Hypercolumns - Sensation and Perception - Lecture Slides, Slides of Brain and Cognitive Science

Download Hypercolumns - Sensation and Perception - Lecture Slides and more Slides Brain and Cognitive Science in PDF only on Docsity! •1 9/16/2008 1 Orientation Columns combine with Ocular Dominance Columns: Hypercolumns! 9/16/2008 2 Docsity.com •2 9/16/2008 3 Orientation Tuning Function of a Cortical Cell 9/16/2008 4 System Overview: How cortical simple cells get their orientation tuning Docsity.com •5 9/16/2008 9 Orientation 9/16/2008 10 Spatial Frequency Docsity.com •6 9/16/2008 11 • Adaptation experiments provide strong evidence that orientation and spatial frequency are coded by neurons somewhere in the human visual system – Cats, Monkeys: Striate cortex, not in retina or LGN – Humans operate the same way as cats and monkeys with respect to selective adaptation Selective Adaptation 9/16/2008 12 Selective Adaptation • Spatial frequency channels • Why would the visual system use spatial frequency filters to analyze images? – Different spatial frequencies emphasize different types of information Docsity.com •7 9/16/2008 13 Frequency Components of an Image 9/16/2008 14 Defining and Separating Different Brain Areas • Brain areas can be differentiated according to 4 main criteria: – Function (physiology) • Neurons in different parts of the brain are responsive to different aspects of the stimulus (= do different things). – Architecture • microanatomy can differ widely across brain areas • For example, V1 is also referred to as "striate cortex" because it has a series of stripes that run parallel to the surface; these stripes end abruptly at the end of V1. – Connections • different areas feed forward and also receive backward- reaching connections from distinct areas. – Topography (e.g., retinotopy) • Each distinct visual area has its own retinotopic map. Remember 'FACT' as a mnemonic Docsity.com > The Human Visual System Dorset Seam Second level of visual ascociation cortex in Parietal lobe Strate cortx (primary visual Dorsal lateral gericuate nucleus Thaamus, ee opie nove. Inferior tomer ‘orto Sarondiaal of Visual associaton cortex Ventral Steam Ten » Responses to Objects and Location Left hemisphere Right hemisphere. [@ Ferm and locaton @ Lccation Source: Adapted trom Haxby,J.V., Horwitz, B., Underbeider, |.G, Maisog, JM, Petrini., and Grady, CL. Joumna of Neuroscience, 1894, 1, 6336-8353. Ten «10 Docsity.com visual world ¥. AN / right temporal retina right nasal retina Ten left hemifield right hemifield optic nerve Meyer's loop optic tract — optic radiations occipital poles ll Docsity.com •12 9/16/2008 23 1. 2. 3. 9/16/2008 24 Meyer’s Loop: Since the lens inverts images, the lower half of the retina sees the upper half of world, and vice versa Longer loop stems from lower half of retina and dives down into temporal lobe Docsity.com •15 9/16/2008 29 Early models of recognition • Template-matching. – Early models of recognition were based on matching the stimulus pattern to a set of stored, pre-defined 'templates'. These models can be expanded by incorporating some pre-processing; that is, rotating the stimulus so that it is upright, scaling it to be of a standard size, etc. • Feature analysis. – Instead of looking for a match to a standard shape, feature analysis emphasizes that what makes an 'A' an A is the letter's unique set of defining features. 9/16/2008 30 Docsity.com •16 9/16/2008 31 Failures of matching-models • There are several key weaknesses of these early approaches to recognition: – 3 dimensions. The template-matching and feature analysis models were designed mostly to recognize alphanumeric characters. However, most real-world human recognition occurs on 3D objects, not 2D symbols. – Superficial differences. It's hard for these models to 'extract' the basic similarity of repeated instances of an item while also representing superficial differences (consider handwriting as an example). 9/16/2008 32 Failures of matching-models • There are several key weaknesses of these early approaches to recognition: – 3 dimensions. The template-matching and feature analysis models were designed mostly to recognize alphanumeric characters. However, most real-world human recognition occurs on 3D objects, not 2D symbols. – Superficial differences. It's hard for these models to 'extract' the basic similarity of repeated instances of an item while also representing superficial differences (consider handwriting as an example). Docsity.com •17 9/16/2008 33 Failures of matching-models • There are several key weaknesses of these early approaches to recognition: – Where to start? These models don't have a clear means for breaking up complex input into a series of objects- to-be-identified; instead, they seem to rely on being 'fed' items (e.g., letters) to recognize. That seems utterly unlike human perception. 9/16/2008 34 Building blocks of recognition? • In order to simulate human recognition in a complex 3D world, more recent models of object recognition have relied on defining constraints (or "primitives") and basic strategies that the visual system might use. • Marr and Nishihara present a model of recognition, restricted to the set of objects that can be described as generalized cones-- objects with a clear main axis and a constant-shape cross section. Docsity.com