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Motor cortex Somatosensory cortex Sensory associative cortex Pars opercularis Visual associative cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area Artificial Brain Organization 1 EE141 Janusz Starzyk, Ohio University Elements of Intelligence Abstract thinking and action planning Capacity to learn and memorize useful things Spatio-temporal memories Ability to talk and communicate Intuition and creativity Consciousness Emotions and understanding others Surviving in complex environment and adaptation Perception Motor skills in relation to sensing and anticipation 2 EE141 Problems of Classical AI Lack of robustness and generalization No real-time processing Central processing of information by a single processor No natural interface to environment 3 EE141 Intelligent Behavior Emergent from interaction with environment Based on large number of sparsely connected neurons Asynchronous Interact with environment through sensorymotor system Value driven Adaptive 4 EE141 Simple Brain Organization Reactive Associations Sensory Inputs Sensors Motor Outputs Actuators 5 EE141 Simple Brain Properties Interacts with environment through sensors and actuators Uses distributed processing in sparsely connected neurons Uses spatio-temporal associative learning Uses feedback for input prediction and screening input information for novelty 6 EE141 Brain Structure with Value System Value System Reinforcement Signal Action Planning Anticipated Response Sensory Inputs Sensors Motor Outputs Actuators 7 EE141 Brain Structure with Value System Properties Interacts with environment through sensors and actuators Uses distributed processing in sparsely connected neurons Uses spatio-temporal associative learning Uses feedback for input prediction and screening input information for novelty Develops an internal value system to evaluate its state in environment using reinforcement learning Plans output actions for each input to maximize the internal state value in relation to environment Uses redundant structures of sparsely connected processing elements 8 EE141 Value System in Reinforcement Learning Control States Environment Controller Value System Optimization Reinforcement Signal 9 EE141 Artificial Brain Organization Value System Understanding Decision making Action Planning Anticipated Response Reinf. Signal Sensors EE141 Sensory Inputs Motor Outputs Actuators 10 Artificial Brain Organization Learning should be restricted to unexpected situation or reward Anticipated response should have expected value Novelty detection should also apply to the value system Need mechanism to improve and compare the value 11 EE141 Artificial Brain Organization Value System Understanding Action Planning Expectation Improvement Detection Comparison Inhibition Novelty Detection Anticipated Response Reinf. Signal Sensors EE141 Sensory Inputs Motor Outputs Actuators 12 Artificial Brain Organization Anticipated response block should learn the response that improves the value A RL optimization mechanism may be used to learn the optimum response for a given value system and sensory input Random perturbation of the optimum should be used to the optimum response in case the value system changed New situation will result in new value and WTA will chose the winner Problem is how to do 13 EE141 Artificial Brain Organization 14 EE141 Artificial Brain Organization Positive Reinforcement Sensory Inputs EE141 Negative Reinforcement Motor Outputs 15 Artificial Brain Selective Processing Sensory inputs is represented by more and more abstract features in the sensory inputs hierarchy Possible implementation is to use winner takes all or Hebbian circuits to select the best match Random wiring may be used to preselect sensory features Uses feedback for input prediction and screening input information for novelty Uses redundant structures of sparsely connected processing elements 16 EE141 Artificial Brain Organization WTA WTA WTA WTA 17 EE141 Microcolumn Organization V. Mountcastle argues that all regions of the brain perform the same algorithm SOLAR combines many groups of neurons (microcolumns) in a pseudorandom way Each microcolumn has the same structure Thus it performs the same computational algorithm satisfying Mountcastle’s principle Mindful Brain Cortical Organization and the Group-Selective Theory of Higher Brain Function G. M. Edelman and V. B. Mountcastle MIT Press, March 1982 18 EE141 Microcolumn Organization superneuron WTA Positive Reinforcement Negative Reinforcement WTA Sensory Inputs WTA Motor Outputs 19 EE141 Superneuron Organization Each microcolumn contains a number of superneurons Within each microcolumn, superneurons compete on different levels of signal propagation Superneuron contains a predetermined configuration of Sensory (blue) Motor and (yellow) Reinforcement neurons (positive green and negative red) Superneurons internally organize to perform operations of Input selection and recognition Association of sensory inputs Feedback based anticipation Learning inhibition Associative value learning, and Value based motor activation 20 EE141 Superneuron Organization Sensory neurons are primarily responsible for providing information about environment They receive inputs from sensors or other sensory neurons on lower level They interact with motor neurons to represent action and state of environment They provide an input to reinforcement neurons They help to activate motor neurons Motor neurons are primarily responsible for activation of motor functions They are activated by reinforcement neurons with the help from sensory neurons They activate actuators or provide an input to lower level motor neurons They provide an input to sensory neurons Reinforcement neurons are primarily responsible for building the internal value system They receive inputs from reinforcement learning sensors or other reinforcement neurons on lower level They receive inputs from sensory neurons They provide an input to motor neurons They help to activate sensory neurons 21 EE141 Sensory Neurons Interactions WTA WTA WTA 22 EE141 Sensory Neurons Functions Sensory neurons are responsible for Representation of inputs from environment Interactions with motor functions Anticipation of inputs and screening for novelty Selection of useful information Identifying invariances Making spatio-temporal associations WTA WTA EE141 23 WTA Sensory Neurons Functions Sensory neurons Represent inputs from environment by Responding to activation from lower level (summation) Selecting most likely scenario (WTA) Interact with motor functions by Responding to activation from motor outputs (summation) Anticipate inputs and screen for novelty by Correlation to sensory inputs from higher level Inhibition of outputs to higher level Select useful information by Correlating its outputs with reinforcement neurons Identify invariances by Making spatio-temporal associations between neighbor sensory neurons 24 EE141 25 EE141