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An activity-dependent computational model of development of the retinotopic map along the dorsoventral axis in the primary visual cortex

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(A) Schematic representation of the LISSOM architecture. (B) V1 retinotopic map developed after training, superimposed with the complex logarithmic map for verification. (C) V1 map development at 200, 400, 600, 750 iterations. (D) Input (Retina) & Output (V1) pairs, post training; for 90°, 45°, 0° bars given as input.
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Figure 1: (A) Schematic representation of the LISSOM architecture. (B) V1 retinotopic map developed after training, superimposed with the complex logarithmic map for verification. (C) V1 map development at 200, 400, 600, 750 iterations. (D) Input (Retina) & Output (V1) pairs, post training; for 90°, 45°, 0° bars given as input.

Mentions: Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes [1]: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Such a map has been approximated by a complex log map [1]. While the development of the map along the nasotemporal axis is controlled by a combination of EphA-ephrin-A signaling as well as spontaneous retinal waves; the mechanisms involved in the map formation along the dorsoventral axis are still unknown [2]. Neural models with correlational learning have successfully explained other visual maps like orientation maps and ocular-dominance maps. No such network models of retinotopic map development exist. In this paper we propose an activity based model which simulates the large-scale development of the retinotopic map along the dorsoventral axis. The architecture consists of a LISSOM (Laterally Interconnected Synergetically Self Organizing Map) [3] with 2 layers; representing the retina, and the V1 respectively (see Figure 1A). At each time step, each neuron in V1, combines the afferent activation (ζr1,r2) along with its lateral excitations and inhibitions (ηkl) from the previous time step.


An activity-dependent computational model of development of the retinotopic map along the dorsoventral axis in the primary visual cortex
(A) Schematic representation of the LISSOM architecture. (B) V1 retinotopic map developed after training, superimposed with the complex logarithmic map for verification. (C) V1 map development at 200, 400, 600, 750 iterations. (D) Input (Retina) & Output (V1) pairs, post training; for 90°, 45°, 0° bars given as input.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4126376&req=5

Figure 1: (A) Schematic representation of the LISSOM architecture. (B) V1 retinotopic map developed after training, superimposed with the complex logarithmic map for verification. (C) V1 map development at 200, 400, 600, 750 iterations. (D) Input (Retina) & Output (V1) pairs, post training; for 90°, 45°, 0° bars given as input.
Mentions: Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes [1]: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Such a map has been approximated by a complex log map [1]. While the development of the map along the nasotemporal axis is controlled by a combination of EphA-ephrin-A signaling as well as spontaneous retinal waves; the mechanisms involved in the map formation along the dorsoventral axis are still unknown [2]. Neural models with correlational learning have successfully explained other visual maps like orientation maps and ocular-dominance maps. No such network models of retinotopic map development exist. In this paper we propose an activity based model which simulates the large-scale development of the retinotopic map along the dorsoventral axis. The architecture consists of a LISSOM (Laterally Interconnected Synergetically Self Organizing Map) [3] with 2 layers; representing the retina, and the V1 respectively (see Figure 1A). At each time step, each neuron in V1, combines the afferent activation (ζr1,r2) along with its lateral excitations and inhibitions (ηkl) from the previous time step.

View Article: PubMed Central - HTML

No MeSH data available.