Show Reference: "Hebbian mechanisms help explain development of multisensory integration in the superior colliculus: a neural network model"

Hebbian mechanisms help explain development of multisensory integration in the superior colliculus: a neural network model. Biological cybernetics, Vol. 106, No. 11-12. (4 December 2012), pp. 691-713, doi:10.1007/s00422-012-0511-9 by Cristiano Cuppini, Elisa Magosso, Benjamin A. Rowland, Barry E. Stein, Mauro Ursino
    abstract = {
                The superior colliculus ({SC}) integrates relevant sensory information (visual, auditory, somatosensory) from several cortical and subcortical structures, to program orientation responses to external events. However, this capacity is not present at birth, and it is acquired only through interactions with cross-modal events during maturation. Mathematical models provide a quantitative framework, valuable in helping to clarify the specific neural mechanisms underlying the maturation of the multisensory integration in the {SC}. We extended a neural network model of the adult {SC} (Cuppini et al., Front Integr Neurosci 4:1-15, 2010) to describe the development of this phenomenon starting from an immature state, based on known or suspected anatomy and physiology, in which: (1) {AES} afferents are present but weak, (2) Responses are driven from {non-AES} afferents, and (3) The visual inputs have a marginal spatial tuning. Sensory experience was modeled by repeatedly presenting modality-specific and cross-modal stimuli. Synapses in the network were modified by simple Hebbian learning rules. As a consequence of this exposure, (1) Receptive fields shrink and come into spatial register, and (2) {SC} neurons gained the adult characteristic integrative properties: enhancement, depression, and inverse effectiveness. Importantly, the unique architecture of the model guided the development so that integration became dependent on the relationship between the cortical input and the {SC}. Manipulations of the statistics of the experience during the development changed the integrative profiles of the neurons, and results matched well with the results of physiological studies.
    author = {Cuppini, Cristiano and Magosso, Elisa and Rowland, Benjamin A. and Stein, Barry E. and Ursino, Mauro},
    day = {4},
    doi = {10.1007/s00422-012-0511-9},
    issn = {1432-0770},
    journal = {Biological cybernetics},
    keywords = {development, enhancement, learning, model, sc, suppression},
    month = dec,
    number = {11-12},
    pages = {691--713},
    pmcid = {PMC3552306},
    pmid = {23011260},
    posted-at = {2012-10-05 12:35:55},
    priority = {2},
    publisher = {Springer Berlin / Heidelberg},
    title = {Hebbian mechanisms help explain development of multisensory integration in the superior colliculus: a neural network model.},
    url = {},
    volume = {106},
    year = {2012}

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Cuppini et al. present a model of the SC that exhibits many of the properties regarding neural connectivity, electrophysiology, and development that have been found experimentally in nature.

The model of the SC due to Cuppini et al. reproduces development of

  1. multi-sensory neurons
  2. multi-sensory enhancement
  3. intra-modality depression
  4. super-additivity
  5. inverse effectiveness

The model due to Cuppini et al. comprises distinct neural populations for

  1. anterior ectosylvian sulcus (AES) and auditory subregion of AES (FAES)
  2. inhibitory interneurons between AES/FAES and SC
  3. space-coded ascending inputs (visual, auditory) to the SC
  4. inhibitory ascending interneurons
  5. (potentially) multi-sensory SC neurons.

The model due to Cuppini et al. does not need neural multiplication to implement superadditivity or inverse effectiveness. Instead, it exploits the sigmoid transfer function in multi-sensory neurons: due to this sigmoid transfer function and due to less-than-unit weights between input and multi-sensory neurons, weak stimuli that fall into the low linear regions of input neurons evoke less than linear responses in multi-sensory neurons. However, the sum of two such stimuli (from different modalities) can be in their linear range and thus the result can be much greater than the sum of the individual responses.

Through lateral connections, a Hebbian learning rule, and approximate initialization, Cuppini et al. manage to learn register between sensory maps. This can be seen as an implementation of a SOM.

Cuppini et al. use mutually inhibitive, modality-specific inhibition (inhibitory inter-neurons that get input from one modality and inhibit inhibitory interneurons receiving input from different modalities) to implement a winner-take-all mechanism between modalities; this leads to a visual (or auditory) capture effect without functional multi-sensory integration.

Their network model builds upon their earlier single-neuron model.

Not sure about the biological motivation of this. Also: it would be interesting to know if functional integration still occurs.

Cuppini et al. do not evaluate their model's performance (comparability to cat/human performance, optimality...)

The model due to Cuppini et al. is inspired only by observed neurophysiology; it has no normative inspiration.

The SC model presented by Cuppini et al. has a circular topology to prevent the border effect.

Without an intact association cortex (or LIP), SC neurons cannot develop or maintain cross-modal integration.

(Neither multi-sensory enhancement nor depression.)

There is no depression in the immature SC.

Cuppini et al. model SC receptive fields (actually: spatial tuning curves) as continuous functions of the distance of a stimulus from the center of the RF.

In Anastasio et al.'s work, receptive fields are binary: either a stimulus is in the field or it isn't; if the average response of an SC neuron is smaller for stimuli that are further away from the center of the RF is smaller, then that's because inference there is less effective.

Cuppini et al. expand on their earlier work in modeling cortico-tectal multi-sensory integration.

They present a model which shows how receptive fields and multi-sensory integration can arise through experience.