Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

Journal of Computational Neuroscience

Volumn 34, Issue 2, 2013, Pages 319-336

  Scarpetta, Silvia ^a,b   Giacco, Ferdinando ^c  

^a UNIVERSITY OF SALERNO   (Italy)

^b INFN SEZIONE DI ROMA   (Italy)

^c SECOND UNIVERSITY OF NAPLES   (Italy)

Author keywords

Associative memory; Learning and memory; Noise robustness; Phase of spikes coding; Replay; STDP; Storage capacity

Indexed keywords

ASSOCIATIVE PROCESSING; ASSOCIATIVE STORAGE; MEMORY ARCHITECTURE; NEURAL NETWORKS; NEURONS;

ASSOCIATIVE MEMORY; INTEGRATE AND FIRES; LEARNING AND MEMORY; NOISE ROBUSTNESS; PHASE RELATIONSHIPS; PHASE-OF-SPIKE CODING; REPLAY; SPATIOTEMPORAL PATTERNS; STDP; STORAGE CAPACITY;

DYNAMICS;

ARTICLE; ASSOCIATIVE MEMORY; CONTROLLED STUDY; DYNAMICS; EXCITABILITY; LEAKY INTEGRATE AND FIRE NETWORK; MATHEMATICAL MODEL; NERVE CELL MEMBRANE POTENTIAL; NERVE CELL NETWORK; NOISE; OSCILLATION; PHASE CODED SPIKE PATTERN; PLASTICITY; PRIORITY JOURNAL; SIGNAL TRANSDUCTION; SPIKE; SPIKE TIMING DEPENDENT PLASTICITY;

EID: 84879689548     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-012-0423-7     Document Type: Article

References (83)

1. Abarbanel, H., Huerta, R., Rabinovich, M.I. (2002). Dynamical model of long-term synaptic plasticity. Proceedings of the National Academy of Sciences of the United States of America, 99, 10132-10137.
2. Amit, D.J., & Treves, A. (1989). Associative memory neural network with low temporal spiking rates. PNAS Biophysics, 86, 7871-7875.
3. Anishchenko, A., & Treves, A. (2006). Autoassociative memory retrieval and spontaneous activity bumps in small-world networks of integrate and fire neurons. Journal of Physiology (Paris), 100, 225-236.
4. Battaglia, F.P., & Treves, A. (1998). Stable and rapid recurrent processing in realistic autoassociative memories. Neural Computation, 10, 431-450.
5. Berkes, P., Orban, G., Lengyel, M., Fiser, J. (2011). Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science, 331, 83-87.
6. Bi, G.Q., & Poo, M.M. (1998). Precise spike timing determines the direction and extent of synaptic modifications in cultured hippocampal neurons. Journal of Neuroscience, 18, 10464-10472.
7. Bi, G.Q., & Poo, M.M. (2001). Synaptic modification by correlated activity: Hebb's postulate revisited. Annual Review of Neuroscience, 24, 139-166.
8. Blair, H.T., Gupta, K., Zhang, K. (2008). Conversion of a phase- to a rate-coded position signal by a three stage model of theta cells, grid cells, and place cells. Hippocampus, 18, 1239-1255.
9. Borisyuk, R.M., & Hoppensteadt, F.C. (1998). Memorizing and recalling spatial-temporal patterns in an oscillator model of the hippocampus. BioSystems, 48, 3-10.
10. Burgess, N., Barry, C., O'Keefe, J. (2007). An oscillatory interference model of grid cell firing. Hippocampus, 17, 801-812.
11. Buzsaki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304, 1926-1929.
12. Carr, M.F., Jadhav, S.P., Frank, L.M. (2011). Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval. Nature Neuroscience, 14, 147-53.
13. Colgin, L.L., Moser, E.I., Moser, M.-B. (2008). Understanding memory through hippocampal remapping. Trends in Neurosciences, 31(9), 469-477.
14. D'Angelo, E., & De Zeeuw, C.I. (2009). Timing and plasticity in the cerebellum: focus on the granular layer. Trends in Neurosciences, 32, 30-40.
15. D'Angelo, E., Koekkoek, S.K., Lombardo, P., Solinas, S., Ros, E., Garrido, J., Schonewille, M., De Zeeuw, C.I. (2009). Timing in the cerebellum: oscillations and resonance in the granular layer. Neuroscience, 162, 805-815.
16. Davidson, T.J., Kloosterman, F., Wilson, M.A. (2009). Hippocampal replay of extended experience. Neuron, 63, 497-507.
17. De Almeida, L., Idiart, M., Lisman, J.E. (2007). Memory retrieval time and memory capacity of the CA3 network: role of gamma frequency oscillations. Learning and Memory, 14, 795-806.
18. Debanne, D., Gahwiler, B.H., Thompson, S.M. (1998). Event driven programming long-term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures. Journal of Physiology, 507, 237-247.
19. Diba, K., & Buzsaki, G. (2007). Forward and reverse hippocampal place-cell sequences during ripples. Nature Neuroscience, 10, 1241-1242.
20. Euston, D.R., Tatsuno, M., McNaughton, B.L. (2007). Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. Science, 318, 1147-1150.
21. Feldman, D.E. (2000). Timing-based LTP and LTD and vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27, 45-56.
22. Fiete, I.R., Senn, W., Wang, C.Z.H., Hahnloser, R.H.R. (2010). Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron, 65(4), 563-576.
23. Geisler, C., Robbe, D., Zugaro, M., Sirota, A., Buzsaki, G. (2007). Hippocampal place cell assemblies are speed-controlled oscillators. Proceedings of the National Academy of Sciences of the United States of America, 104, 8149-8154.
24. Gelperin, A. (2006). Olfactory computations and network oscillation. The Journal of Neuroscience, 26, 1663-1668.
25. Gerstner, W., Kempter, R., Van Hemmen, L., Wagner, H. (1996). A neuronal learning rule for sub-millisecond temporal coding. Nature, 383, 76-81.
26. Gerstner, W., & Kistler, W. (2002). Spiking neuron models: Single neurons, populations, plasticity. New York: Cambridge University Press.
27. Gerstner, W., Ritz, R., van Hemmen, J.L. (1993). Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biological Cybernetics, 69, 503-515.
28. Gilson, M., Masquelier, T., Hugues, E. (2011). STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Computational Biology, 7(10), e100223.
29. Giocomo, L.M., Moser, M.-B., Moser, E.I. (2011). Computational models of grid cells. Neuron, 71(4), 589-603.
30. Girardeau, G., & Zugaro, M. (2011). Hippocampal ripples and memory consolidation. Current Opinion in Neurobiology, 21, 452-459.
31. Graupner, M., & Brunel, N. (2010). Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models. Frontiers in Computational Neuroscience, 4, 136.
32. Han, F., Caporale, N., Dan, Y. (2008). Reverberation of recent visual experience in spontaneous cortical waves. Neuron, 60, 321-327.
33. Hasselmo, M.E. (1993). Acetylcholine and learning in a cortical associative memory. Neural Computation, 5, 32-44.
34. Hasselmo, M.E. (1999). Neuromodulation: acetylcholine and memory consolidation. Trend in Cognitive Sciences, 3, 351-359.
35. Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79, 2554-2558.
36. Hopfield, J.J. (1995). Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, 33-36.
37. Huxter, J., Burgess, N., O'Keefe, J. (2003). Independent rate and temporal coding in hippocampal pyramidal cells. Nature, 425, 828-832.
38. Issa, J.B., & Zhang, K. (2012). Universal conditions for exact path integration in neural systems. Proceedings of the National Academy of Sciences of the United States of America, 109(17), 6716-6720.
39. Izhikevich, E.M. (2006). Polychronization: computation with spikes. Neural Computation, 18(2), 245-282.
40. Jeewajee, A., Barry, C., O'Keefe, J., Burgess, N. (2008). Grid cells and theta as oscillatory interference: electrophysiological data from freely moving rats. Hippocampus, 18, 1175-1185.
41. Ji, D., & Wilson, M.A. (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature Neuroscience, 10, 100-107.
42. Kayser, C., Montemurro, M.A., Nikos, K., Logothetis, N.K., Panzeri, S. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron, 61, 597-608.
43. Lansink, C.S., Goltstein, P.M., Lankelma, J.V., McNaughton, B.L., Pennartz, C.M.A. (2009). Hippocampus leads ventral striatum in replay of place-reward information. PLoS Biology, 7(8), e1000173.
44. Lau, P.-M., & Bi, G.-Q. (2005). Synaptic mechanisms of persistent reverberatory activity in neuronal networks. In Proceedings of the National Academy of Sciences of the United States of America, 102, 10333-10338.
45. Legenstein, R., Nager, C.C., Maass, W. (2005). What can a neuron learn with spike-timing-dependent plasticity? Neural Computation, 17, 2337-2382.
46. Leibold, C., & Kempter, R. (2006). Memory capacity for sequences in a recurrent network with biological constraints. Neural Computation, 18, 904-941.
47. Lengyel, M., & Dayan, P. (2007). Uncertainty, phase and oscillatory hippocampal recall. In Advances in neural information processing systems. MIT Press.
48. Lengyel, M., Huhn, Z., Erdi, P. (2005a). Computational theories on the function of theta oscillations. Biological Cybernetics, 92, 393-408.
49. Lengyel, M., Kwag, J., Paulsen, O., Dayan, P. (2005b). Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves. Nature Neuroscience, 8(12), 1677-1683.
50. Luczak, A., & Maclean, J.N. (2012). Default activity patterns at the neocortical microcircuit level. Frontiers in Integral Neuroscience, 6, 30.
51. Magee, J.C., & Johnston, D. (1997). A synaptically controlled associative signal for Hebbian plasticity in hippocampal neurons. Science, 275, 209-212.
52. Markram, H., Gerstner, W., PerJesper Sjostrom, P.J. (2011). A history of spike-timing-dependent plasticity. Frontiers in Synaptic Neuroscience, 3, 4.
53. Markram, H., Lubke, J., Frotscher, M., Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213-215.
54. Masquelier, T., Hugues, E., Deco, G., Thorpe, S.J. (2009). Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. The Journal of Neuroscience, 29, 13484-13493.
55. McNaughton, B.L., Battaglia, F.P., Jensen, O., Edvard, I., Moser, E.I., Moser, M.B. (2006). Path integration and the neural basis of the 'cognitive map'. Nature Reviews Neuroscience, 7, 663-678.
56. Memmesheimer, R.M., & Timme, M. (2006). Designing the dynamics of spiking neural networks. Physical Review Letters, 97, 188101.
57. Montemurro, M.A., Rasch, M.J., Murayama, Y., Logothetis, N.K., Panzeri, S. (2008). Phase-of-firing coding of natural visual stimuli in primary visual cortex. Current Biology, 18, 375-380.
58. Nadasdy, Z., Hirase, H., Czurko, A., Csicsvari, J., Buzsaki, G. (1999). Replay and time compression of recurring spike sequences in the hippocampus. The Journal of Neuroscience, 19, 9497-9507.
59. O'Keefe, J., & Burgess, N. (2005). Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocampus, 15, 853-866.
60. O'Keefe, J., & Recce, M.L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3, 317-330.
61. Olmi, S., Livi, R., Politi, A., Torcini, A. (2010). Collective oscillations in disordered neural networks. Physical Review E, 81, 046119.
62. Pastalkova, E., Itskov, V., Amarasingham, A., Buzsaki, G. (2008). Internally generated cell assembly sequences in the rat hippocampus. Science, 321, 1322-1327.
63. Ringach, D.L. (2009). Spontaneous and driven cortical activity: implications for computation. Current Opinion in Neurobiology, 19, 439-44.
64. Scarpetta, S., de Candia, A., Giacco, F. (2010). Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity. Frontiers in Synaptic Neuroscience, 2, 1-12.
65. Scarpetta, S., de Candia, A., Giacco, F. (2011a). Dynamics and storage capacity of neural networks with small-world topology. Frontiers in Artificial Intelligence and Applications, 226, 218-226.
66. Scarpetta, S., Giacco, F., de Candia, A. (2011b) Storage capacity of phase-coded patterns in sparse neural networks. Europhysics Letters, 95, 28006.
67. Scarpetta, S., & Marinaro, M. (2005). A learning rule for place fields in a cortical model: theta phase precession as a network effect. Hippocampus, 15(7), 979-989.
68. Scarpetta, S., Yoshioka, M., Marinaro, M. (2008). Encoding and replay of dynamic attractors with multiple frequencies Analysis of a STDP based learning rule. In Dynamic brain - From neural spikes to behaviors, Lecture notes in computer science (Vol. 5286/2008).
69. Scarpetta, S., Zhaoping, L., Hertz, J. (2001). Spike-timing-dependent learning for oscillatory networks. Advances in Neural Information Processing Systems, 13, 152-158.
70. Scarpetta, S., Zhaoping L., Hertz, J. (2002). Hebbian imprinting and retrieval in oscillatory neural networks. Neural Computation, 14, 2371-96.
71. Schwindel, C.D., & McNaughton, B.L. (2011). Hippocampal-cortical interactions and the dynamics of memory trace reactivation. Progress in Brain Research, 193, 163-177.
72. Shouval, H.Z., Wang, S.S., Wittenberg, G.M. (2010). Spike timing dependent plasticity: a consequence of more fundamental learning rules. Frontiers in Computational Neuroscience, 4, 19.
73. Siegel, M., Warden, M.R., Miller, E.K. (2009). Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences of the United States of America, 106, 21341-21346.
74. Sjostrom, P.J., Turrigiano, G., Nelson, S.B. (2001). Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32, 1149-1164.
75. Song, S., & Abbot, L.F. (2001). Cortical development and remapping through spike timing-dependent plasticity. Neuron, 32, 339-350.
76. Verduzco-Flores, S.O., Bodner, M., Ermentrout, B. (2012). A model for complex sequence learning and reproduction in neural populations. Journal of Computational Neuroscience, 32(3), 403-423.
77. Welday, A.C., Shlifer, I.G., Bloom, M.L., Zhang, K., Blair, H.T. (2011). Cosine directional tuning of theta cell burst frequencies: evidence for spatial coding by oscillatory interference. The Journal of Neuroscience, 31(45), 16157-16176.
78. Welinder, P.E., Burak, Y., Fiete, I.R. (2008). Grid cells: the position code, neural network models of activity, and the problem of learning. Hippocampus, 18, 1283-300.
79. Wills, T.J., Lever, C., Cacucci, F., Burges, N., O'Keefe, J. (2005). Attractor dynamics in the hippocampal representation of the local environment. Science, 308(5723), 873-876.
80. Wittenberg, G.M., & Wang, S.S.H. (2006). Malleability of spike-timing-dependent plasticity at the CA3-CA1 synapse. Journal of Neuroscience, 26, 6610-6617.
81. Yoshioka, M., Scarpetta, S., Marinaro, M. (2007). Spatiotemporal learning in analog neural networks using spike-timing-dependent synaptic plasticity. Physical Review E, 75, 051917.
82. Zilli, E.A., & Hasselmo, M. (2010). Coupled noisy spiking neurons as velocity-controlled oscillators in a model of grid cell spatial firing. The Journal of Neuroscience, 30(41), 13850-13860.
83. Zilli, E.A., Yoshida, M., Tahvildari, B., Giocomo, L.M., Hasselmo, M.E. (2009). Evaluation of the oscillatory interference model of grid cell firing through analysis and measured period variance of some biological oscillators. PLoS Computational Biology, 5, e1000573.