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1、COGNITIVE NEUROSCIENCENotePlease read book to review major brain structures and their functionsPlease read book to review brain imaging techniquesSee also additional slides available on class websiteCognitive Neurosciencethe study of the relation between cognitive processes and brain activitiesPoten
2、tial to measure some “hidden” processes that are part of cognitive theories (e.g. memory activation, attention, “insight”)Measuring when and where activity is happening. Different techniques have different strengths: tradeoff between spatial and temporal resolutionTechniques for Studying Brain Funct
3、ioningSingle unit recordingsHubel and Wiesel (1962, 1979)Event-related potentials (ERPs)Positron emission tomography (PET)Magnetic resonance imaging (MRI and fMRI)Magneto-encephalography (MEG)Transcranial magnetic stimulation (TMS)The spatial and temporal ranges of some techniques used to study brai
4、n functioning. Single Cell Recording(usually in animal studies)Measure neural activity with probes. E.g., research byHubel and Wiesel:Hubel and Wiesel (1962)Studied LGN and primary visual cortex in the cat. Found cells with different receptive fields different ways of responding to light in certain
5、areasLGN On cell (shown on left)LGN Off cellDirectional cellAction potential frequency of a cell associated with a specific receptive field in a monkeys field of vision. The frequency increases as a light stimulus is brought closer to the receptive field.COMPUTATIONAL COGNITIVE SCIENCEComputer Model
6、sArtificial intelligenceConstructing computer systems that produce intelligent outcomesComputational modelingProgramming computers to model or mimic some aspects of human cognitive functioning. Modeling natural intelligence. Simulations of behaviorWhy do we need computational models?Provides precisi
7、on need to specify complex theories. Makes vague verbal terms specific Provides explanationsObtain quantitative predictions just as meteorologists use computer models to predict tomorrows weather, the goal of modeling human behavior is to predict performance in novel settingsNeural NetworksAlternati
8、ve to traditional information processing models Also known as: PDP (parallel distributed processing approach) and Connectionist modelsNeural networks are networks of simple processors that operate simultaneouslySome biological plausibilitySIdealized neurons (units)OutputProcessorInputsAbstract, simp
9、lified description of a neuronDifferent ways to represent information with neural networks: localist representationconcept 1concept 2concept 3Each unit represents just one item “grandmother” cells100000000100010000Unit 1Unit 2Unit 3Unit 4Unit 5(activations of units; 0=off 1=on)Unit 6Coarse Coding/ D
10、istributed Representationsconcept 1concept 2concept 3111000101101010101(activations of units; 0=off 1=on)Each unit is involved in the representation of multiple itemsUnit 1Unit 2Unit 3Unit 4Unit 5Unit 6Advantage of Distributed RepresentationsEfficiency Solve the combinatorial explosion problem: With
11、 n binary units, 2n different representations possible. (e.g.) How many English words from a combination of 26 alphabet letters? Damage resistanceEven if some units do not work, information is still preserved because information is distributed across a network, performance degrades gradually as func
12、tion of damage (aka: robustness, fault-tolerance, graceful degradation)Suppose we lost unit 6concept 1concept 2concept 3111000101101010101(activations of units; 0=off 1=on)Can the three concepts still be discriminated?Unit 1Unit 2Unit 3Unit 4Unit 5Unit 6An example calculation for a single neuronDiag
13、ram showing how the inputs from a number of units are combined to determine the overall input to unit-i. Unit-i has a threshold of 1; so if its net input exceeds 1 then it will respond with +1, but if the net input is less than 1 then it will respond with 1Neural-Network ModelsThe simplest models in
14、clude three layers of units:(1) The input layer is a set of units that receives stimulation from the external environment. (2) The units in the input layer are connected to units in a hidden layer, so named because these units have no direct contact with the environment. (3) The units in the hidden
15、layer in turn are connected to those in the output layer. Multi-layered NetworksActivation flows from a layer of input units through a set of hidden units to output unitsWeights determine how input patterns are mapped to output patternsNetwork can learn to associate output patterns with input patter
16、ns by adjusting weightsHidden units tend to develop internal representations of the input-output associationsBackpropagation is a common weight-adjustment algorithmhidden unitsinput unitsoutput unitsExample of Learning Networkshttp:/www.cs.ubc.ca/labs/lci/CIspace/Version3/neural/index.htmlAnother ex
17、ample: NETtalk7 groups of 29 input units26 output units80 hidden units_a_cat_7 letters of text input(after Hinton, 1989)target letterteacher/k/target outputConnectionist network learns to pronounce English words: i.e., learns spelling to sound relationships. Listen to this audio demo.Other demosHopf
18、ield networkhttp:/www.cbu.edu/pong/ai/hopfield/hopfieldapplet.htmlBackpropagation algorithm and competitive learning:http:/www.cs.ubc.ca/labs/lci/CIspace/Version4/neural/http:/www.psychology.mcmaster.ca/4i03/demos/demos.htmlCompetitive learning:http:/www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/re
19、search/gsn/DemoGNG/GNG.htmlVarious networks:http:/diwww.epfl.ch/mantra/tutorial/english/Optical character recognition:http:/sund.de/netze/applets/BPN/bpn2/ochre.htmlBrain-wave simulatorhttp:/www.itee.uq.edu.au/%7Ecogs2010/cmc/home.htmlNeural Network ModelsInspired by real neurons and brain organization but are highly idealized Can spontaneously generalize beyond information explicitly given to networkRetrieve information even when network is damaged (graceful degradation)Networks can be taught: learning is possible by changing weighted connections between nodes
