Transcript emotion

Emotion in Brain-Inspired
Developmental Networks
Juyang (John) Weng
Computer Sci., Neurosci., Cognitive Sci.
Michigan State University
East Lansing, MI 49924 USA
[email protected]
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Patterns of Communication
(c) Autonomic nervous system;
(a) Point to Point
(b) Release hormones into blood (d) Diffuse modulatory systems
Beart, Connors & Paradiso 2007
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Major Neurotransmitters
Beart, Connors & Paradiso 2007
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BMI 831
Cognitive Science
for Brain-Mind
Research
Lecturer:
Juyang Weng
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BMI 871
Introduction to
Computational
Brain-Mind
Lecturer:
Juyang Weng
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Motivation/emotion in the Brain
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Motivational system in the brain:
biological causality for the development of emotion
Lower (physiology) emotion:
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Arousal: noradrenaline, oxytocin, cortisol
Habituation sensitization, familiarization
(acetylcholine and norepinephrine systems)
Pain avoidance (serotonin system)
Pleasure seeking (dopamine system)
Basic emotion: angry, happy, sad, scared, disgust, etc.
Higher emotion: moods, dispositions (e.g., jealousy)
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Neuromodulatory Systems
Jeff Krichmar, AB, 2008
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Quiz: Why Modulations
Quiz: The main purpose
of modulatory systems is:
A. Animal like
B. Establish bounds with
human
C. Look intelligent
D. Speed-up learning
E. Being quick to fight or
flee
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Quiz: Why Modulations
Quiz: The main purpose
of modulatory systems is:
A. Animal like
B. Establish bounds with
human
C. Look intelligent
D. Speed-up learning
E. Being quick to fight or
flee
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Why Motivation?
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Evolution: Pressure of survival
Understanding of the environment should not be
the primary goal of a life
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Sexual selection: survival is not sufficient
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Fruit flies, Rats, Humans
Males: Fight with males, court with females
Females: Select the male winners
Motivation: quick ways to sense a variety of
values
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Neuromodulators, diffused transmission
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In the blind, visual cortex is
reassigned to audition and touch.
Therefore, we chose not to
statically model brain areas!
Brain areas should emerge
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Motivation: Architectural Concepts
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Symbolic Value Systems
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Sutton & Barto 1981:
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reward as positive values
Delayed rewards
Ogmen 1997: Punishments, rewards, novelty
Sporns et al. 1999: Darwin robot
Kakade & Dayan 2002: novelty and shaping
Oudeyer et al. 2007: error max; progress max; similaritybased progress maximization
Huang & Weng 2007: punishment, reward, novelty
Cox & Krichmar 2009: Neuromodulation as a robot controller
Singh et al. 2010: reward and evolution
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Symbolic: Q-Learning
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Symbolic reinforcement learning
No need for state transition probability
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Value driven
Does not allow state inconsistency
Future value estimation for delayed rewards
Time discount model: state-action value:
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Q-Learning Limitations
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Symbolic:
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Exponential number of states, static
Brittle
Fixed, greedy value function:
Immediate rewards are preferred
Does not allow perception
Does not allow creativity for other nonmodeled concepts
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Theory: For Any AFA There Is a GDN
Marvin Minsky criticized ANNs
AFA: Agent Finite Automaton
GDN: Generative
Developmental Network
Weng IJCNN 2010
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Emergent: 5-HT & DA Systems
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Blue: 5-HT system
Red: DA system
Mechanism 1 (extra cell):
Mechanism 2 (within cell):
zip increases the threshold T
zis reduces the threshold T
If pre-action > T, fire
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5-HT, DA, Ach, NE
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Serotonin (5-HT):
pain, stress, threats and punishment
Dopamine: (DA)
pleasure, wanting, anticipation, and
reward
Acetylcholine (Ach):
expected uncertainty?
Norepinephrine (NE):
novelty (unexpected uncertainty)?
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Emergent Value Systems
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Daly, Brown, Weng 2011, Paslaski, VanDam, & Weng 2011,
Weng et al. Neural Networks 2013:
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Wang, Wu, & Weng 2011, 2012
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Neuromorphic Motivated Systems based on DN: 5-HT and DA
Wandering or foraging, face recognition
Neuromorphic Motivated Systems based on DN: Ach and NE
Novelty and Uncertainty
Synapse maintenance
Segmentation of objects from backgrounds
Zheng, Qian, Weng, Zheng 2013
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Effects on internal brain areas: change learning rates
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Effects of 5-HT and DA on Y?
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Effect on Z area:
Inhibit and excite actions
Weng et al. IJCNN 2011
Effect on Y area:
on learning rate
Zheng, Qian, Weng & Zhang, IJCNN 2013
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Dopamine System
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Serotonin System
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Acetylcholine System
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Norepinephrine System
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Bottom-Up Weights and 5-HT
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Y Weights for Z Neurons
Paslaski et al. IJCNN 2011
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Error Rates for Recognition
Paslaski et al. IJCNN 2011
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Navigation, Wandering, Foraging
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Three synthetic agents:
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Self (S)
Attractor (A)
Repulsor (R)
5-HT: distance-lonely and distancefear
DA: d < distance-desire
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Setting
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Environmental Settings
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No brainer (control): no pain, no pleasure
Love or War (D-lonely, D-fear) = (50, 50)
Little Danger (D-lonely, D-fear) = (25, 125)
Much Danger (D-lonely, D-fear) = (125, 25)
Looking over the Fence
(D-lonely, D-fear) = (125, 125)
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Average Distance
Daly et al. IJCNN 2011
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Ach and NE: Goal for Segmentation
(a) Bottom-up input to a neuron. (b) True object contour. (c) Estimated synaptogenic factor
Wang, Wu and Weng, IJCNN 2011
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Global Confidence: Face Recognition
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Ach & NE: Receiver Operating Curve
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ACh Estimates
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DN: Motivational System as Emotion
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Emergent nervous system
Modeled:
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5-HT (serotonin)
DA (dopamine)
Ach (acetylcholine)
NE (norepinephrine)
Basic and higher emotion:
developed from experience
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