* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Document
Mirror neuron wikipedia , lookup
Embodied language processing wikipedia , lookup
Eyeblink conditioning wikipedia , lookup
Affective neuroscience wikipedia , lookup
Binding problem wikipedia , lookup
Haemodynamic response wikipedia , lookup
Neuropsychology wikipedia , lookup
Neurophilosophy wikipedia , lookup
Environmental enrichment wikipedia , lookup
Brain Rules wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Cognitive neuroscience wikipedia , lookup
Neurolinguistics wikipedia , lookup
Functional magnetic resonance imaging wikipedia , lookup
Development of the nervous system wikipedia , lookup
Cognitive neuroscience of music wikipedia , lookup
History of neuroimaging wikipedia , lookup
Emotional lateralization wikipedia , lookup
Neural coding wikipedia , lookup
Cortical cooling wikipedia , lookup
Stimulus (physiology) wikipedia , lookup
Nervous system network models wikipedia , lookup
Premovement neuronal activity wikipedia , lookup
Neuroanatomy of memory wikipedia , lookup
Activity-dependent plasticity wikipedia , lookup
Optogenetics wikipedia , lookup
Human brain wikipedia , lookup
Neuroanatomy wikipedia , lookup
Aging brain wikipedia , lookup
Clinical neurochemistry wikipedia , lookup
Synaptic gating wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Neuroplasticity wikipedia , lookup
Metastability in the brain wikipedia , lookup
Neuroesthetics wikipedia , lookup
Neuroeconomics wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Time perception wikipedia , lookup
Efficient coding hypothesis wikipedia , lookup
Neural correlates of consciousness wikipedia , lookup
Cerebral cortex wikipedia , lookup
Chapter 4: The Organized Brain Overview of Questions • How can brain damage affect a person’s perception? • Are there separate brain areas that determine our perception of different qualities? • How has the operation of our visual system been shaped by evolution and by our day-today experiences? Lateral Geniculate Nucleus of the Thalamus Maps: Representing Spatial Layout • Retinotopic map - each place on the retina corresponds to a place on the LGN • Determining retinotopic maps - record from neurons with an electrode that penetrates the LGN obliquely – LGN has 6 layers – Stimulating receptive fields on the retina shows the location of the corresponding neuron in the LGN Lateral Geniculate Nucleus Retinotopic mapping of neurons in the LGN. The Map on the Cortex • Cortex shows retinotopic map too – Electrodes recording from a cat’s visual cortex shows: • Receptive fields on the retina that overlap also overlap in the cortex • This pattern is seen using an oblique penetration of the cortex Retinotopic mapping of neurons in the cortex. The Map on the Cortex - continued • Cortical magnification factor – Fovea has more cortical space than expected • Fovea accounts for .01% of retina • Signals from fovea account for 8% to 10% of the visual cortex • This provides extra processing for highacuity tasks • How do we know this stuff? Brain Imaging Techniques • Positron emission tomography (PET) – Person is injected with a harmless radioactive tracer – Tracer moves through bloodstream – Monitoring the radioactivity measures blood flow – Changes in blood flow show changes in brain activity The subtraction technique Brain Imaging Techniques - continued • Functional magnetic resonance imaging (fMRI) measures blood flow by: – Hemoglobin carries oxygen and contains a ferrous molecule that is magnetic – Brain activity takes up oxygen, which makes the hemoglobin more magnetic – fMRI determines activity of areas of the brain by detecting changes in magnetic response of hemoglobin • Subtraction technique is used like in PET Purple and teal areas show the extent of stimuli that were presented while a person was in an fMRI scanner. (b) Purple and teal indicates areas of the brain activated by the stimulation in (a). Organization in Columns • LGN receives signals for right and left eyes – Layers 2, 3, and 5 receive input from the ipsilateral eye – Layers 1, 4, and 6 receive input from the contralateral eye • Electrodes inserted perpendicular to the surface show that receptive fields along the track are in the same location in the retina Cross section of the LGN showing layers. Organization in Columns - continued • Visual cortex shows: – Location columns • Receptive fields at the same location on the retina are within a column – Orientation columns • Neurons within columns fire maximally to the same orientation of stimuli • Adjacent columns change preference in an orderly fashion • 1 millimeter across the cortex represents entire range of orientation Figure 4.9 When an electrode penetrates the cortex perpendicularly, the receptive fields of the neurons encountered along this track overlap. The receptive field recorded at each numbered position along the electrode track is indicated by a correspondingly numbered square. All of the cortical neurons encountered along track A respond best to horizontal bars (indicated by the red lines cutting across the electrode track.) All of the neurons along track B respond best to bars oriented at 45 degrees. Organization in Columns - continued • Visual cortex shows (cont.) – Ocular dominance columns • Neurons in the cortex respond preferentially to one eye • Neurons with the same preference are organized into columns • The columns alternate in a left-right pattern every .25 to .50 mm across the cortex Figure 4.11 (a) How a peppermint stick creates an image on the retina and a pattern of activation on the cortex. (b) How a long peppermint stick would activate a number of different orientation columns in the cortex. Lesioning or Ablation Experiments 1. An animal is trained to indicate perceptual capacities 2. A specific part of the brain is removed or destroyed 3. The animal is retrained to determine which perceptual abilities remain • The results reveal which portions of the brain are responsible for specific behaviors What and Where Pathways • Ungerleider and Mishkin (1983) – Object discrimination problem • Monkey is shown an object • Then presented with two choice task • Reward given for detecting the target – Landmark discrimination problem • Monkey is trained to pick the food well next to a cylinder What and Where Pathways - continued • Ungerleider and Mishkin (cont.) – Using ablation, part of the parietal lobe was removed from half the monkeys and part of the temporal lobe was removed from the other half – Retesting the monkeys showed that: • Removal of temporal lobe tissue resulted in problems with the landmark discrimination task - What pathway (I see it, but don’t know where it is) • Removal of parietal lobe tissue resulted in problems with the object discrimination task - Where pathway (I know where it is, but I don’t know what it is) The monkey cortex, showing the what, or ventral pathway from the occipital lobe to the temporal lobe, and the where, or dorsal pathway from the occipital lobe to the parietal lobe. What and Where Pathways - continued • What pathway also called doral pathway • Where pathway also called ventral pathway • Both pathways originate in retina – Ventral pathway begins in small or medium ganglion cells • Called P-cells • Axons synapse in layers 3, 4, 5, & 6 of LGN • Called parvocellular layers What and Where Pathways - continued – Dorsal pathway begins in large ganglion cells • Called M-cells • Axons synapse in layers 1 & 2 of LGN • Called magnocellular layers • Ablation research with monkeys shows: – Parvo channels send color, texture, shape and depth information – Magno channels send motion information The dorsal and ventral streams in the cortex originate with the magno and parvo ganglion cells and the magno and parvo layers of the LGN. The red arrow represents connections between the streams. The dashed blue arrows represent feedback - signals that flow “backward.” Retinal ganglion cells and their functions. What and Where Pathways - continued • Where pathway may actually be “How” pathway – Dorsal stream shows function for both location and for action – Evidence from neuropsychology • Single dissociations: two functions involve different mechanisms • Double dissociations: two functions involve different mechanisms and operate independently Table 4.2 Double dissociations in TV sets and people. What and How Pathways Neuropsycholgical Evidence • Behavior of patient D.F. – Damage to ventral pathway due to gas leak – Not able to match orientation of card with slot – But was able to match orientation if she was placing card in a slot – Other patients show opposite effects – Evidence shows double dissociation between ventral and dorsal pathways Figure 4.16 Performance of D.F. and a person without brain damage for two tasks: (a) judging the orientation of a slot; and (b) placing a card through the slot. See text for details. (From the Visual Brain in Action by A. D. Milner and M. A. Goodale. Copyright ©1995 by Oxford University Press. Reprinted by permission.) What and How Pathways - Further Evidence • Rod and frame illusion – Observers perform two tasks: matching and grasping • Matching task involves ventral (what) pathway • Grasping task involves dorsal (how) pathway – Results show that the frame orientation affects the matching task but not the grasping task Figure 4.17 (a) Rod and frame illusion. Both small lines are oriented vertically. (b) Matching task and results. (c) Grasping task and results. See text for details. Modularity: Structures for Faces, Places, and Bodies • Module - a brain structure that processes information about specific stimuli – Inferotemporal (IT) cortex in monkeys • One part responds best to faces while another responds best to heads • Results have led to proposal that IT cortex is a form perception module – Temporal lobe damage in humans results in prosopagnosia Figure 4.18 (a) Monkey brain showing location of the inferotemporal cortex (IT) in the lower part of the temporal lobe. (b) Human brain showing location of the fusiform face area (FFA) in the fusiform gyrus, which is located under the temporal lobe. Figure 4.20 Response of a neuron in the IT cortex for which the person’s head is an important part of the stimulus because firing stops when the head is covered. (From “Recognition of Objects and Their Components Parts: Responses of Single Units in the Temporal Cortex of the Macaque,” by E. Washmuth, M. W. Oram, and D. I. Perrett, 1994, Cerebral Cortex, 4, Copyright © 1994 by Oxford University Press.) Modularity: Structures for Faces, Places, and Bodies - continued • Evidence from humans using fMRI and the subtraction technique show: – Fusiform face area (FFA) responds best to faces as well as when context implies a face – Parahippocampal place area (PPA) responds best to spatial layout – Extrastriate body area (EBA) responds best to pictures of full bodies and body parts Figure 4.21 fMRI response of the human fusiform face area. Activation occurs when a face is present (E) or is implied (D) but is lower when other stimuli are presented (A,B,C,F). (Reprinted with permission from Cox, D., Meyers, E., Sinha, P. (2004). Contextually evoked object-specific responses in human visual cortex, Science, 304, 115-117. Evolution and Plasticity: Neural Specialization • Evolution is partially responsible for shaping sensory responses: – Newborn monkeys respond to direction of movement and depth of objects – Babies prefer looking at pictures of assembled parts of faces – Thus “hardwiring” of neurons plays a part in sensory systems Evolution and Plasticity: Neural Specialization - continued • Plasticity of neurons also shapes sensory responses – Experience-dependent plasticity in animals • Monkeys trained to recognize specific view of unfamiliar object • Other views of object showed decline in recognition as object rotated from trained view • Neurons in the IT cortex showed maximal response to the trained orientation Figure 4.23 (a) Stimuli like those used by Logothetis & Pauls (1995). (b) Monkey’s ability to recognize the training shape and rotated views of the shape that were not seen during training. (c) Response of neurons in the IT cortex of the trained monkey to the training shape and the rotated shape. Evolution and Plasticity: Neural Specialization - continued – Experience-dependent plasticity in humans • Brain imaging experiments show areas that respond best to letters and words • fMRI experiments show that training results in areas of the FFA responding best to: – Greeble stimuli – Cars and birds for experts in these areas Figure 4.24 (a) Greeble stimuli used by Gauthier. Participants were trained to name each different Greeble. (b) Brain responses to Greebles and faces before and after Greeble training. (a: From Figure 1a, p. 569, from Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P. L., & Gore, J. C. (1999). Activation of the middle fusiform “face area” increases with experience in recognizing novel objects. Nature Neuroscience, 2, 568-573.) Figure 4.25 Ways that the brain is organized. Sensory Code: Representation of Environment • Sensory code - representation of perceived objects through neural firing – Specificity coding - specific neurons responding to specific stimuli • Leads to the “grandmother cell” hypothesis • Recent research shows cells in the hippocampus that respond to concepts such as Halle Berry Figure 4.29 (a) Location of the hippocampus and some of the other structures that were studied by Quiroga and coworkers (2005) Sensory Code: Representation of Environment - continued – Problems with specificity coding: • Too many different stimuli to assign specific neurons • Most neurons respond to a number of different stimuli • Distributed coding - pattern of firing across many neurons codes specific objects – Large number of stimuli can be coded by a few neurons Sensory Code: Representation of Environment - continued – Coding can be distributed across many brain areas • Monkeys’ IT cortex shows overlap of activation caused by different stimuli • fMRI experiments with humans show the same type of effect • Thus, although there is specific response within modules, there is also activation across modules for specific stimuli Figure 4.26 How faces could be coded according to (a) specificity coding and (b) distributed coding. The height of the bars indicates the response of neurons 1, 2, and 3 to each stimulus face. See text for explanation.