tens to hundreds of self-exciting neurons

  26. Network model: bistability from recurrent excitation Input spikes here Memory activity

  27. Miller and Wang, Chaos 2006 cf Miller et al, PLOS Biol. 2005 Stability increases exponentially with number of neurons in pool

  28. Rate=f(I) I Rate I=f(Rate) Network model: Continuous attractor with moderate feedback Firing rate curve Feedback current W Rate

  29. I(app) Rate=f(I) I Rate I=f(Rate) Network model: Continuous attractor with moderate feedback Firing rate curve Feedback current

  30. I(app) Rate=f(I) I Rate I=f(Rate) Network model: Continuous attractor with moderate feedback Firing rate curve Feedback current

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