Publish in OALib Journal
ISSN: 2333-9721
APC: Only $99

Paper Submission

Views	Downloads

Relative Articles
Visual Working Memory in Human Cortex
Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory
Visual cortex combines a stimulus and an error-like signal with a proportion that is dependent on time, space, and stimulus contrast
How the Visual Cortex Handles Stimulus Noise: Insights from Amblyopia
The Right Frontopolar Cortex Is Involved in Visual-Spatial Prospective Memory
Increased Amygdala and Visual Cortex Activity and Functional Connectivity towards Stimulus Novelty Is Associated with State Anxiety
Negative BOLD fMRI Response in the Visual Cortex Carries Precise Stimulus-Specific Information
Stimulus-Entrained Oscillatory Activity Propagates as Waves from Area 18 to 17 in Cat Visual Cortex
Stimulus Dependency of Object-Evoked Responses in Human Visual Cortex: An Inverse Problem for Category Specificity
More...

PLOS Biology 2009

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

DOI: 10.1371/journal.pbio.1000260

Danko Nikoli？ equal contributor ,Stefan H？usler equal contributor,Wolf Singer,Wolfgang Maass

Full-Text Cite this paper Add to My Lib

Abstract:

It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤~20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs.

References

[1]	Hubel D. H, Wiesel T. N (1962) Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol 160: 106–154.
[2]	Britten K. H, Newsome W. T, Shadlen M. N, Celebrini S, Movshon J. A (1996) A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci 13: 87–100.
[3]	McAdams C. J, Maunsell J. H. R (1999) Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J Neurosci 19: 431–441.
[4]	Biederlack J, Castelo-Branco M, Neuenschwander S, Wheeler D. W, Singer W, et al. (2006) Brightness induction: rate enhancement and neuronal synchronization as complementary codes. Neuron 52: 1073–1083.
[5]	Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24: 49–65.
[6]	Samonds J. M, Allison J. D, Brown H. A, Bonds A. B (2004) Cooperative synchronized assemblies enhance orientation discrimination. Proc Natl Acad Sci U S A 101: 6722–6727.
[7]	Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cog Sci 9: 474–480.
[8]	Van Rullen R, Thorpe S. J (2001) Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex? Neural Comput 13: 1255–1283.
[9]	Nikoli？ D (2007) Non-parametric detection of temporal order across pairwise measurements of time delays. J Comput Neurosci 22: 5–19.
[10]	Fries P, Nikoli？ D, Singer W (2007) The gamma cycle. Trends Neurosc 30: 309–316.
[11]	Grün S, Diesmann M, Aertsen A (2002) Unitary events in multiple singleneuron activity. I. Detection and significance. Neural Comp 14: 43–80.
[12]	Fries P, Roelfsema P. R, Engel A. K, Konig P, Singer W (1997) Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry. Proc Natl Acad Sci U S A 94: 12699–12704.
[13]	Johnson D. H, Gruner C. M, Baggerly K, Seshagiri C (2001) Information-theoretic analysis of neural coding. J Comput Neurosci 10: 47–69.
[14]	Pipa G, Wheeler D. W, Singer W, Nikoli？ D (2008) NeuroXidence: a non-parametric test on excess or deficiency of joint-spike events. J Comput Neurosci, 25: 64–88.
[15]	Schneider G, Nikoli？ D (2006) Detection and assessment of near-zero delays in neuronal spiking activity. J Neurosci Methods 152: 97–106.
[16]	Schneider G, Havenith M. N, Nikoli？ D (2006) Spatiotemporal structure in large neuronal networks detected from cross-correlation. Neural Comput 18: 2387–2413.
[17]	Optican L. M, Richmond B. J (1987) Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. J Neurophysiol 57: 162–178.
[18]	Tovee M. J, Rolls E. T, Treves A, Bellis R. P (1993) Information encoding and the responses of single neurons in the primate temporal visual-cortex. J Neurophysiol 70: 640–654.
[19]	Bialek W, Rieke F, de Ruyter van Steveninck R. R, Warland D (1991) Reading a neural code. Science 252: 1854–1857.
[20]	Bialek W, Rieke F (1992) Reliability and information transmission in spiking neurons. Trends Neurosci 15: 428–434.
[21]	Heller J, Hertz J. A, Kj？r T. W, Richmond B. J (1995) Information flow and temporal coding in primate pattern vision. J Comput Neurosci 2: 175–193.
[22]	Meyers E. M, Freedman D. J, Kreiman G. K, Miller E. K, Poggio T (2008) Dynamic population coding of category information in inferior temporal and prefrontal cortex. J Neurophysiol 100: 1407–1419.
[23]	Hung C. P, Kreiman G, Poggio T, DiCarlo J. J (2005) Fast readout of object identity from macaque inferior temporal cortex. Science 310: 863–866.
[24]	Averbeck B. B, Latham P. E, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7: 358–366.
[25]	Sperling G (1960) The information available in brief visual presentations. Psychol Monogr 74: 1–29.
[26]	Phillips W. A, Baddeley A. D (1971) Reaction time and short-term visual memory. Psychon Sci 22: 73–74.
[27]	Phillips W. A (1974) On the distinction between sensory storage and short-term visual memory. Percept Psychophys 16: 283–290.
[28]	di Lollo V (1977) Temporal characteristics of iconic memory. Nature 267: 241–243.
[29]	Smithson H, Mollon J (2006) Do masks terminate the icon? The Quarterly Journal of Experimental Psychology 59: 150–160.
[30]	Buonomano D. V, Merzenich M. M (1995) Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267: 1028–1030.
[31]	Maass W, Natschl？ger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14: 2531–2560.
[32]	J？ger H (2002) Short term memory in echo state networks. GMD Report 152, German National Research Center for Information Technology. Available at: http://www.faculty.iu-bremen.de/hjaeger/？pubs/STMEchoStatesTechRep.pdf Accessed: January 1, 2006.
[33]	Wyss R, K？nig P, Verschure P. F (2003) Invariant representations of visual patterns in a temporal population code. Proc Natl Acad Sci U S A 100: 324–329.
[34]	White O. L, Lee D. D, Sompolinsky H (2004) Short-term memory in orthogonal neural networks. Phys Rev Lett 92: 148102.
[35]	J？ger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304: 78–80.
[36]	Riesenhuber M, Poggio T (2000) Models of object recognition. Nature Neurosci 3: 1199–1204.
[37]	Serre T, Kouh M, Cadieu C, Knoblich U, Kreiman G, et al. (2005) A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex. Cambridge (Massachusetts): Massachusetts Institutes of Technology. AI Memo 2005-036 (CBCL Memo 259).
[38]	Spruston N, Johnston D (1992) Perforated patch-clamp analysis of the passive membrane properties of three classes of hippocampal neurons. J Neurophysiol 67: 508–529.
[39]	Kasper E. M, Larkman A. U, Lübke J, Blakemore C (1994) Pyramidal neurons in layer 5 of the rat visual cortex. II. Development of electrophysiological properties. J Comp Neurol 339: 475–494.
[40]	Fries P, Neuenschwander S, Engel A. K, Goebel R, Singer W (2001) Rapid feature selective neuronal synchronization through correlated latency shifting. Nat Neurosci 4: 194–200.
[41]	Orban G. A (1984) Neuronal operations in the visual cortex. Berlin (Germany): Springer-Verlag. 367 p.
[42]	Uttal W. R (1969) Masking of alphabetic character recognition by dynamic visual noise (DVN). Percept Psychophys 6: 121–128.
[43]	Gerstein G. L, Perkel D. H (1972) Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. Biophys J 12: 453–473.
[44]	Tallon-Baudry C, Bertrand O, Delpuech C, Pernier J (1997) Oscillatory γ-band (30–70 Hz) activity induced by a visual search task in humans. J Neurosc 17: 722–734.
[45]	Schneidman E, Berry M. J, Segev R, Bialek W (2006) Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440: 1007–1012.
[46]	Yu S, Huang D, Singer W, Nikoli？ D (2008) A small world of neuronal synchrony. Cereb Cortex 18: 2891–2901.
[47]	Rager G, Singer W (1998) The response of cat visual cortex to flicker stimuli of variable frequency. Eur J Neurosci 10: 1856–77.
[48]	Softky W. R, Koch C (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J Neurosci 13: 334–350.
[49]	Tomko G. J, Crapper D. R (1974) Neuronal variability: non-stationary responses to identical visual stimuli. Brain Res 79: 405–418.
[50]	Shadlen M. N, Newsome W. T (1994) Noise, neural codes and cortical organization. Curr Opin Neurobiol 4: 569–579.
[51]	Shadlen M. N, Newsome W. T (1995) Is there a signal in the noise? Curr Opin Neurobiol 5: 248–250.
[52]	Shadlen M. N, Newsome W. T (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18: 3870–3896.
[53]	Carandini M (2004) Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: e264. doi:10.1371/journal.pbio.0020264.
[54]	K？nig P, Engel A. K, Singer W (1996) Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci 19: 130–137.
[55]	Zucker R. S, Regehr W. G (2002) Short-term synaptic plasticity. Annu Rev Physiol 64: 355–405.
[56]	Kennedy M. B, Bennett M. K, Bulleit R. F, Erondu N. E, Jennings V. R, et al. (1990) Structure and regulation of type II calcium/calmodulin-dependent protein kinase in central nervous system neurons. Cold Spring Harb Symp Quant Biol 55: 101–110.
[57]	Markram R, Segal M (1992) The inositol 1,4,5-triphospate pathway mediates cholinergic potentiation of rat hippocampal neuronal responses to NMDA. J Physiol 447: 513–533.
[58]	Kohn A (2007) Visual adaptation: physiology, mechanisms and functional benefits. J Neurophysiol 97: 3155–3164.
[59]	Bisley J. W, Zaksas D, Droll J. A, Paternak T (2004) Activity of neurons in cortical area MT during a memory for motion task. J Neurophysiol 91: 286–300.
[60]	Fuster J. M, Jervey J. P (1982) Neuronal firing in the inferotemporal cortex of the monkey in a visual memory task. J Neurosci 2: 361–375.
[61]	Wheeler M. E, Treisman A. M (2002) Binding in short-term visual memory. J Exp Psych Gen 131: 48–64.
[62]	Natschl？ger T, Maass W (2005) Dynamics of information and emergent computation in generic neural microcircuit models. Neural Netw 18: 1301–1308.
[63]	Olshausen B. A, Field D. J (2006) What is the other 85% of V1 doing? In: Sejnowski T. J, van Hemmen L, editors. Problems in systems neuroscience. Oxford (United Kingdom): Oxford University Press. pp. 182–211.
[64]	Lewicki M (1998) A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9: R53–R78.
[65]	Fano U (1947) Ionization yield of radiations. II. The fluctuations of the number of ions. Phys Rev 72: 26–29.
[66]	Baddeley R, Abbott L. F, Booth M. C. A, Sengpiel F, Freeman T, et al. (1997) Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc Biol Sci 264: 1775–1783.
[67]	Chih-Chung C, Chih-Jen L (2001) LIBSVM: a library for support vector machines. Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed January 1, 2006.
[68]	Vapnik V. N (1998) Statistical learning theory. New York (New York): Wiley. 736 p.
[69]	Sch？lkopf B, Smola A. J (2002) Learning with kernels. Cambridge (Massachusetts): MIT Press. 644 p.

Full-Text