The LIDA Tutorial - Cognitive Computing Research Group (CCRG)
Complex Systems - Cognitive Computing Research Group (CCRG)
Transcript of Complex Systems - Cognitive Computing Research Group (CCRG)
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How Minds Work Neurobiological Nonlinear Complex Systems
Stan Franklin Computer Science Division & Institute for Intelligent Systems The University of Memphis
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Systems • Undefined term • Examples: solar system, automobile, weather system, desktop computer, nervous system, chair
• Systems often composed of parts or subsystems
• Subsystems generate the behavior of the system
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Dynamical System
• X a set, called the state space • Each point x ∈ X is a state of the system • A state is a snapshot of the system’s condition at some point in time
• T:X—>X the system’s global dynamics • T(x) is the next state following x
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Itinerary x 0 the state at time 0 T(x 0 ) = x 1 state at time 1 T(x 1 ) = x 2 state at time 2 …T(x n ) = x n+1 The sequence x 0, x 1, x 2, … x n …
Is called an itinerary
Dynamical systems theory studies the long range behavior of itineraries
Does it – Stabilize (fixed point)? – Endlessly repeat (periodic)?
– Go wild (chaotic)?
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One Dimensional Example
• X the set of digits {0,1,2,3,4,5,6,7,8,9}
• Itinerary an infinite decimal between 0 and 1
• .1212121212… an itinerary with x 0 = 1, x 1 = 2, x 2 = 1, etc.
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Example Itineraries • .3333333… stabilizes (converges to a fixed point 3)
• .987654321111111… stabilizes after a transient
• .123412341234… oscillates with period 4 • .654321212121… oscillates after a transient
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Chaotic Itinerary
• .41421256... (√2 1) chaotic itinerary • Deterministic (in this case algorithmic) • Inherently unpredictable • Sensitive dependence on initial conditions
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Longterm Behavior of Itineraries
• An itinerary can – Converge to a fixed point (stabilize) – Be periodic (oscillate) – Be chaotic (unpredictable)
• Attractors – itineraries of states close to them converge to them
• Basin of attraction – set of initial states whose itineraries converge to an attractor
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Onedimensional dynamical system
State space X = real numbers & ∞
Global Dynamics T(x) = x 2
Itineraries 0,0,0,0,… fixed point 1,1,1,1,… fixed point 2,4,8,16, … converges
to ∞ .5, .25,.125 … converges
to 0 2,4,8,16, … converges
to ∞
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Basins of attraction
. . .
. .
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1
0
1
X = reals plus T = squaring function
point repellor
point attractor
point attractor
Basin of 0 1
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Continuous vs Discrete • Discrete dynamical system, discrete time steps, x(t + 1) = T(x(t))
• Continuous dynamical system, continuous time, update continuously via solutions to differential equations
• Either can be approximated by the other
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Vector Field • Vector field – vector at each state specifies the global dynamics
• Vector gives direction and velocity of the instantaneous movement of that state
• Trajectory instead of itinerary
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Limit Cycle
• Limit cycle attractor denoted by heavy line • Trajectory of any state ends up on the limit cycle, or approaching it arbitrarily closely
• Basin of attraction the whole space • Continuous version of a periodic attractor
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Olfactory Perception
• Particular to a certain sensory modality, for example, olfaction
• Distinguish between the smell of a carrot and the smell of a fox
• Of critical importance to a rabbit • How is it done?
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Anatomy of olfaction
Receptors
Bulb
Cortex
⌦
⌦
Limbic & motor systems ⌦
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Olfactory Receptors • Receptors are chemoreceptor neurons, each with a docking place for a molecule of complementary shape
• Born with receptors keyed to many differently shaped molecules
• Receptor cells sensitive to a particular odorant are clustered nonuniformly
• Receptors occupy a two dimensional array • Odor specific data is in spatial and temporal patterns of activity in this array
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Olfaction in Action • A sniff sucks in molecules of smoke, which dock at some of the receptors
• Changes activity on the receptor array • Signal passed to olfactory bulb • New pattern recognized as smoke • Smoke signal passes to olfactory cortex • Become alarmed and signals to the motor cortex "get me out of here"
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Recognition Problems • Smoke composed of many types of molecules • Different fires produce different smoke stimulating very different receptors
• Pattern of receptors stimulated depends on the air currents and the geometry of nostrils
• Particular pattern stimulated might occur only once in the lifetime of the individual
• Each resulting pattern must be recognized as smoke—how?
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The HOW of Recognition • Meaning comes from pattern of activity over entire olfactory bulb
• Every bulb neuron participates in every olfactory discrimination
• Same odorant produces distinct patterns • Intention required for pattern to form • All patterns change with new learning
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Dynamics of Recognition • Exhalation – olfactory bulb stabilized in its chaotic attractor
• Inhalation – input from the receptor sheet destabilizes the olfactory bulb
• If smell is known, the trajectory falls into a limit cycle basin of attraction
• The odorant is recognized
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Readings
• Freeman, W. J. 1999. How Brains Make Up Their Minds. London: Weidenfeld & Nicolson General.
• Franklin, S. 1995. Artificial Minds. Cambridge MA: MIT Press
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Email and Web Addresses • Stan Franklin
– [email protected] – www.cs.memphis.edu/~franklin
• “Conscious” Software Research Group – www. csrg.memphis.edu/