'Receptive field' presentation slideshows

Unit 2, Week 1

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Nerves, Taste, Touch

Nerves, Taste, Touch. BIOL241 Last lecture. Taste. Tastants taste receptor cells taste buds five primary taste sensations Properties of the taste system Na + , H + , Ca ++ 80% Smell. Taste Bud & Receptors. Connective tissue. Gustatory hair. Taste fibers of cranial nerve.

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Lecture # 22

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SENSORY NERVOUS SYSTEM

SENSORY NERVOUS SYSTEM . Dr. Ayisha Qureshi Assistant Professor MBBS, Mphil. Stimulus & Modalities. A stimulus is a change detectable by the body. Stimuli exist in a variety of energy forms, or modalities, such as heat, light, sound, pressure, and chemical changes .

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Biologically Inspired Intelligent Systems

Biologically Inspired Intelligent Systems. Lecture 5 Dr. Roger S. Gaborski. Class Presentations. Simple r eceptive field Matlab script. %Dog128Filter.m %Generates difference of Gaussians 128x128 imageSize = 128; sigmaex = .095* imageSize ; sigmainh = .19* imageSize ;

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Image Classification

Image Classification. Convolutional networks - Why. Convolutions Reduce parameters Capture shift-invariance: location of patch in image should not matter Subsampling Allows greater invariance to deformations Allows the capture of large patterns with small filters.

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Somatosensory System Touch - mechanoreception

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Somatosensation

Somatosensation. Mechanoreceptors that respond to touch/pressure on the surface of the body. Sensory nerve responds propotional to pressure 4 types of mechanoreceptors: Meissner corpuscles & Merkel discs - cutaneous light touch with high resolution.

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Dr W Kolbinger, Sensory Systems (2009)

Sensory Systems. Picture: Rene Descartes (1596-1650). 1. Dr W Kolbinger, Sensory Systems (2009). Lecture Outline. Common Plan of Sensory Systems Four Sensory Receptor Classes Three Basic Processes in a Sensory Receptor Encoding of Four Stimulus Attributes

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Week 6 Perception

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Photoreceptors and Retina

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Consciousness and Attention

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Michael Arbib: CS564 - Brain Theory and Artificial Intelligence University of Southern California, Fall 2001

Michael Arbib: CS564 - Brain Theory and Artificial Intelligence University of Southern California, Fall 2001. Lecture 22. Saccades 2 Reading Assignments: Reprint

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Basics of Computational Neuroscience

Basics of Computational Neuroscience. The Interdisciplinary Nature of Computational Neuroscience. What is computational neuroscience ?. Lecture: Computational Neuroscience, Contents. 1. Computational neuroscience and the perspective of scientists versus that of behaving agents

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How the Brain Learns: Rules and Outcomes

How the Brain Learns: Rules and Outcomes. Psychology 209 January 28, 2013. The brain is highly plastic and changes in response to experience . Alteration of experience leads to alterations of neural representations in the brain.

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Midterm 1

Midterm 1. Oct. 21 in class. Read this article by Wednesday next week!. Visual Pathways. Lateral Geniculate Nucleus maintains segregation: of M and P cells (mango and parvo) of left and right eyes. P cells project to layers 3 - 6 M cells project to layers 1 and 2. Visual Pathways.

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Visual Pathways

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CSC 578 Neural Networks and Deep Learning

CSC 578 Neural Networks and Deep Learning. 6-1. Convolutional Neural Networks (1) (Some figures adapted from NNDL book ). Convolution Neural Networks. Convolutional Neural Networks Convolution, pooling and fully-connected layers Convolution kernel/filter Local receptive field

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Sensory Systems

Sensory Systems. Lecture 12. Sensory Information. Detection of changes in environment external or internal 4 main functions perception control of movement regulation of body function maintenance of arousal Not all conscious experience ~. Sensation vs. Perception . Sensation

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How do we perceive elongated patch?

How do we perceive elongated patch?. Probability summation. Physiological summation. Differences. Probability summation: independent of the configuration of the stimuli Physiological summation: may depends on the configuration of the stimuli. STIMULI. Collinear vs Orthogonal .

229 views • 12 slides