Modeling Self-Organization in the Visual Cortex

Miikkulainen, Risto; Bednar, James A.; Choe, Yoonsuck; Sirosh, Joseph

doi:10.1016/b978-044450270-4/50019-x

Cited by 37 publications

(73 citation statements)

References 15 publications

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“…For instance, is "less" required from development because we have built an overlycomplicated model that is simply too powerful or underconstrained? Consider what is known about the structure we built in to the model (as described in Section 4.1): cortical maps are well documented and change over development (e.g., Kohonen, 1982;Miikkulainen, Bednar, Choe & Sirosh, 1999); the layered structure of cortex is evident prenatally (e.g., Rakic, 1995); receptive fields become tuned with experience (e.g., Clark, Allard, Jenkins & Merzenich, 1988). In each of these cases, the structure we built into our model is consistent with studies of both prenatal and postnatal development and is grounded in a neural principles.…”

Section: Insights Into Development and Challenges On The Horizonmentioning

confidence: 99%

“…Put concretely, the parameter changes reported in Table A1 were all implemented "by hand" for the present simulations. It will be important in future efforts to probe whether such changes can emerge from, for instance, Hebbian processes involved in the tuning of cortical maps (Bednar & Miikkulainen, 2007;Kohonen, 1982;Miikkulainen, et al, 1999). We have taken steps in this direction by implementing a reference frame alignment and calibration process in the "adult" model .…”

Section: Insights Into Development and Challenges On The Horizonmentioning

confidence: 99%

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Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Simmering¹,

Schutte²,

Spencer³

2008

Brain Research

115

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Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks-the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity-generating novel, testable predictions-and generality-spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

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Section: Insights Into Development and Challenges On The Horizonmentioning

confidence: 99%

Section: Insights Into Development and Challenges On The Horizonmentioning

confidence: 99%

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Simmering¹,

Schutte²,

Spencer³

2008

Brain Research

115

View full text Add to dashboard Cite

show abstract

“…Furthermore, recurrent (artificial) neural network models are able to exhibit rich temporal dynamics [4], [5], [6]. Thus, time becomes an essential factor in neural network operation, whether it is natural or artificial (also see [7], [8], [9], [10]). …”

Section: Introductionmentioning

confidence: 99%

Evolution of recollection and prediction in neural networks

Chung

Kwon

Choe

2009

2009 International Joint Conference on Neural Networks

Self Cite

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Abstract-A large number of neural network models are based on a feedforward topology (perceptrons, backpropagation networks, radial basis functions, support vector machines, etc.), thus lacking dynamics. In such networks, the order of input presentation is meaningless (i.e., it does not affect the behavior) since the behavior is largely reactive. That is, such neural networks can only operate in the present, having no access to the past or the future. However, biological neural networks are mostly constructed with a recurrent topology, and recurrent (artificial) neural network models are able to exhibit rich temporal dynamics, thus time becomes an essential factor in their operation. In this paper, we will investigate the emergence of recollection and prediction in evolving neural networks. First, we will show how reactive, feedforward networks can evolve a memory-like function (recollection) through utilizing external markers dropped and detected in the environment. Second, we will investigate how recurrent networks with more predictable internal state trajectory can emerge as an eventual winner in evolutionary struggle when competing networks with less predictable trajectory show the same level of behavioral performance. We expect our results to help us better understand the evolutionary origin of recollection and prediction in neuronal networks, and better appreciate the role of time in brain function.

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“…The low magnification analysis will determine if a given region is an invasive area in a similar manner as a pathologist would do when evaluating a biopsy. In order to mimic this behaviour, we exploit the relationship between human vision and neuroscience [14].…”

Section: Low Resolution Analysis and Sparse Codingmentioning

confidence: 99%

Time-efficient sparse analysis of histopathological whole slide images

Huang

Veillard

Roux

et al. 2011

Computerized Medical Imaging and Graphics

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Histopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.

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Modeling Self-Organization in the Visual Cortex

Cited by 37 publications

References 15 publications

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Evolution of recollection and prediction in neural networks

Time-efficient sparse analysis of histopathological whole slide images

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