Computational Maps in the Visual Cortex

By Risto Miikkulainen, Jim Bednar, Yoonsuck Choe, Joseph Sirosh. 2005

263 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

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Citations in the corpus

A self-organizing neural network architecture for learning human-object interactions (2017, background): “process of topology preservation is motivated by similar neural mechanisms found in multiple cortical areas of the brainx [15].”
Self-organizing neural integration of pose-motion features for human action recognition (2015, background): “The visual system is composed of topographically arranged structures that organize according to environmental stimuli (Hubel and Wiesel, 1962; von der Malsburg, 1973; Hubel and Wiesel, 1977; Miikkulainen et al., 2005).”
Incorporating Prior Knowledge on Features into Learning (2007, methodology): “This assumption is supported by the existence of organization maps in the cortex, e.g. in the organization of the primary visual cortex the meta-features can be position, orientation and scale (see e.g., Miikkulainen et al., 2005).”
Compete to Compute (2013, background): “The functional properties of competitive interactions have been further studied to show, among other things, the effects of distance-dependent kernels [8], inhibitory time lags [8, 9], development of self-organizing maps [10, 11, 12], and the role of WTA networks in attention [13].”
Making machine learning models interpretable (2012, background): “The cognitive processing of visual stimuli [22, 23] includes an element of natural subjectivity, that the analyst must aim to control as much as possible [6].”
Autonomous Evolution of Topographic Regularities in Artificial Neural Networks (2010, background): “Perhaps one reason for this lack of engagement is that, while considerable research in neural computation has focused on modelling aspects of real brains (Andrade et al., 2001; Bednar et al., 2002; Miikkulainen and Dyer, 1989; Miikkulainen et al., 2005; Swindale, 1996), NE algorithms generally do no”
Building a mechanistic model of the development and function of the primary visual cortex. (2012, unknown): “LISSOM/GCAL neurons each participate in multiple functional maps, but have only a single set of lateral connections.”
Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex (2013, methodology): “…network models for map development (von der Malsburg, 1973; Kohonen, 1982; Obermayer et al., 1990; Sirosh and Miikkulainen, 1994; Barrow et al., 1996; Burger and Lang, 1999, 2001; Bednar and Miikkulainen, 2004; Miikkulainen et al., 2005), while omitting mechanisms that destroy stability.”