Noise tolerance of attractor and feedforward memory models

Neural Computation

Volumn 24, Issue 2, 2012, Pages 332-390

  Lim, Sukbin ^a   Goldman, Mark S ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; FEEDBACK SYSTEM; LETTER; NERVE CELL; PHYSIOLOGY; SHORT TERM MEMORY;

FEEDBACK, PHYSIOLOGICAL; MEMORY, SHORT-TERM; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NEURONS;

CONVEYING; DYNAMICS; ECONOMIC AND SOCIAL EFFECTS; FISHER INFORMATION MATRIX; GAUSSIAN NOISE (ELECTRONIC); LINEAR NETWORKS; NETWORK ARCHITECTURE; NEURAL NETWORKS;

ATTRACTOR NETWORKS; DYNAMIC RANGE; FEED FORWARD; FEED-FORWARD NETWORK; FISHER INFORMATION; MEMORY MODELING; MEMORY NETWORK; NOISE TOLERANCE; SHORT TERM MEMORY; SIGNAL ARRIVAL;

NEURONS;

EID: 84856384696     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00234     Document Type: Letter

References (53)

1. Abbott, L. F., & Dayan, P. (1999). The effect of correlated variability on the accuracy of a population code. Neural Comput., 11(1), 91-101.
2. Aksay, E., Baker, R., Seung, H. S., & Tank, D. W. (2000). Anatomy and discharge properties of pre-motor neurons in the goldfish medulla that have eye-position signals during fixations. J. Neurophysiol., 84(2), 1035-1049.
3. Amit, D. J., & Brunel, N. (1997). Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex, 7(3), 237-252.
4. Averbeck, B. B., Latham, P. E., & Pouget, A. (2006). Neural correlations, population coding and computation. Nat. Rev. Neurosci., 7(5), 358-366.
5. Baddeley, R., Abbott, L. F., Booth, M. C., Sengpiel, F., Freeman, T., & Wakeman, E. A. (1997). Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. Biol. Sci., 264(1389), 1775-1783.
6. Brody, C. D., Romo, R., & Kepecs, A. (2003). Basic mechanisms for graded persistent activity: Discrete attractors, continuous attractors, and dynamic representations. Curr. Opin. Neurobiol., 13(2), 204-211.
7. Brown, E., Gao, J., Holmes, P., Bogacz, R., Gilzenrat, M.,& Cohen, J. D. (2005). Simple neural networks that optimize decisions. International Journal of Bifurcation and Chaos, 15(3), 803-826.
8. Butts, D. A., & Goldman, M. S. (2006). Tuning curves, neuronal variability, and sensory coding. PLoS Biol., 4(4), e92.
9. Camperi, M., & Wang, X. J. (1998). A model of visuospatial working memory in prefrontal cortex: Recurrent network and cellular bistability. J. Comput. Neurosci., 5(4), 383-405.
10. Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., & Corrado, G. S. (2010). Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nat. Neurosci., 13(3), 369-378.
11. Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nat. Neurosci., 3, 1184-1191.
12. Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D. (2008). One-dimensional dynamics of attention and decision making in LIP. Neuron, 58(1), 15-25.
13. Ganguli, S., Huh, D., & Sompolinsky, H. (2008). Memory traces in dynamical systems. Proc. Natl. Acad. Sci. USA, 105(48), 18970-18975.
14. Ganguli, S., & Latham, P. (2009). Feedforward to the past: The relation between neuronal connectivity, amplification, and short-term memory. Neuron, 61(4), 499-501.
15. Goldman, M. S. (2009). Memorywithout feedback in a neural network. Neuron, 61(4), 621-634.
16. Goldman, M. S., Compte, A., & Wang, X. J. (2009). Neural integrator models. In L. R. Squire (Ed.), Encyclopedia of Neuroscience (Vol. 6, pp. 165-178). Oxford: Academic Press.
17. Goldman, M. S., Levine, J. H., Major, G., Tank, D. W., & Seung, H. S. (2003). Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. Cereb. Cortex, 13(11), 1185-1195.
18. Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14(3), 477-485.
19. Hempel, C. M., Hartman, K. H., Wang, X. J., Turrigiano, G. G., & Nelson, S. B. (2000). Multiple forms of short-term plasticity at excitatory synapses in ratmedial prefrontal cortex. J. Neurophysiol., 83(5), 3031-3041.
20. Horn, R. A., & Johnson, C. R. (1985). Matrix analysis. Cambridge: Cambridge University Press.
21. Koulakov, A. A., Raghavachari, S., Kepecs, A., & Lisman, J. E. (2002). Model for a robust neural integrator. Nat. Neurosci., 5(8), 775-782.
22. Latham, P. E., Deneve, S., & Pouget, A. (2003). Optimal computation with attractor networks. J. Physiol. Paris, 97(4-6), 683-694.
23. Long, M. A., Jin, D. Z., & Fee, M. S. (2010). Support for a synaptic chain model of neuronal sequence generation. Nature, 468(7322), 394-399.
24. MacDonald, C., Lepage, K., Eden, U., & Eichenbaum, H. (2011). Hippocampal "time cells" bridge the gap in memory for discontiguous events. Neuron, 71(4), 737-749.
25. Machens, C. K., Romo, R., & Brody, C. D. (2010). Functional, but not anatomical, separation of "what" and "when" in prefrontal cortex. J. Neurosci., 30(1), 350-360.
26. Major, G., & Tank, D. (2004). Persistent neural activity: Prevalence and mechanisms. Curr. Opin. Neurobiol., 14(6), 675-684.
27. Marder, E., Abbott, L. F., Turrigiano, G. G., Liu, Z, & Golowasch, J. (1996). Memory from the dynamics of intrinsic membrane constants. Proc. Natl. Acad. Sci. U.S.A., 93, 13481-13486.
28. Mauk, M. D., & Buonomano, D. V. (2004). The neural basis of temporal processing. Annu. Rev. Neurosci., 27, 307-340.
29. Mongillo, G., Barak, O., & Tsodyks, M. (2008). Synaptic theory of working memory. Science, 319(5869), 1543-1546.
30. Murphy, B. K., & Miller, K. D. (2009). Balanced amplification: A new mechanism of selective amplification of neural activity patterns. Neuron, 61(4), 635-648.
31. Pastalkova, E., Itskov, V., Amarasingham, A., & Buzsaki, G. (2008). Internally generated cell assembly sequences in the rat hippocampus. Science, 321(5894), 1322-1327.
32. Ploner, C. J., Gaymard, B., Rivaud, S., Agid, Y., & Pierrot-Deseilligny, C. (1998). Temporal limits of spatial working memory in humans. Eur. J. Neurosci., 10(2), 794-797.
33. Rabinovich, M., Huerta, R., & Laurent, G. (2008). Transient dynamics for neural processing. Science, 321(5885), 48-50.
34. Rajan, K., Abbott, L. F., & Sompolinsky, H. (2010). Stimulus-dependent suppression of chaos in recurrent neural networks. Phys. Rev. E. Stat. Nonlin. Soft Matter Phys., 82(1 Pt. 1), 011903.
35. Renart, A., Song, P., & Wang, X. J. (2003). Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. Neuron, 38(3), 473-485.
36. Robinson, D. A. (1989). Integrating with neurons. Annu. Rev. Neurosci., 12, 33-45.
37. Romo, R., Brody, C. D., Hernandez, A., & Lemus, L. (1999). Neuronal correlates of parametric working memory in the prefrontal cortex. Nature, 399(6735), 470-473.
38. Salinas, E., & Abbott, L. F. (1994). Vector reconstruction from firing rates. J. Comput. Neurosci., 1(1-2), 89-107.
39. Savin, C., & Triesch, J. (2009). Developing a working memory with rewardmodulated STDP. CoSyNe Abs., T29.
40. Seung, H. S. (1996). How the brain keeps the eyes still. Proc. Natl. Acad. Sci. USA, 93(23), 13339-13344.
41. Shadlen, M. N., Britten, K. H., Newsome, W. T., & Movshon, J. A. (1996). A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci., 16(4), 1486-1510.
42. Sharp, P. E., Blair, H. T., & Cho, J. (2001). The anatomical and computational basis of the rat head-direction cell signal. Trends Neurosci., 24(5), 289-294.
43. Singh, R., & Eliasmith, C. (2006). Higher-dimensional neurons explain the tuning and dynamics of working memory cells. J. Neurosci., 26(14), 3667-3678.
44. Sompolinsky, H., Yoon, H., Kang, K., & Shamir, M. (2001). Population coding in neuronal systems with correlated noise. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 64(5 Pt. 1), 051904.
45. Taube, J. S., & Bassett, J. P. (2003). Persistent neural activity in head direction cells. Cereb. Cortex, 13(11), 1162-1172.
46. Toyoizumi, T. (2010). An extensive memory lifetime is achieved by coupled nonlinear neurons. Soc. Neurosci. Abs., 197.14.
47. Trefethen, L. N., & Embree, M. (2005). Spectra and pseudospectra: The behavior of nonnormal matrices and operators. Princeton, NJ: Princeton University Press.
48. Wang, X. J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci., 24(8), 455-463.
49. Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P. S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci., 9(4), 534-542.
50. Weber, R. B., & Daroff, R. B. (1972). Corrective movements following refixation saccades: Type and control system analysis. Vision Res., 12(3), 467-475.
51. White, J. M., Sparks, D. L., & Stanford, T. R. (1994). Saccades to remembered target locations: An analysis of systematic and variable errors. Vision Res., 34(1), 79-92.
52. White, O. L., Lee, D. D., & Sompolinsky, H. (2004). Short-termmemory in orthogonal neural networks. Phys. Rev. Lett., 92(14), 148102.
53. Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 370(6485), 140-143.