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Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

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Kendrick, Keith M., Zhan, Yang, Fischer, Hanno, Nicol, Alister U., Zhang, Xuejuan and Feng, Jianfeng. (2011) Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex. BMC Neuroscience, Vol.12 (No.55). ISSN 1471-2202

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Official URL: http://dx.doi.org/10.1186/1471-2202-12-55

Abstract

Background: How oscillatory brain rhythms alone, or in combination, influence cortical information processing to
support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings
were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual
discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid
functional interpretation of learning-evoked changes.
Results: Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased,
as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and
gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and
this was not simply a consequence of increased theta amplitude. Actual discrimination performance was
significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with
theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during
stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons
could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar
changes were produced as in inferotemporal cortex after learning. The model showed that these changes could
potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without
increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity
following learning and its magnitude was correlated with discrimination performance.
Conclusions: Face discrimination learning produces significant increases in both theta amplitude and the strength
of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A
network model which can reproduce these changes suggests that a key function of such learning-evoked
alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal
firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way
learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.

Item Type:

Journal Article

Subjects:

Q Science > QP Physiology

Divisions:

Faculty of Science > Computer Science

Library of Congress Subject Headings (LCSH):

Learning -- Physiological aspects, Neural networks (Neurobiology), Theta rhythm

Journal or Publication Title:

BMC Neuroscience

Publisher:

BioMed Central Ltd.

ISSN:

1471-2202

Official Date:

9 June 2011

Dates:

Date	Event
9 June 2011	Published

Volume:

Vol.12

Number:

No.55

Identification Number:

10.1186/1471-2202-12-55

Status:

Peer Reviewed

Access rights to Published version:

Open Access

Funder:

Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), SEARCH Foundation, European Union (EU), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC)

Grant number:

BBS/B/07691 (BBSRC), 10771155 (NSFC), 10971196 (NSFC)

References:

1. Buzsaki G: Rhythms of the Brain. Oxford University Press, USA; 2006.
2. Gevins A, Smith ME, McEvoy L, Yu D: High-resolution EEG mapping of
cortical activation related to working memory: effects of task difficulty,
type of processing and practice. Cereb Cortex 1997, 7:374-385.
3. Raghavachari S, Kahana MJ, Rizzuto DS, Caplan JB, Kirschen MP,
Bourgeois B, Madsen R, Lisman JE: Gating of human theta oscillations by
a working memory task. J Neurosci 2001, 21:3175-3183.
4. Lee H, Simpson GV, Logothetis NK, Rainer G: Phase locking of single
neuron activity to theta oscillations during working memory in monkey
extrastriate visual cortex. Neuron 2005, 45:147-156.
5. Holscher C, Anwyl R, Rowan MJ: Stimulation on the positive phase of
hippocampal theta rhythm induces long-term potentiation that can be
depotentiated by stimulation on the negative phase in area CA1 in vivo.
J Neurosci 1997, 17:6470-6477.
6. Jensen O, Kaiser J, Lachaux JP: Human gamma-frequency oscillations
associated with attention and memory. Trends Neurosci 2007, 30:317-324.
7. Csibra G, Davis G, Spratling MW, Johnson MH: Gamma oscillations and
object processing in the infant brain. Science 2000, 290:1582-1585.
8. Busch NA, Herrmann CS, Muller MM, Lenz D, Gruber T: A cross-laboratory
study of event-related gamma activity in a standard object recognition
paradigm. Neuroimage 2006, 33:1169-1177.
9. Fries P, Reynolds JH, Rorie AE, Desimone R: Modulation of oscillatory
neuronal synchronization by selective visual attention. Science 2001,
291:1560-1563.
10. Fries P: A mechanism for cognitive dynamics: neuronal communication
through neuronal coherence. Trends Cog Sci 2005, 9:474-480.
11. Canolty RT, Edwards E, Dalal SS, Soltani M, Nagarajan SS, Kirsch HE,
Berger MS, Barbaro NM, Knight RT: High gamma power is phase-locked to
theta oscillations in human neocortex. Science 2006, 313:1626-1628.
12. Tort ABL, Komorowski RW, Manns JR, Kopell NJ, Eichenbaum H: Thetagamma
coupling increases during the learning of item-context
associations. Proc Natl Acad Sci USA 2009, 106:20942-20947.
13. Tort ABL, Komorowski R, Eichenbaum H, Kopell N: Measuring Phase-
Amplitude Coupling Between Neuronal Oscillations of Different
Frequencies. J Neurophysiol 2010, 104:1195-1210.
14. Jensen O: Maintenance of multiple working memory items by temporal
segmentation. Neuroscience 2006, 139:237-249.
15. Givens B: Stimulus-evoked resetting of the dentate theta rhythm:
relation to working memory. NeuroReport 1996, 8:159-163.
16. Rizzuto DS, Madsen JR, Bromfield EB, Schulze-Bonhage A, Seelig D,
Aschenbrenner-Scheibe R, Kahana MJ: Reset of human neocortical
oscillations during a working memory task. Proc Natl Acad Sci USA 2003,
100:7931-7936.
17. Sederberg PB, Kahana MJ, Howard MW, Donner EJ, Madsen JR: Theta and
gamma oscillations during encoding predict subsequent recall. J
Neurosci 2003, 23:10809-10814.
18. Lakatos P, Shah AS, Knuth KH, Ulbert I, Karmos G, Schroeder CE: An
oscillatory hierarchy controlling neuronal excitability and stimulus
processing in the auditory cortex. J Neurophys 2005, 94:1904-1911.
19. Moretti DV, Fracassi C, Pievani M, Geroldi C, Zanetti O, Sosta K, Rossini PM,
Frisoni GB: Increase of theta/gamma ratio is associated with memory
impairment. Clin Neurophysiol 2009, 120:295-303.
20. Tate AJ, Fischer H, Leigh AE, Kendrick KM: Behavioural and
neurophysiological evidence for face identity and face emotion
processing in animals. Phil Trans Roy Soc B: Biol Sci 2006, 361:2155-2172.
21. Jacobs J, Kahana MJ, Ekstrom AD, Fried I: Brain oscillations control timing
of single-neuron activity in humans. J Neurosci 2007, 27:3839-3844.
22. Liu L, Wong TP, Pozza MF, Lingenhoehl K, Wang Y, Sheng M, Auberson YP,
Wang YT: Role of NMDA receptor subtypes in governing the direction of
hippocampal synaptic plasticity. Science 2004, 304:1021-1024.
23. Sceniak M, MacIver MB: Slow GABAA mediated synaptic transmission in
rat visual cortex. BMC Neurosci 2008, 9:8.
24. Pearce RA: Physiological evidence for two distinct GABAA responses in
rat hippocampus. Neuron 1993, 10:189-200.
25. White JA, Banks MI, Pearce RA, Kopell NJ: Networks of interneurons with
fast and slow γ-aminobutyric acid type A (GABAA) kinetics provide
substrate for mixed gamma-theta rhythm. Proc Natl Acad Sci USA 2000,
97:8128-8133.
26. Tort ABL, Rotstein HG, Dugladze T, Gloveli T, Kopell NJ: On the formation
of gamma-coherent cell assemblies by oriens lacunosum-moleculare
interneurons in the hippocampus. Proc Natl Acad Sci USA 2007,
104:3490-13495.
27. Bland BH, Oddie SD: Theta band oscillation and synchrony in the
hippocampal formation and associated structures: the case for its role in
sensorimotor integration. Behav Brain Res 2001, 127:119-36.
28. Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E,
Sejnowski TJ: Dynamic brain sources of visual evoked responses. Science
2002, 295:690-694.
29. Mazaheri A, Jensen O: Posterior α activity is not phase-reset by visual
stimuli. Proc Natl Acad Sci USA 2006, 103:2948-2952.
30. Rolls ET, Judge SJ, Sanghera MK: Activity of neurones in the
inferotemporal cortex of the alert monkey. Brain Res 1977, 130:229-238.
31. Tsao DY, Freiwald WA, Tootel RBH, Livingstone MS: A cortical region
consisting entirely of face-selective cells. Science 2006, 311:670-674.
32. Hermann MJ, Ehlis A-C, Ellgring H, Fallgatter AJ: Early stages (P100) of face
perception in humans as measured with event-related potentials (ERPs).
J Neural Transm 2005, 112:1073-1081.
33. Liu J, Harris A, Kanwisher N: Stages of processing in face perception: an
MEG study. Nat Neurosci 2002, 5:910-916.
34. Peissig JJ, Singer J, Kawasaki K, Sheinberg DL: Effects of long-term object
familiarity on event-related potentials in the monkey. Cereb Cortex 2007,
17:1323-1334.
35. Bentin S, Allison T, Puce A, Perez E, McCarthy G: Electrophysiological
studies of face perception in humans. J Cog Neurosci 1996, 6:551-565.
36. Scott LS, Tanaka JW, Sheinberg DL, Curran T: A re-evaluation of the
electrophysiological correlates of expert object processing. J Cogn
Neurosci 2006, 18:1453-1465.
37. Anderson B, Mruczek REB, Kawasaki K, Sheinberg D: Effects of familiarity of
neural activity in monkey inferior temporal lobe. Cereb Cortex 2008,
18:2540-2552.
38. Teyler TJ, Hamm JP, Clapp WC, Johnson BW, Corballis MC, Kirk IJ: Longterm
potentiation of human visual evoked potentials. Eur J Neurosci 2005,
21:2045-2050.
39. Ge T, Kendrick KM, Feng J: A novel extended Granger causal model
approach demonstrates brain hemispheric differences during face
recognition learning. PLoS Comput Biol 2009, 5(11):e1000570.
40. Rutishauser U, Ross IB, Mamelak AN, Schuman EM: Human memory
strength is predicted by theta-frequency phase-locking of single
neurons. Nature 2010, 464:903-907.
41. Smith MA, Kohn A: Spatial and temporal scales of neuronal correlation in
primary visual cortex. J Neurosci 2008, 28:12591-12603.
42. Kendrick KM, Baldwin BA: Single unit recording in the conscious sheep. In
Methods in Neuroscience. Edited by: Conn PM. New York: Academic Press;
1991:3-15.
43. Horton PM, Nicol AU, Kendrick KM, Feng JF: Spike sorting based upon
machine learning algorithms (SOMA). J Neurosci Meth 2007, 160:52-68, 36.
44. Torrence C, Compo GP: A Practical Guide to Wavelet Analysis. Bull Am
Meteorological Soc 1998, 79:61-78.
45. Fisher NI: Statistical Analysis of Circular Data. Cambridge University Press;
1995.
46. Halliday DM, Rosenberg JR, Amjad AM, Breeze P, Conway BA, Farmer SF: A
framework for the analysis of mixed time series/point process data–
Theory and application to the study of physiological tremor, single
motor unit discharges and electromyograms. Prog Biophys Mol Biol 1995,
64:237-278.
47. Wang X-J: Synaptic basis of cortical persistent activity: the importance of
NMDA receptors to working memory. J Neurosci 1999, 19:9587-9603.
48. Wang X-J, Rinzel J: Alternating and synchronous rhythms in reciprocally
inhibitory model neurons. Neural Comp 1992, 4:84-97.