Literature DB >> 12063126

Multimodality in the superior colliculus: an information theoretic analysis.

Paul Patton1, Kamel Belkacem-Boussaid, Thomas J Anastasio.   

Abstract

The deep superior colliculus (DSC) integrates multisensory input and triggers an orienting movement toward the source of stimulation (target). It would seem reasonable to suppose that input of an additional modality should always increase the amount of information received by a DSC neuron concerning a target. However, of all DSC neurons studied, only about one half in the cat and one-quarter in the monkey were multimodal. The rest received only unimodal input. Multimodal DSC neurons show the properties of multisensory enhancement, in which the neural response to an input of one modality is augmented by input of another modality, and of inverse effectiveness, in which weaker unimodal responses produce a higher percentage enhancement. Previously, we demonstrated that these properties are consistent with the hypothesis that DSC neurons use Bayes' rule to compute the posterior probability that a target is present given their stochastic sensory inputs. Here we use an information theoretic analysis of our Bayesian model to show that input of an additional modality may indeed increase target information, but only if input received from the initial modality does not completely reduce uncertainty concerning the presence of a target. Unimodal DSC neurons may be those whose unimodal input fully reduces target uncertainty and therefore have no need for input of another modality.

Mesh:

Year:  2002        PMID: 12063126     DOI: 10.1016/s0926-6410(02)00057-5

Source DB:  PubMed          Journal:  Brain Res Cogn Brain Res        ISSN: 0926-6410


  9 in total

1.  Why aren't all deep superior colliculus neurons multisensory? A Bayes' ratio analysis.

Authors:  Hans Colonius; Adele Diederich
Journal:  Cogn Affect Behav Neurosci       Date:  2004-09       Impact factor: 3.282

2.  Visual speech speeds up the neural processing of auditory speech.

Authors:  Virginie van Wassenhove; Ken W Grant; David Poeppel
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-12       Impact factor: 11.205

3.  Multisensory integration in the superior colliculus: a neural network model.

Authors:  Mauro Ursino; Cristiano Cuppini; Elisa Magosso; Andrea Serino; Giuseppe di Pellegrino
Journal:  J Comput Neurosci       Date:  2008-05-14       Impact factor: 1.621

4.  Hebbian mechanisms help explain development of multisensory integration in the superior colliculus: a neural network model.

Authors:  C Cuppini; E Magosso; B Rowland; B Stein; M Ursino
Journal:  Biol Cybern       Date:  2012-08-04       Impact factor: 2.086

5.  Multisensory integration in the superior colliculus requires synergy among corticocollicular inputs.

Authors:  Juan Carlos Alvarado; Terrence R Stanford; Benjamin A Rowland; J William Vaughan; Barry E Stein
Journal:  J Neurosci       Date:  2009-05-20       Impact factor: 6.167

Review 6.  Challenges in quantifying multisensory integration: alternative criteria, models, and inverse effectiveness.

Authors:  Barry E Stein; Terrence R Stanford; Ramnarayan Ramachandran; Thomas J Perrault; Benjamin A Rowland
Journal:  Exp Brain Res       Date:  2009-06-24       Impact factor: 1.972

7.  Organization, maturation, and plasticity of multisensory integration: insights from computational modeling studies.

Authors:  Cristiano Cuppini; Elisa Magosso; Mauro Ursino
Journal:  Front Psychol       Date:  2011-05-02

8.  Modeling multisensory enhancement with self-organizing maps.

Authors:  Jacob G Martin; M Alex Meredith; Khurshid Ahmad
Journal:  Front Comput Neurosci       Date:  2009-06-24       Impact factor: 2.380

Review 9.  Sensory perception: lessons from synesthesia: using synesthesia to inform the understanding of sensory perception.

Authors:  Joshua Paul Harvey
Journal:  Yale J Biol Med       Date:  2013-06-13
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.