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Extrinsic and intrinsic representations

Published online by Cambridge University Press:  28 November 2019

Sidney R. Lehky Open the ORCID record for Sidney R. Lehky [Opens in a new window]  and
Anne B. Sereno
Sidney R. Lehky
Affiliation:
Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA92037sidney@salk.edu
Anne B. Sereno
Affiliation:
Department of Psychological Sciences, Purdue University, West Lafayette, IN47907 School of Biomedical Engineering, Purdue University, West Lafayette, IN47907. asereno@purdue.eduhttps://engineering.purdue.edu/SerenoLab
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Abstract

We extend the discussion in the target article about distinctions between extrinsic coding (external references to known things, as required by information theory) and the alternative we and the target article both favor, intrinsic coding (internal relationships within sensory and motor signals). Central to our thinking about intrinsic coding is population coding and the concept of high-dimensional neural response spaces.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e235
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Copyright
Copyright © Cambridge University Press 2019

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References

Baldassi, C., Alemi-Neissi, A., Pagan, M., Dicarlo, J. J., Zecchina, R. & Zoccolan, D. (2013) Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLoS Computational Biology 9:e1003167.CrossRefGoogle Scholar
Eifuku, S., De Souza, W. C., Tamura, R., Nishijo, H. & Ono, T. (2004) Neuronal correlates of face identification in the monkey anterior temporal cortical areas. Journal of Neurophysiology 91:358–71.CrossRefGoogle ScholarPubMed
Jazayeri, M. & Movshon, J. A. (2006) Optimal representation of sensory information by neural populations. Nature Neuroscience 9(5):690–96.CrossRefGoogle ScholarPubMed
Kayaert, G., Biederman, I. & Vogels, R. (2005) Representation of regular and irregular shapes in macaque inferotemporal cortex. Cerebral Cortex 15:1308–21.CrossRefGoogle ScholarPubMed
Kiani, R., Esteky, H., Mirpour, K. & Tanaka, K. (2007) Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. Journal of Neurophysiology 97:4296–309.CrossRefGoogle ScholarPubMed
Lehky, S. R. & Sereno, A. B. (2007) Comparison of shape encoding in primate dorsal and ventral visual pathways. Journal of Neurophysiology 97:307–19.CrossRefGoogle ScholarPubMed
Lehky, S. R. & Sereno, A. B. (2011) Population coding of visual space: Modeling. Frontiers in Computational Neuroscience 4:155. doi: 10.3389/fncom.2010.00155.CrossRefGoogle ScholarPubMed
Lehky, S. R., Sereno, M. E. & Sereno, A. B. (2013) Population coding and the labeling problem: extrinsic versus intrinsic representations Neural Computation 25:2235–64.CrossRefGoogle ScholarPubMed
Murata, A., Gallese, V., Luppino, G., Kaseda, M. & Sakata, H. (2000) Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP. Journal of Neurophysiology 83:2580–601.CrossRefGoogle ScholarPubMed
Op de Beeck, H., Wagemans, J. & Vogels, R. (2001) Inferotemporal neurons represent low-dimensional configurations of parameterized shapes. Nature Neuroscience 4:1244–52.CrossRefGoogle ScholarPubMed
Pouget, A., Dayan, P. & Zemel, R. S. (2003) Inference and computation with population codes. Annual Review of Neuroscience 26:381–410.CrossRefGoogle ScholarPubMed
Quian Quiroga, R. & Panzeri, S. (2009) Extracting information from neuronal populations: Information theory and decoding approaches. Nature Reviews Neuroscience 10:173–85.CrossRefGoogle ScholarPubMed
Rolls, E. T. & Tovée, M. J. (1995) Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Neurophysiology 73:713–26.CrossRefGoogle ScholarPubMed
Sereno, A. B. & Lehky, S. R. (2011) Population coding of visual space: Comparison of spatial representations in dorsal and ventral pathways. Frontiers in Computational Neuroscience 4:159. doi:10.3389/fncom.2010.00159.CrossRefGoogle ScholarPubMed
Sereno, A. B. & Lehky, S. R. (2018) Attention effects on neural population representations for shape and location are stronger in the ventral than dorsal stream. eNeuro 5:e0371-0317.2018.CrossRefGoogle ScholarPubMed
Sereno, A. B., Sereno, M. E. & Lehky, S. R. (2014) Recovering stimulus locations using populations of eye-position modulated neurons in dorsal and ventral visual streams of non-human primates. Frontiers in Integrative Neuroscience 8:28. doi:10.3389/fnint.2014.00028.CrossRefGoogle ScholarPubMed
Young, M. P. & Yamane, S. (1992) Sparse population coding of faces in the inferotemporal cortex. Science 256:1327–31.CrossRefGoogle ScholarPubMed
Sereno, A. B., Lehky, S. R. & Sereno, M.E. (2019) Representation of shape, space, and attention in monkey cortex. [Epub ahead of print] Cortex. doi:10.1016/j.cortex.2019.06.005.CrossRefGoogle ScholarPubMed