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Computational Intelligence Cognitive Neuroscience Based on a course taught by Prof. Randall O'Reilly University of Colorado, Prof. Włodzisław Duch Uniwersytet Mikołaja Kopernika and http://wikipedia.org/ http://grey.colorado.edu/CompCogNeuro/index.php/CECN_CU_Boulder_OReilly http://grey.colorado.edu/CompCogNeuro/index.php/Main_Page Janusz A. Starzyk EE141 1 The Brain ... The most interesting and the most complex object in the known universe How can we understand the workings of the brain? On what level should we attack this question? An external description won’t help much. How can we understand the workings of a TV or computer? Experiments won’t suffice, we must have a diagram and an understanding of operational principles. To make certain that we understand how it works, we must make a model. 2 EE141 How do we know anything? An important question: how do we know things? Example: super diet based on dr. K, Chinese medicine and other miracle methods. How do we know that they work? How do we know that they are for real? Gall noticed that the skull shape decides about ones abilities. Thousands of cases confirmed his observations. Craniometry: measuring the bones of the skull to determine intelligence. Do I know or I only believe I know? Not being certain allows to learn, certainty makes learning difficult. If we know how easy it is to err we could avoid a scientific fallacy. 3 EE141 How to understand the brain? To understand: reduce to simpler mechanisms? Which mechanisms? Analogies with computers? RAM, CPU? Logic? Those are poor analogies. Psychology: first you must describe behavior, it looks for explanations most often on a descriptive level, but how to understand them? Physical reductionism: mechanisms of the brain. Reconstructionism: using mechanisms to reconstruct the brain’s functions We can answer many questions only from an ecological and evolutionary perspective: why is the world the way it is? Because that’s how it made itself ... Why does the cortex have a laminar and columnar structure? To create: what must we know in order to create an artificial brain? EE141 4 From molecules through neural networks 10-10 m, molecular level: ion channels, synapses, properties of cell membranes, biophysics, neurochemistry, psychopharmacology; 10-6 m, single neurons: neurochemistry, biophysics, LTP, neurophysiology, neuron models, specific activity detectors, emerging. 10-4 m, small networks: synchronization of neuron activity, recurrence, neurodynamics, multistable systems, pattern generators, memory, chaotic behaviors, neural encoding; neurophysiology ... 10-3 m, functional neural groups: cortical columns (104-105), group synchronization, population encoding, microcircuits, Local Field Potentials, large-scale neurodynamics, sequential memory, neuroanatomy and neurophysiology. 5 EE141 … to behavior 10-2 m, mesoscope networks: sensory-motor maps, self-organization, field theory, associative memory, theory of continuous areas, EEG, MEG, PET/fMRI imaging methods ... 10-1 m, transcortical fields, functional brain areas: simplified cortical models, subcortical structures, sensory-motor functions, functional integration, higher psychic functions, working memory, consciousness; (neuro)psychology, computer psychiatry ... Cognitive effects Principles of interactions Neurobiological mechanisms 6 EE141 Levels of description Summary (Churchland, Sejnowski 1988) 7 EE141 EE141 How does it all work? 8 Systemic level 9 EE141 … to the mind Now a miracle happens ... 1 m, CNS, the whole brain and organism: An interior world arises, intentional behaviors, goal-oriented actions, thought, language, everything that behavioral psychology examines. Approximations of neural models: Finite State Machine, rules of behavior, models based on the knowledge of cognitive mechanisms in artificial intelligence. What happened to the psyche, the internal perspective? Lost in translation: networks => finished machines => behavior 10 EE141 A neurocognitive approach Computational cognitive neuroscience: detailed models of cognitive functions and neurons. Neurocognitive computing: simplified models of higher cognitive functions, thinking, problem solving, attention, language, cognitive and behavioral controls. Lots of speculation, but qualitative models explaining the results of psychophysical experiments as well as the causes of mental illnesses are developing quickly. Even simple brain-like information processing yields results similar to the real ones! Forewarning against excessive optimism based on behavioral models. 11 EE141 Model of transformation Agent Architecture Reason Short-term Memory Perceive Act RETRIEVAL LEARNING Long-term Memory INPUT OUTPUT Task Environment Simulation or Real-World System EE141 From Randolph M. Jones, P : www.soartech.com 12 Model of self-organization Topographical representations in numerous areas of the brain: sensory impulses, in the motor cortex and cerebellum, multimodal maps of orientation inferior colliculus, visual system maps and maps of the auditory cortex. o Model (Kohonen 1981): competition between groups of neurons and local cooperation. x=data o=weights of neurons x o o o o x o o x o xo N-dimensional input space o o o Neurons react to signals adjusting their parameters so that similar impulses awaken neighboring neurons. Weights locate points in N-D neural network w 2-D 13 EE141 Dynamic model Strong feedback, neurodynamics. Hopfield model: associative memory, learning based on Hebb’s law, synchronized dynamics, two-state neurons. Vector of input potentials V(0)=Vini , i.e. input = output. Dynamics (iterations) Hopfield’s network reaches stationary states, or the answers of the network (vectors of elemental activation) to the posed question Vini (autoassociation). If the connections are symmetrical then such a network trends to a stationary state (local attractor). Vi t 1 sgn I i t 1 sgn t = discrete time. EE141 j WijV14j j Biophysical model – spiking neurons Synapses Soma I syn (t ) Spike EPSP, IPSP Rsyn Csyn Spike Cm Rm “Spiking Neuron Models”, W. Gerstner and W. Kistler Cambridge University Press, 2002 http://icwww.epfl.ch/~gerstner//SPNM/SPNM.html EE141 15 Molecular foundations Action potentials are the result of currents which flow through ionic channels in the cell membrane Hodgkin and Huxley measured these currents and described their dynamics through differential equations. -70mV Na+ Action potential K+ Ca2+ Ions/protein EE141 16 Hodgkin-Huxley model 100 inside I K mV C gK gNa gl Na outside 0 Ion channels sodium I Na potassium Ion pump leakage IK stimulus I leak du C g Na m3h(u ENa ) g K n 4 (u EK ) gl (u El ) I (t ) dt dh dm dn hnmhn0m ))u ) 0((u 0u( dt dt dt hn(m(uu()u) ) EE141 The likelihood the channel is open is described by extra variables m, n, and h. 17 Impulse response model Activation j t ti^ i Stimulus: EPSP t t f j Activation: AP t t ^ i ui Stimulus: EPSP t t ui t t t ui t EE141 w t t ij j Firing: All impulses and neurons Previous impulse i ^ i f j f j linear f t t ^ i threshold 18 Integration and activation model Activation j i ui I reset Stimulus : EPSP d ui ui RI (t ) dt ui t Fire+reset EE141 linear t t jf threshold 19 Psychological Phenomena Visual perception: viewing natural imagery we must understand ways of encoding obiects and scenes. Spatial awareness: considering the interaction between streams of visual information will let us simulate concentration Memory: modeling hippocampal structures allows us to understand various aspects of episodic memory, and learning mechanisms show how semantic memory arises. Working memory: explaining the capacity to simultaneously hold in the mind several numbers while performing calculations requires specific mechanisms in the neural model. 20 EE141 Psychological Phenomena Reading words: the network will learn to read and pronounce words and then to generalize its knowledge to the pronunciation of new words as well as to recreate certain forms of dyslexia. Semantic representations: analyzing a text on the basis of context, the appearance of individual words, the network will learn the semantics of many ideas. Decision-making and task execution: A model of the prefrontal cortex will be able to keep attention on performed tasks in spite of hindering variables. Development of the representation of the motor and somatosensory cortex: through learning and controlled selforganization; 21 EE141 Advantages of model simulations Models help to understand phenomena: enable new inspirations, perspectives on a problem allow the simulation of effects of damages and disorders (drugs, poisoning). help to understand behavior, models can be formulated on various levels of complexity, models of phenomena overlapping in a continuous fashion (e.g. motion or perception), models allow detailed control of experimental conditions and an exact analysis of the results Models require exact specification of underlying assumptions: allow for new predictions perform deconstructions of psychological concepts (working memory?) allow us to understand the complexity of a problem allow for simplifications enabling analysis of a complex system provide a uniform, cohesive plan of action 22 EE141 Disadvantages of simulations Models are often too simple, they should contain many levels. Models can be too complex, sometimes theory allows for simpler explanations (why are there no hurricanes on the equator?). It’s not always known what to provide for in a model. Even if models work, that doesn’t mean that we understand the mechanisms Many alternative yet very different models can explain the same phenomenon. What’s important are general rules, parameters are limited by neurobiology on various levels; the more phenomena a model explains, the more plausible and universal it is. Allowing for interactions and emergences (construction) is very important. Knowledge acquired from models should undergo accumulation. 23 EE141 Cognitive motivation Although the thinking process seems to be sequential information processing, more detailed models predict parallel processing Gradual transition between conscious and subconscious processes Parallel processing of sensory-motor signals by tens of millions of neurons Specialized areas of memory responsible for various representations e.g. shape, color, space, time Levels of symbolic representation More diffuse than binary logic Learning mechanisms as a foundation for cognitive science When you learn, you change the method of information processing in your brain Resonance between ”bottom-up” representation and ”top-down” understanding Prediction and competition of ideas 24 EE141