Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Journal of Neuroscience
  • Log in
  • My Cart
Journal of Neuroscience

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE
PreviousNext
Journal Club

Cortical Selectivity through Random Connectivity

Joe Corey and Benjamin Scholl
Journal of Neuroscience 25 July 2012, 32 (30) 10103-10104; DOI: https://doi.org/10.1523/JNEUROSCI.2463-12.2012
Joe Corey
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Benjamin Scholl
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

In visual cortex (V1), neurons are highly organized for many different stimulus properties including orientation selectivity, retinotopy, and ocular dominance. Since the discovery of functional architecture in monkeys (Powell and Mountcastle, 1959) and the detailed examination of orientation columns in cat (Hubel and Wiesel, 1962), it has been thought that V1 organization is essential for extracting sensory information and generating highly selective responses in neurons. Surprisingly, however, this functional architecture has not been found in some visual animals, particularly rodents. In the visual cortex of rats, mice, and the gray squirrel there is no evidence for orientation maps or cortical columns, although there still exists highly orientation-selective neurons (Ohki and Reid, 2007). Instead, extracellular recordings and two-photon calcium imaging have revealed a salt-and-pepper organization of orientation selectivity (Ohki and Reid, 2007).

Visual cortex organization appears to diverge among mammals, with primates and carnivores possessing a regular columnar architecture, while rodents show no obvious structure, despite all having neurons strongly selective for oriented visual stimuli. This species difference raises questions about the role of columnar organization in information processing and sensory computation. For example, does the emergence of orientation selectivity require organized cortical connectivity or are random connections sufficient? Hansel and van Vreeswijk (2012) sought to determine whether robust orientation tuning in animals without columnar organization can arise merely from random connectivity. To address this issue, the authors constructed a network model of visual cortex and demonstrated that strong orientation selectivity can emerge from random connectivity (Fig. 1).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Schematic of network model from Hansel and Vreeswijk (2012). Orientation selectivity in layer 4 is distributed randomly in a salt-and-pepper fashion. Selectivity in layer 2/3 emerges through random connections (gray lines). Fine-scale connections (red lines) are not necessary for emergent tuning, but might enhance selectivity in tandem.

Envisioning cortical cell layers as two-dimensional sheets, the authors defined connection probability between neurons as a function of cortical distance and not similarity in stimulus preference. This neural network has a feedforward design, such that synaptic inputs from one neuron sheet (layer 4) converge onto neurons in a second (layer 2/3; Fig. 1). Since neurons in layer 4 have a salt-and-pepper organization of orientation selectivity, neurons in layer 2/3 receive a random mixture of inputs (Fig. 1). There is also recurrent input in layer 2/3, with connection probabilities defined only as a function of cortical distance and not functional similarity. Using a realistic conductance-based cell model and a network comprised of both excitatory and inhibitory neurons, Hansel and van Vreeswijk (2012) demonstrated that a cortical network without any functional structure, either between neuron layers or within a layer, can still generate orientation selectivity (Hansel and van Vreeswijk, 2012, their Fig. 4).

This remarkable finding can be explained by considering the orientation tuning of individual inputs. Since orientation tuning of synaptic inputs onto layer 2/3 are random, the ensemble tuning is reduced through simple averaging as the number of inputs increase. This creates a very large untuned component and a relatively nonexistent tuned component. Because this network is populated by both excitatory and inhibitory neurons operating in a balanced activity regime, untuned excitatory and inhibitory inputs roughly cancel each other (Hansel and van Vreeswijk, 2012, their Fig. 2). This cancellation causes the remaining tuned components to dominate responses and render neurons strongly orientation selective. Interestingly, Hansel and van Vreeswijk (2012) found that the emergence of orientation selectivity is largely independent to the degree of orientation selectivity in the input layer. In fact, in agreement with empirical evidence showing that stimulus orientation and spatial frequency preference is roughly equivalent across laminar layers (Niell and Stryker, 2008), differences in the degree of selectivity between layer 4 and layer 2/3 were either small or nonexistent (Hansel and van Vreeswijk, 2012, their Table 2). They also show that abolishing tuning of layer 4 inhibitory neurons does not drastically affect the emergence of orientation selectivity, which is important considering that inhibitory neurons are more broadly tuned than excitatory neurons in mouse visual cortex (Kerlin et al., 2010). These findings show that orientation selectivity can be generated through random synaptic inputs, with local connection probabilities determined by cortical distance, suggesting a nonspecific synaptic mechanism which rodents might use to extract visual stimulus features.

Although their model exhibits aspects of rodent visual cortex, Hansel and van Vreeswijk (2012) do not make any claims about this being the sole mechanism in generating orientation selectivity. Random connections might be sufficient, but this does not imply that feature-selective functional connectivity is absent in animals with “salt-and-pepper” organization. It is possible that the orientation selectivity arising from random connections is further strengthened by precise fine-scale local connectivity and that these two systems work in tandem (Fig. 1). For example, it has been shown that there exists fine-scale organization in intracortical connections between neighboring neurons in mouse visual cortex, where nearby neurons with similar orientation preference have a higher probability of being connected to one another (Ko et al., 2011). Given this knowledge, Hansel and van Vreeswijk (2012) could modify their recurrent network component and weight connection probabilities between neurons by both cortical distance and stimulus preference. For example, increasing the connection probability of nearby neurons with similar orientation preference might further enhance their selectivity (Fig. 1).

It should be mentioned that functional connectivity arising purely from random connections is unlikely when considering synaptic plasticity. During spike timing-dependent plasticity, for example, synaptic connections between neurons of similar orientation preference can be strengthened by repeated stimulus presentations. A synergistic resolution to random versus specific connectivity might be found when considering experience-dependent plasticity during development. Before the critical period, mice are exposed to relatively little visual stimulation, yet orientation selectivity exists (Wang et al., 2010). Random connectivity may initially provide a scaffolding for orientation tuning, which is improved upon during development. This suggests a developmental progression whereby random connections generate selectivity that is later shaped by learning mechanisms in the form of feature-selective plasticity.

These authors have shown that robust orientation tuning in animals without a functional map can arise merely from random connectivity, dislodging the notion that functional organization of neuron response properties is required to generate orientation selectivity. In addition, this emergent selectivity was largely independent of the input orientation selectivity and constraints placed on inhibitory neurons. In the future, it will be important to establish the role of random synaptic connections in generating stimulus feature selectivities, particularly during development, whereby these types of synaptic mechanisms could create the framework of cortical organization and a basis for strengthening selectivity through experience-dependent plasticity.

Footnotes

  • Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see http://www.jneurosci.org/misc/ifa_features.shtml.

  • We are grateful to Nicholas Priebe, Andrew Tan, and David Hansel for helpful discussions.

  • Correspondence should be addressed to Benjamin Scholl, Institute of Neuroscience, The University of Texas at Austin, 2400 Speedway, Austin, TX 78712. scholl.ben{at}gmail.com

References

  1. ↵
    1. Hansel D,
    2. van Vreeswijk C
    (2012) The mechanism of orientation selectivity in primary visual cortex without a functional map. J Neurosci 32:4049–4064.
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Hubel DH,
    2. Wiesel TN
    (1962) Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol Lond 160:106–154.
    OpenUrlFREE Full Text
  3. ↵
    1. Kerlin AM,
    2. Andermann ML,
    3. Berezovskii VK,
    4. Reid RC
    (2010) Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67:858–871.
    OpenUrlCrossRefPubMed
  4. ↵
    1. Ko H,
    2. Hofer SB,
    3. Pichler B,
    4. Buchanan KA,
    5. Sjöström PJ,
    6. Mrsic-Flogel TD
    (2011) Functional specificity of local synaptic connections in neocortical networks. Nature 473:87–91.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Niell CM,
    2. Stryker MP
    (2008) Highly selective receptive fields in mouse visual cortex. J Neurosci 28:7520–7536.
    OpenUrlAbstract/FREE Full Text
  6. ↵
    1. Ohki K,
    2. Reid RC
    (2007) Specificity and randomness in the visual cortex. Curr Opin Neurobiol 17:401–407.
    OpenUrlCrossRefPubMed
  7. ↵
    1. Powell TP,
    2. Mountcastle VB
    (1959) Some aspects of the functional organization of the cortex of the postcentral gyrus of the monkey: a correlation of findings obtained in a single unit analysis with cytoarchitecture. Bull Johns Hopkins Hosp 105:133–162.
    OpenUrlPubMed
  8. ↵
    1. Wang BS,
    2. Sarnaik R,
    3. Cang J
    (2010) Critical period matches binocular orientation preference in the visual Cortex. Neuron 65:246–256.
    OpenUrlCrossRefPubMed
Back to top

In this issue

The Journal of Neuroscience: 32 (30)
Journal of Neuroscience
Vol. 32, Issue 30
25 Jul 2012
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Advertising (PDF)
  • Ed Board (PDF)
Email

Thank you for sharing this Journal of Neuroscience article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Cortical Selectivity through Random Connectivity
(Your Name) has forwarded a page to you from Journal of Neuroscience
(Your Name) thought you would be interested in this article in Journal of Neuroscience.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
Cortical Selectivity through Random Connectivity
Joe Corey, Benjamin Scholl
Journal of Neuroscience 25 July 2012, 32 (30) 10103-10104; DOI: 10.1523/JNEUROSCI.2463-12.2012

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Request Permissions
Share
Cortical Selectivity through Random Connectivity
Joe Corey, Benjamin Scholl
Journal of Neuroscience 25 July 2012, 32 (30) 10103-10104; DOI: 10.1523/JNEUROSCI.2463-12.2012
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

  • Toward an Interpersonal Neuroscience in Technologically Assisted (Virtual) Interactions
  • Complex Interactions between Distinct Theta Oscillatory Patterns during Sleep Deprivation
  • Motion Processing and Categorical Decisions in Medial Superior Temporal and Lateral Intraparietal Areas
Show more Journal Club
  • Home
  • Alerts
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Issue Archive
  • Collections

Information

  • For Authors
  • For Advertisers
  • For the Media
  • For Subscribers

About

  • About the Journal
  • Editorial Board
  • Privacy Policy
  • Contact
(JNeurosci logo)
(SfN logo)

Copyright © 2023 by the Society for Neuroscience.
JNeurosci Online ISSN: 1529-2401

The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. Publication of an advertisement or other product mention in JNeurosci should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci.