Neural population coding: Combining insights from microscopic and mass signals

Trends in Cognitive Sciences

Volumn 19, Issue 3, 2015, Pages 162-172

  Panzeri, Stefano ^a,b   Macke, Jakob H ^b,c,d   Gross, Joachim ^e   Kayser, Christoph ^e  

^a ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c Bernstein Center Tübingen   (Germany)

^d UNIVERSITY OF TÜBINGEN   (Germany)

^e UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Cross correlation; Multiscale processing; Neural code; Neuroimaging; Oscillation; Relative timing; Sensory processing; State dependent coding

Indexed keywords

CODES (SYMBOLS); INDEPENDENT COMPONENT ANALYSIS; INFORMATION SCIENCE; NEURAL NETWORKS; NEUROIMAGING; NEUROPHYSIOLOGY;

CROSS CORRELATIONS; MULTISCALE PROCESSING; NEURAL CODE; OSCILLATION; RELATIVE TIMING; SENSORY PROCESSING; STATE-DEPENDENT;

BRAIN;

BRAIN; BRAIN CELL; CODING; DYNAMICS; HUMAN; INFORMATION PROCESSING; MICROSCOPY; NERVE CELL; NEURAL POPULATION CODING; NEUROIMAGING; NONHUMAN; REVIEW; SENSORY NERVE CELL; SENSORY STIMULATION; STIMULUS; ACTION POTENTIAL; ANIMAL; PHYSIOLOGY;

ACTION POTENTIALS; ANIMALS; BRAIN; HUMANS; NEURONS;

EID: 84933675980     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2015.01.002     Document Type: Review

References (119)

1. Averbeck B.B., et al. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 2006, 7:358-366.
2. Stanley G.B. Reading and writing the neural code. Nat. Neurosci. 2013, 16:259-263.
3. Hebb D.O. The Organization of Behaviour. A Neuropsychological Theory 1949, Wiley.
4. Jacobs A.L., et al. Ruling out and ruling in neural codes. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:5936-5941.
5. Stark E., et al. Diode probes for spatiotemporal optical control of multiple neurons in freely moving animals. J. Neurophysiol. 2012, 108:349-363.
6. Berenyi A., et al. Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. J. Neurophysiol. 2014, 111:1132-1149.
7. Harvey C.D., et al. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 2012, 484:62-68.
8. Scott B.B., et al. Cellular resolution functional imaging in behaving rats using voluntary head restraint. Neuron 2013, 80:371-384.
9. Kriegeskorte N., Kievit R.A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 2013, 17:401-412.
10. Logothetis N.K., et al. Hippocampal-cortical interaction during periods of subcortical silence. Nature 2012, 491:547-553.
11. Einevoll G.T., et al. Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat. Rev. Neurosci. 2013, 14:770-780.
12. Cunningham J., Yu B.M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 2014, 15:1752-1758.
13. Churchland M.M., et al. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Curr. Opin. Neurobiol. 2007, 17:609-618.
14. Haxby J.V., et al. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 2014, 37:435-456.
15. Quian Quiroga R., Panzeri S. Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 2009, 10:173-185.
16. Shamir M. Emerging principles of population coding: in search for the neural code. Curr. Opin. Neurobiol. 2014, 25:140-148.
17. Shadlen M.N., Newsome W.T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 1998, 18:3870-3896.
18. Rothschild G., et al. Functional organization and population dynamics in the mouse primary auditory cortex. Nat. Neurosci. 2010, 13:353-360.
19. Martin K.A., Schroder S. Functional heterogeneity in neighboring neurons of cat primary visual cortex in response to both artificial and natural stimuli. J. Neurosci. 2013, 33:7325-7344.
20. Quiroga R.Q., et al. Decoding visual inputs from multiple neurons in the human temporal lobe. J. Neurophysiol. 2007, 98:1997-2007.
21. Rolls E.T., et al. The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Exp. Brain Res. 1997, 114:149-162.
22. Ince R.A., et al. Neural codes formed by small and temporally precise populations in auditory cortex. J. Neurosci. 2013, 33:18277-18287.
23. Bathellier B., et al. Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron 2012, 76:435-449.
24. Shenoy K.V., et al. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 2013, 36:337-359.
25. Barth A.L., Poulet J.F. Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 2012, 35:345-355.
26. Houweling A.R., Brecht M. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 2008, 451:65-68.
27. Segev I., London M. Untangling dendrites with quantitative models. Science 2000, 290:744-750.
28. Branco T., Hausser M. Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron 2011, 69:885-892.
29. Garcia-Lazaro J.A., et al. Independent population coding of speech with sub-millisecond precision. J. Neurosci. 2013, 33:19362-19372.
30. Centanni T.M., et al. Detection and identification of speech sounds using cortical activity patterns. Neuroscience 2014, 258:292-306.
31. Engineer C.T., et al. Cortical activity patterns predict speech discrimination ability. Nat. Neurosci. 2008, 11:603-608.
32. Zuo Y., et al. Complementary contributions of spike timing and spike rate to perceptual decisions in rat S1 and S2 Cortex. Curr. Biol. 2015, 25:357-363.
33. Kiani R., et al. Differences in onset latency of macaque inferotemporal neural responses to primate and non-primate faces. J. Neurophysiol. 2005, 94:1587-1596.
34. Eskandar E.N., Assad J.A. Dissociation of visual, motor and predictive signals in parietal cortex during visual guidance. Nat. Neurosci. 1999, 2:88-93.
35. Rigotti M., et al. The importance of mixed selectivity in complex cognitive tasks. Nature 2013, 497:585-590.
36. Rigotti M., et al. Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses. Front. Comput. Neurosci. 2010, 4:24.
37. Barak O., et al. The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off. J. Neurosci. 2013, 33:3844-3856.
38. Kriegeskorte N. Relating population-code representations between man, monkey, and computational models. Front. Neurosci. 2009, 3:363-373.
39. Cohen M.R., Kohn A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 2011, 14:811-819.
40. Panzeri S., et al. Correlations and the encoding of information in the nervous system. Proc. Biol. Sci. 1999, 266:1001-1012.
41. Brette R. Computing with neural synchrony. PLoS Comput. Biol. 2012, 8:e1002561.
42. Singer W. Neuronal synchrony: a versatile code for the definition of relations?. Neuron 1999, 24:49-65. 111-125.
43. Zohary E., et al. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 1994, 370:140-143.
44. Ecker A.S., et al. Decorrelated neuronal firing in cortical microcircuits. Science 2010, 327:584-587.
45. Renart A., et al. The asynchronous state in cortical circuits. Science 2010, 327:587-590.
46. Ecker A.S., et al. The effect of noise correlations in populations of diversely tuned neurons. J. Neurosci. 2011, 31:14272-14283.
47. Shamir M., Sompolinsky H. Implications of neuronal diversity on population coding. Neural Comput. 2006, 18:1951-1986.
48. Haefner R.M., et al. Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nat. Neurosci. 2013, 16:235-242.
49. Moreno-Bote R., et al. Information-limiting correlations. Nat. Neurosci. 2014, 17:1410-1417.
50. Abbott L.F., Dayan P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 1999, 11:91-101.
51. Schneidman E., et al. Synergy, redundancy, and independence in population codes. J. Neurosci. 2003, 23:11539-11553.
52. Salinas E., Sejnowski T.J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2001, 2:539-550.
53. Berkes P., et al. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 2011, 331:83-87.
54. Fiser J., et al. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 2010, 14:119-130.
55. Haefner R.M., Fiser J. Good noise or bad noise: the role of correlated variability in a probabilistic inference framework. Computational and Systems Neuroscience (Cosyne) Abstracts 2014, I39 2014.
56. Ohiorhenuan I.E., et al. Sparse coding and high-order correlations in fine-scale cortical networks. Nature 2010, 466:617-621.
57. Froudarakis E., et al. Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nat. Neurosci. 2014, 17:851-857.
58. Macke J.H., et al. Common input explains higher-order correlations and entropy in a simple model of neural population activity. Phys. Rev. Lett. 2011, 106:208102.
59. Shriki O., et al. Fast coding of orientation in primary visual cortex. PLoS Comput. Biol. 2012, 8:e1002536.
60. Panzeri S., et al. Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 2010, 33:111-120.
61. Gollisch T., Meister M. Rapid neural coding in the retina with relative spike latencies. Science 2008, 319:1108-1111.
62. VanRullen R., et al. Spike times make sense. Trends Neurosci. 2005, 28:1-4.
63. Gutig R., et al. Computing complex visual features with retinal spike times. PLoS ONE 2013, 8:e53063.
64. Brasselet R., et al. Neurons with stereotyped and rapid responses provide a reference frame for relative temporal coding in primate auditory cortex. J. Neurosci. 2012, 32:2998-3008.
65. Luczak A., et al. Gating of sensory input by spontaneous cortical activity. J. Neurosci. 2013, 33:1684-1695.
66. Havenith M.N., et al. Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead. J. Neurosci. 2011, 31:8570-8584.
67. Schoonover C.E., et al. Comparative strength and dendritic organization of thalamocortical and corticocortical synapses onto excitatory layer 4 neurons. J. Neurosci. 2014, 34:6746-6758.
68. Kelly S.T., et al. The role of thalamic population synchrony in the emergence of cortical feature selectivity. PLoS Comput. Biol. 2014, 10:e1003418.
69. Nikolic D., et al. Gamma oscillations: precise temporal coordination without a metronome. Trends Cogn. Sci. 2013, 17:54-55.
70. Akam T., Kullmann D.M. Oscillatory multiplexing of population codes for selective communication in the mammalian brain. Nat. Rev. Neurosci. 2014, 15:111-122.
71. Buonomano D.V., Maass W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 2009, 10:113-125.
72. Renart A., Machens C.K. Variability in neural activity and behavior. Curr. Opin. Neurobiol. 2014, 25:211-220.
73. Douglas R.J., Martin K.A. Mapping the matrix: the ways of neocortex. Neuron 2007, 56:226-238.
74. Harris K.D., Thiele A. Cortical state and attention. Nat. Rev. Neurosci. 2011, 12:509-523.
75. Lee S.H., Dan Y. Neuromodulation of brain states. Neuron 2012, 76:209-222.
76. Goard M., Dan Y. Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci. 2009, 12:1444-1449.
77. Pinto L., et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat. Neurosci. 2013, 16:1857-1863.
78. Curto C., et al. A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex. J. Neurosci. 2009, 29:10600-10612.
79. Macke, J.H. et al. (2011) Empirical models of spiking in neural populations. In In Advances in neural information processing systems, pp. 1350-1358.
80. Ecker A.S., et al. State dependence of noise correlations in macaque primary visual cortex. Neuron 2014, 82:235-248.
81. Marder E. Neuromodulation of neuronal circuits: back to the future. Neuron 2012, 76:1-11.
82. King J.R., Dehaene S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 2014, 18:203-210.
83. Isik L., et al. The dynamics of invariant object recognition in the human visual system. J. Neurophysiol. 2014, 111:91-102.
84. Gross J. Analytical methods and experimental approaches for electrophysiological studies of brain oscillations. J. Neurosci. Methods 2014, 228:57-66.
85. Majima K., et al. Decoding visual object categories from temporal correlations of ECoG signals. Neuroimage 2013, 90C:74-83.
86. Mesgarani N., et al. Phonetic feature encoding in human superior temporal gyrus. Science 2014, 343:1006-1010.
87. Pasley B.N., et al. Reconstructing speech from human auditory cortex. PLoS Biol 2012, 10:e1001251.
88. Cichy R.M., et al. Resolving human object recognition in space and time. Nat. Neurosci. 2014, 17:455-462.
89. Ramkumar P., et al. Feature-specific information processing precedes concerted activation in human visual cortex. J. Neurosci. 2013, 33:7691-7699.
90. Zion Golumbic E., et al. Visual input enhances selective speech envelope tracking in auditory cortex at a 'cocktail party'. J. Neurosci. 2013, 33:1417-1426.
91. Ng B.S.W., et al. EEG phase patterns reflect the selectivity of neural firing. Cereb. Cortex 2013, 23:389-398.
92. Gross J., et al. Speech rhythms and multiplexed oscillatory sensory coding in the human brain. PLoS Biol. 2013, 11:e1001752.
93. Howard M.F., Poeppel D. Discrimination of speech stimuli based on neuronal response phase patterns depends on acoustics but not comprehension. J. Neurophysiol. 2010, 104:2500-2511.
94. Schyns P.G., et al. Cracking the code of oscillatory activity. PLoS Biol. 2011, 9:e1001064.
95. Lopour B.A., et al. Coding of information in the phase of local field potentials within human medial temporal lobe. Neuron 2013, 79:594-606.
96. Belitski A., et al. Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands. J. Comput. Neurosci. 2010, 29:533-545.
97. Ding N., Simon J.Z. Adaptive temporal encoding leads to a background-insensitive cortical representation of speech. J. Neurosci. 2013, 33:5728-5735.
98. Weisz N., et al. Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception. Proc. Natl. Acad. Sci. U.S.A. 2014, 111:E417-E425.
99. Liebe S., et al. Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance. Nat. Neurosci. 2012, 15:456-462. S451-452.
100. Canolty R.T., Knight R.T. The functional role of cross-frequency coupling. Trends Cogn. Sci. 2010, 14:506-515.
101. Kayser C., et al. Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron 2009, 61:597-608.
102. Buzsaki G., et al. The origin of extracellular fields and currents - EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 2012, 13:407-420.
103. Lakatos P., et al. The leading sense: supramodal control of neurophysiological context by attention. Neuron 2009, 64:419-430.
104. Montemurro M.A., et al. Phase-of-firing coding of natural visual stimuli in primary visual cortex. Curr. Biol. 2008, 18:375-380.
105. Rey H.G., et al. Timing of single-neuron and local field potential responses in the human medial temporal lobe. Curr. Biol. 2014, 24:299-304.
106. Canolty R.T., et al. Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:17356-17361.
107. Kerr J.N., Denk W. Imaging in vivo: watching the brain in action. Nat. Rev. Neurosci. 2008, 9:195-205.
108. Lopes da Silva F. EEG and MEG: relevance to neuroscience. Neuron 2013, 80:1112-1128.
109. Schneidman E., et al. Synergy from silence in a combinatorial neural code. J. Neurosci. 2011, 31:15732-15741.
110. He B.J. Scale-free brain activity: past, present, and future. Trends Cogn. Sci. 2014, 18:480-487.
111. Whittingstall K., Logothetis N.K. Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex. Neuron 2009, 64:281-289.
112. Lakatos P., et al. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J. Neurophysiol. 2005, 94:1904-1911.
113. Thut G., et al. The functional importance of rhythmic activity in the brain. Curr. Biol. 2012, 22:R658-R663.
114. Ng B.S., et al. A precluding but not ensuring role of entrained low-frequency oscillations for auditory perception. J. Neurosci. 2012, 32:12268-12276.
115. Jensen O., et al. Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing. Trends Neurosci. 2014, 37:357-369.
116. Ding N., et al. Robust cortical entrainment to the speech envelope relies on the spectro-temporal fine structure. Neuroimage 2013, 88C:41-46.
117. Mazzoni A., et al. Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Comput. Biol. 2008, 4:e1000239.
118. Fries P., et al. The gamma cycle. Trends Neurosci. 2007, 30:309-316.
119. Brunel N., Wang X.J. What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. J. Neurophysiol. 2003, 90:415-430.