The Receptive Field is Dead. Long Live the Receptive Field?

Overview

Journal Curr Opin Neurobiol

Specialties Biology
Neurology

Date 2014 Mar 13

PMID 24618227

Citations 11

Authors

Adrienne Fairhall

Affiliations

Soon will be listed here.

Abstract

Advances in experimental techniques, including behavioral paradigms using rich stimuli under closed loop conditions and the interfacing of neural systems with external inputs and outputs, reveal complex dynamics in the neural code and require a revisiting of standard concepts of representation. High-throughput recording and imaging methods along with the ability to observe and control neuronal subpopulations allow increasingly detailed access to the neural circuitry that subserves neural representations and the computations they support. How do we harness theory to build biologically grounded models of complex neural function?

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References

Harris K, Mrsic-Flogel T . Cortical connectivity and sensory coding. Nature. 2013; 503(7474):51-8. DOI: 10.1038/nature12654. View

Bargmann C, Marder E . From the connectome to brain function. Nat Methods. 2013; 10(6):483-90. DOI: 10.1038/nmeth.2451. View

Shenoy K, Sahani M, Churchland M . Cortical control of arm movements: a dynamical systems perspective. Annu Rev Neurosci. 2013; 36:337-59. DOI: 10.1146/annurev-neuro-062111-150509. View

Olshausen B, Field D . Sparse coding of sensory inputs. Curr Opin Neurobiol. 2004; 14(4):481-7. DOI: 10.1016/j.conb.2004.07.007. View

Keller G, Bonhoeffer T, Hubener M . Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron. 2012; 74(5):809-15. DOI: 10.1016/j.neuron.2012.03.040. View

Brunel N, Hakim V, Richardson M . Single neuron dynamics and computation. Curr Opin Neurobiol. 2014; 25:149-55. DOI: 10.1016/j.conb.2014.01.005. View

Mease R, Famulare M, Gjorgjieva J, Moody W, Fairhall A . Emergence of adaptive computation by single neurons in the developing cortex. J Neurosci. 2013; 33(30):12154-70. PMC: 3721832. DOI: 10.1523/JNEUROSCI.3263-12.2013. View

Elyada Y, Haag J, Borst A . Different receptive fields in axons and dendrites underlie robust coding in motion-sensitive neurons. Nat Neurosci. 2009; 12(3):327-32. DOI: 10.1038/nn.2269. View

Roy J, Riesenhuber M, Poggio T, Miller E . Prefrontal cortex activity during flexible categorization. J Neurosci. 2010; 30(25):8519-28. PMC: 3709835. DOI: 10.1523/JNEUROSCI.4837-09.2010. View

10.

Hedrick K, Cox S . Structure-preserving model reduction of passive and quasi-active neurons. J Comput Neurosci. 2012; 34(1):1-26. DOI: 10.1007/s10827-012-0403-y. View

11.

Adibi M, McDonald J, Clifford C, Arabzadeh E . Adaptation improves neural coding efficiency despite increasing correlations in variability. J Neurosci. 2013; 33(5):2108-20. PMC: 6619115. DOI: 10.1523/JNEUROSCI.3449-12.2013. View

12.

Hong S, Aguera Y Arcas B, Fairhall A . Single neuron computation: from dynamical system to feature detector. Neural Comput. 2007; 19(12):3133-72. DOI: 10.1162/neco.2007.19.12.3133. View

13.

Sussillo D . Neural circuits as computational dynamical systems. Curr Opin Neurobiol. 2014; 25:156-63. DOI: 10.1016/j.conb.2014.01.008. View

14.

Cox D . Do we understand high-level vision?. Curr Opin Neurobiol. 2014; 25:187-93. DOI: 10.1016/j.conb.2014.01.016. View

15.

Helmstaedter M, Briggman K, Turaga S, Jain V, Seung H, Denk W . Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature. 2013; 500(7461):168-74. DOI: 10.1038/nature12346. View

16.

Fee M . The role of efference copy in striatal learning. Curr Opin Neurobiol. 2014; 25:194-200. PMC: 4153469. DOI: 10.1016/j.conb.2014.01.012. View

17.

Wander J, Rao R . Brain-computer interfaces: a powerful tool for scientific inquiry. Curr Opin Neurobiol. 2014; 25:70-5. PMC: 3980496. DOI: 10.1016/j.conb.2013.11.013. View

18.

Sharpee T . Computational identification of receptive fields. Annu Rev Neurosci. 2013; 36:103-20. PMC: 3760488. DOI: 10.1146/annurev-neuro-062012-170253. View

19.

Kastner D, Baccus S . Insights from the retina into the diverse and general computations of adaptation, detection, and prediction. Curr Opin Neurobiol. 2014; 25:63-9. DOI: 10.1016/j.conb.2013.11.012. View

20.

Licata A, Kaufman M, Raposo D, Ryan M, Sheppard J, Churchland A . Posterior Parietal Cortex Guides Visual Decisions in Rats. J Neurosci. 2017; 37(19):4954-4966. PMC: 5426183. DOI: 10.1523/JNEUROSCI.0105-17.2017. View