A Nonlinear Model of Spatiotemporal Retinal Processing PDF Download
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Author: Paolo Gaudiano
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 30
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Author: Paolo Gaudiano
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 30
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Author: Matthias Wulf
Publisher: IOS Press
ISBN: 9783898382540
Category : Computational neuroscience
Languages : en
Pages : 356
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Author: Paolo Gaudiano
Publisher:
ISBN:
Category : Retina
Languages : en
Pages : 11
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 188
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Book Description
We applied two first-order, linear, time-varying, differential equations to the task of segmenting motion from sequences of images. The equations are modified Grossberg formulas for long-term and short-term memory models characterizing the neurotransmitter and cell-activity levels of a synapse and neuron. We described how a two layered, sensory, neural network can be built using the equations to simulate the amacrine neurons of the retina. The model is defined using adaptive input nodes (adaptive model) and is compared to a similar model without these nodes (O and G model). By replicating the basic amacrine neuron model to form both one- and two-dimensional arrays, we created a novel method for processing images over time and space. To simulate the veto effect observed in shunt inhibitory synaptic junctions, we applied a nonrecurrent, asynchronous, inhibitory region in the receptive field of our amacrine neural model. We show how this effects the performance of the model ill one dimension. In two dimensions we investigate the models' response to synthesized imagery (pristine) and to real, forward looking infrared radar (FLIR) images. The output of our models are further processed through two types of moving-average filters - causal and noncausal.
Author: Masumi Ishikawa
Publisher: Springer
ISBN: 3540691588
Category : Computers
Languages : en
Pages : 1165
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Book Description
These two-volume books comprise the post-conference proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007) held in Kitakyushu, Japan, during November 13–16, 2007. The Asia Paci?c Neural Network Assembly (APNNA) was founded in 1993. The ?rst ICONIP was held in 1994 in Seoul, Korea, sponsored by APNNA in collaboration with regional organizations. Since then, ICONIP has consistently provided prestigious opp- tunities for presenting and exchanging ideas on neural networks and related ?elds. Research ?elds covered by ICONIP have now expanded to include such ?elds as bioinformatics, brain machine interfaces, robotics, and computational intelligence. We had 288 ordinary paper submissions and 3 special organized session p- posals. Although the quality of submitted papers on the average was excepti- ally high, only 60% of them were accepted after rigorous reviews, each paper being reviewed by three reviewers. Concerning special organized session prop- als, two out of three were accepted. In addition to ordinary submitted papers, we invited 15 special organized sessions organized by leading researchers in emerging ?elds to promote future expansion of neural information processing. ICONIP 2007 was held at the newly established Kitakyushu Science and Research Park in Kitakyushu, Japan. Its theme was “Towards an Integrated Approach to the Brain—Brain-Inspired Engineering and Brain Science,” which emphasizes the need for cross-disciplinary approaches for understanding brain functions and utilizing the knowledge for contributions to the society. It was jointly sponsored by APNNA, Japanese Neural Network Society (JNNS), and the 21st century COE program at Kyushu Institute of Technology.
Author: J. Leo van Hemmen
Publisher: Springer Science & Business Media
ISBN: 0387217037
Category : Computers
Languages : en
Pages : 424
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Book Description
This volume, with chapters by leading researchers in the field, is devoted to early vision and attention, that is, to the first stages of visual information processing. This state-of-the-art look at biological neural networks spans the many subfields, such as computational and experimental neuroscience; anatomy and physiology; visual information processing and scene segmentation; perception at illusory contours; control of visual attention; and paradigms for computing with spiking neurons.
Author: Amol Gharat
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"Our visual system is sensitive to boundaries defined by differences in cues such as luminance (first-order cue), as well as texture, contrast, or motion (second-order cues). Gradients in these cues can be utilized to perform tasks such as figure-ground segregation and 3D shape perception. A significant fraction of neurons in the early visual cortex of cats and monkeys have been shown to be selective to both first- and second-order boundaries. These neurons are thought to be the neural correlate for perceptual encoding of such boundaries. They are selective for the same boundary orientation irrespective of the cue (first- or second-order) that defines it ("form cue-invariance"), which makes these neurons powerful candidates for the task of segmentation. However, the neural circuitry that gives rise to this selectivity for the early stages of visual processing remains unclear. To address this question, I perform neurophysiological recordings at the early stages of the visual pathway in cats, and then build biologically inspired neural circuit models that can account for visual response properties of neurons at subcortical as well as early cortical stages. In Chapter 2, I use multi-electrode recordings to demonstrate the presence of a significant fraction of neurons in cat Area 18 with nonlinear receptive fields like those of subcortical Y-type cells. These neurons have receptive field properties intermediate between subcortical Y cells and cortical orientation selective cue-invariant neurons. These are strong candidates for building cue-invariant orientation-selective neurons. Furthermore I present a novel neural circuit model that pools such Y-like neurons in an unbalanced "push-pull" manner, to generate orientation-selective cue-invariant receptive fields.In Chapter 3, I estimate biologically constrained neural network models of cat LGN receptive fields using recent machine learning methods (deep learning). The receptive fields are modeled as arising from a two-stage convolutional neural network model. The first stage, corresponding to retinal bipolar cell subunits, is modeled as a convolutional filter layer, and the second stage is modeled as a pooling layer. These two layers are separated by an intermediate parametric nonlinearity. I train such a neural network model for each recorded LGN neuron, using its spiking responses to naturalistic texture stimuli. These models are not only better in comparison to the standard linear-nonlinear models at predicting response to arbitrary stimuli, but they also recover biologically interpretable subunit models.In chapter 4, I evaluate the integration of ON- and OFF-pathway inputs by individual neurons in early cortical areas of the cat (Area 17 and Area 18). In this study, I model receptive fields of cortical simple cells as a linear weighted sum of rectified inputs from model ON- and OFF-center LGN afferents, with the weights estimated using a regression framework. The estimated models reveal significant asymmetries in spatiotemporal integration of ON and OFF signals within simple cell receptive fields. These observed asymmetries could provide the neural mechanism for generating cue-invariant receptive fields from Y-pathway inputs.In summary, I put together our knowledge of retinal as well as early cortical processing to show how spatial nonlinearities emerging from the retina could provide an essential basis for cortical visual processing. I further evaluate these neural mechanisms by estimating single neuron receptive field models, using modern system identification methods. Finally I propose, and provide supportive evidence for, a novel neural circuit mechanism that could explain the cue-invariant processing of luminance- and texture-defined boundaries through a common pathway." --
Author:
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 812
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Author: Giedrius T. Buračas
Publisher: IOS Press
ISBN: 9781586031817
Category :
Languages : en
Pages : 352
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Author: Max J. Freeberg
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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