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IJCNN 2010 Panel
Between Bottom-up and Top-DownWhat Is “the Much in-between”?
Wednesday July 21, 4:50pm, Room 123Presentation Panel Members:
Paolo ArenaEdgar Koerner
Asim Roy Ron Sun Yan Meng
Giogio MettaAngelo CangelosiJanusz A. Starzyk John G. Taylor
Narayan Srinivasa
Juyang Weng
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Questions for the Penal1. Does the brain use symbolic representation in a fashion similar
to our symbolic AI? Why? What lesson can we learn?
2. Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?
3. How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? Between bottom and top, what is the “much in-between”?
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Questions for the Penal1. Does the brain use symbolic representation in a fashion similar
to our symbolic AI? Why? What lesson can we learn?
2. Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?
3. How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? Between bottom and top, what is the “much in-between”?
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Embodied cognitive capabilities from simple brains
Paolo Arena
University of Catania
ITALY
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(1) L. Chittka and J. Niven, “Are bigger brains better?” Current Biology 19, 2009 (2) Shettleworth, S.J. 2001: Animal cognition and animal behaviour, – Animal Behaviour 61: 277-286.(3) van Swinderen, B. and Greenspan, R.J. (2003) Salience modulates 20–30 Hz brain activity in Drosophila. Nat. Neurosci. 6, 579–586(4) V. Srinivasan, Evidence for counting in insects, Anim Cogn (2008)(5) R. Strauss et al. “Analysis of a spatial orientation memory in Drosophila”, Nature 453, 1244-1247, 2008 (6) V. Srinivasan et al. “Grouping of visual objects by honeybees” J. Exp. Biol., 2004(7) Jan Wessnitzer*, Place memory in crickets, Proc. R. Soc. B (2008)
Simple animals (insects) are not only reflex automataThey show (individually) capabilities like numerosity (4) , attention and categorisation-like processes (6) , sameness vs difference (1), water maze solution (7)
considered as clear traits of cognition (2)
in a limited neuron number (105-106) and simpler organization than mammals. Drosophila Melanogaster is a cognitive poor cousin of the Bee, but genetic tools are available to unravel the structure-function-behavior loop (5)
Some Capabilities of a simple Brain
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•Orientation•object detection• classification• memory
•Olfactory learning •Context generalization from visual info•Behav. Evaluation
Toward an insect brain model
EnvironmentEnvironment
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Heisenberg, Nature, 2003
Some key aspects of simple Brains
TI
ME
Rabinovich et al., 2003J. Comp. Neurosci.
Axo-axonal horizontal connections among KC in locust MBs for space-time processing
Advanced polymodalCalices (Bee). Strausfeld et al. J. Comp. Neurology (2009)
Connectionist models of simple brains can show goal-directed reasoning as:Simple forms of sequence learning based on cascades of environmentally induced correlations
…following - P. Arena et al. IEEE TRANS. on N.NET.(2009)
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From sensory data distribution to symbol-like representation:
What is “in-between” ?
Edgar Koerner
Honda Research Institute Europe
Offenbach, Germany
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From signal to symbol processing
prediction modulates processing based on acquired knowledge about scene and context
short- and long-term memory of scenes, actions and its outcomes
predicted distancewarning
…
multi cue fusion using learning
scene rep.: integration / prediction
context / situation / task
image
common preprocessing
cue #1 cue #2 cue #n
• learned cue relevance from environment (day, night, rain)
• use of domain knowledge e.g. predicted road area
• predicted car positions knowledge of expected featurescontext / task
learn
)( 224 RfR
Ra
multilateral topologicalinteraction
weight
unique neuronal & topological task dependent fusion strategy
learn
cues
for simple reactive systems, direct linking works sufficiently good
How to link sensory data distribution to symbolic knowledge representation ?
intermediate representations seem to be required for robust and flexible problem solving in a complex environment
How to define intermediate representations - by sensory data structure?
Complex interaction in real world environment requires anchoring of knowledge representation to sensory data distributions
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What determines intermediate representations in the brain?
The cortex stores in its hierarchical structure a hierarchy of representations, like features, objects, scenes, concepts, …
The basic wiring of the representational hierarchy is genetically encoded, referenced to the subcortical behaviour control.
This is a-priori knowledge, acquired while interacting with the environment during phylogenetic development.
Learning only fills in the blanks, adding specific experience based on sensory data.
Fellemann & van Essen 1991
What is stored at which location in the cortex - is not defined by sensory data structure, - but by pointers from its phylogenetically older subcortical reference structure (behaviour control).
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From activity distributions to symbols cortical area m cortical area k
activity at the upper cortical layers is abundant (activity distribution), lower layers 5 & 6 show sparse activation patterns (symbol quality)
The large pyramidal neurons of layer 5 (Py5) of the columnar structures are the read-out neurons to behaviour control.
Without subcortical structures no behaviour!
The cortex memorizes experience of successful behaviour for reuse under similar situations.
Behaviour related subcortical inputs control processing and learning at the respective columns.
The Py5 are fishing for useful information to control behaviour. Subcortical inputs can modulate all pyramidal neurons in a column.
processing of afferent data distributions
processing of afferent data distributions
afferentinput
◦ ◦◦lateralassociation
afferentinput
lateralassociation
1
2
3
4
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behaviour control output
cortex
thalamusbehaviour control output
subcortical structures(behaviour control)
subcortical structures(behaviour control)
top-downtop-down
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Repetitive instantiation of “symbols” in the brain?
There is no single “symbolic” knowledge representation level on top- Each behavioural output may instantiate a decision (symbolic quality) based on data distributions shaped by its afferent, efferent, and association inputs- Complexity of symbol content increases with hierarchic representation level
Seemingly, there is no „intermediate“ representation without a behavioural relevance
Behaviour includes both interaction with environment (overt) internal simulation for planning and decision making (covert)
hierarchic levels
symbol
activitydistribution
symbol
symbol
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Behaviour control needs define the “in-between”
For defining the representational architecture of autonomously interacting systems we have to elucidate the relational architecture of the targeted behaviour, but not to start with data structure
The system does not describe the outside world from an observer’s point of view, but it describes its interaction with the environment from a subjective point of view – using the metric of its control space for defining the “in-between” levels of representation
◦◦◦
◦ ◦ ◦
afferent data distribution
symbolic knowledgerepresentation
afferent data distribution
behaviour output
◦ ◦ ◦◦◦◦
◦◦ ◦
◦◦◦
Intermediate representations in the brain are not essentially defined by sensory data structure, but by the relational architecture of behaviour control.
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Q1: «Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?»
Is localist representation = symbolic representation?
Jeff Bowers has published a paper or two arguing for the viability of grandmother cells -- cells that represent whole "objects" such as a specific face (or your grandmother's face)
Connectionists claim they are at the symbolic level too and can do symbolic computation (Smolensky and others)
Connectionists don’t deny the symbolic level
The argument is not about symbolic level, but about the form and nature of representation and computation
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ReferencesBowers JS (2009). On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. Psychological review, 116 (1), 220-51 PMID: 19159155
Bowers JS (2010). More on grandmother cells and the biological implausibility of PDP models of cognition: a reply to Plaut and McClelland (2010) and Quian Quiroga and Kreiman (2010). Psychological review, 117 (1) PMID: 20063980
Plaut, D., & McClelland, J. (2010). Locating object knowledge in the brain: Comment on Bowers’s (2009) attempt to revive the grandmother cell hypothesis. Psychological Review, 117 (1), 284-288 DOI: 10.1037/a0017101
Smolensky, P. (1988). On the proper treatment of connectionism. The Behavioral and Brain Sciences, 11, 1-74.
Smolensky, P. (1995). Connectionism, constituency and the language of thought. In Macdonald, C., & Macdonald, G. (Eds.), Connectionism. Cambridge, MA: Blackwell.
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Q2: «Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks
cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed sensor-based reasoning? Why? What lesson can we learn?»
There is ongoing work on neural implementation of symbolic logic
Van der Velde and de Kamps have proposed a neural blackboard architecture for sentence structure analysis
It allows for combinatorial (or compositional) structures
Frank van der Velde and Marc de Kamps (2006). Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences, 29 : 37-70
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Q3: «Symbolic AI architectures start from abstract concepts and connectionist architectures start from concrete receptors (e.g., pixels). Hybrid architectures use both.
What is the "much in-between" --- between concrete sensory inputs and abstract concepts?»
Almost all useful knowledge is built bottom-up; that’s why you have years and years of schooling and practice
You may be taught all the grammar rules of a language, but you won’t be able to write good essays unless you practice for years and years
You can read all the books and watch all the video about playing a game (soccer, tennis or swimming), but you can’t play the game unless you practice
OLD AI - rules are given to you and all you have to do is store and use them But it doesn’t work – try learning math without ever solving a problem
In many cases, rules are given to you to guide learning, but they are not part of the operational system
Abstract concepts need to be translated into operational networks Roy, A. 2000. On Connectionism, Rule Extraction and Brain-like Learning. IEEE Transactions on Fuzzy Systems, 8, 2, 222-227.
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Q4: «How do you think about the brain/artificial architecture for autonomous development? As the brain is "skull-closed," how does it fully autonomously
develop its internal representations for the "much in-between" through experience, from one task to the next?»
NSF Report (2007) – “This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment autonomous artificial learning systems.”
We need to develop autonomous algorithms – ones that don’t depend on human babysitting for fine tuning of parameters
It’s time to either fix our algorithms or invent new ones
We are interested in forming working groups to create new kinds of algorithms
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Autonomous Learning and Development within CLARION
How a hybrid cognitive architecture (CLARION, as a psychological theory) interacts with the world and learns
Ron Sun
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CLARION Theory: Basic Ideas(psychologically realistic, comprehensive theory
of the mind) Hybrid connectionist-symbolic system Dual representation – dual-process theory of the mind
(two levels or more --- implicit, explicit, meta-cognitive, etc.; Evans and Frankish, 2009)
Capable of both explicit goal-directed reasoning (e.g. in action decision making), as well as implicit reactive responses (from pattern recognition)
The two levels interact with each other Bottom-up learning and top-down learning: key to
autonomous learning and development Motivational and meta-cognitive processes: secondary
control of behavior
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CLARION: Answers to the Questions Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why?
What lesson can we learn? Yes, to a certain extent. But that’s a tiny part. Majority is implicit, embodied, gradual, and/or
reactive, as illustrated by CLARION and associated psychological evidence (see Sun, 2002; Sun et al., 2005, Psyc Rev; Helie and Sun, 2010, Psyc Rev).
Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?
Yes. Connectionist models can perform reasoning, implicitly or explicitly. See Sun (1994, 2002, 2011). But what is the point?
Both bottom-up and top-down learning are needed for human learning. Their interactions are crucially important (for developing reasoning and beyond).
How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?
Humans learn through interacting with the world. In the process, rely heavily on implicit learning, bottom-up learning (as well as top-down learning), and on meta-cognitive control and regulation, on the basis of the motivational underpinnings (as in CLARION). Also learn through interacting with the social and cultural environment (imitation, communication, teaching, etc.).
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A Few Pointers to the Literature on the CLARION Theory, Data, and
Implementation R. Sun, Anatomy of the Mind. Oxford University Press, New York. 2011. R. Sun, Duality of the Mind. Lawrence Erlbaum Associates, Mahwah, NJ. 2002.
S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 2010.
R. Sun, Motivational representations within a computational cognitive architecture . Cognitive Computation, Vol.1, No.1, pp.91-103. 2009.
R. Sun, P. Slusarz, and C. Terry, The interaction of the explicit and the implicit in skill learning: A dual-process approach . Psychological Review, Vol.112, No.1, pp.159-192. 2005.
R. Sun, E. Merrill, and T. Peterson, From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, Vol.25, No.2, pp.203-244. 2001.
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Between Bottom-Up and Top-Down What Is «the Much in-between»?
Yan Meng
Department of Electrical and Computer Engineering
Stevens Institute of Technology
Hoboken, NJ, USA
Email: [email protected]
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Q1: Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?
All representation are symbolic. A symbol represents something else by association, resemblance, or convention. The real questions is what level of abstraction is necessary to reproduce the functionality of a brain, assuming a perfect model? How can we enable an AI system to modify/extend its abstraction.
Q2: Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed sensor-based reasoning? Why? What lesson can we learn?
It isn’t the issue of capability, but rather efficiency. One can devise a case study that is narrow or simple enough to solve with a feasibly-sized ANN, that doesn’t solve the underlying scalability problem nor bring us any closer to a system with general reasoning capabilities. ANN is useful as an interface between sensor inputs and more abstract reasoning modules in hybrid AI systems.
Q3: How do you think about the brain/artificial architecture for autonomous development? As the brain is "skull-closed," how does it fully autonomously develop its internal representations from one task to the next? What is the “much-in-between”?
Brain does not start as a random network that self-organizes based on experience, brain starts with some basic set of primitives and basic assumptions which must be highly self-extensible. New knowledge gained from experience/stimuli and existing knowledge used to interpret experiences.
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What is “Much in between”? and Knowledge Organization• There is a hierarchy of abstraction
starting from the least abstract (“raw sensory patterns) and progressing through multiple levels of increasing abstraction
• Each level is basically indentifying patterns in the level below itself
• Higher abstract concepts are built from relationships among and generalizations of lower abstract concepts
Input Sequence
BCM
GRN
CMA-ES
EnvolvingTuning
E-BCM-GRN Model
Input
Output
textLabel 1 Label 2 …….
(Y. Meng, Y. Jin, J. Yin, and M. Conforth, IJCNN 2010)
Input Frames…...
Spatial Features
(0,0) (0,1)
(1,0) (1,1)
(m+1,n+1)
…...
…...
…...
…...
…...
(1,1)
(m,n)
…...
…...…...
…...
…...
Middle Layers
Higher Layer
Neurons
…...
Long Term Memory
Context Hierarchy
Working Memory
UnconsciousProcesses
UnconsciousProcesses
Short Term Memory
SensoryMemory
ActivePerception
PassivePerception
Short TermEpisodicMemory
PerceptualMemory
ProceduralMemory
SemanticMemory
EpisodicMemory
Short TermPerceptual
Memory
Reflex
Stimuli
Action
Conscious Focus
ConsciousVolition
IntrinsicMotivation
PreservationDrives
UnconsciousSkill
UnconsciousProcesses
Problem
Solution
UnconsciousProcesses
UnconsciousProcesses
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Fusing Bottom-up Learning and Top-down Learning in Object Recognition
• NN Architecture– One input layer, one output layer,
multiple hidden layers– Independent bi-directional connections
between adjacent layers• Data flows
– Bottom-up pathway generates hypotheses
– Top-down pathway produces predictions– Fusion techniques integrate two-pathway
info • Learning
– Gradient descent learning for both batch and online visual object recognition tasks
vy
D
uy
ix
jx
L
uvc
ivw
vjp
(Y. Zheng, Y. Meng, and Y. Jin, IJCNN 2010)
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… in betweenGiorgio Metta
Italian Institute of Technology & University of Genoa
Genoa, ITALY
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Some of the “in between” (in the brain)
Mirror neurons: motor neurons that are activated when “seeing” someone else’s hand performing a manipulative action and in performing the same action
Found also in f4, parietal cortex, but mirror effects more widespread that initially thought
From: Fadiga, L., L. Fogassi, V. Gallese, and G. Rizzolatti, Visuomotor Neurons: ambiguity of the discharge or "motor“ Perception? Internation Journal of Psychophysiology, 2000. 35: p. 165-177.
The type of action seen is relevant
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Action and visual perception
Understanding mirror neurons: a bio-robotic approach. G. Metta, G. Sandini, L. Natale,
L. Craighero, L. Fadiga. Interaction Studies. Volume 7 Issue 2. 2006
Classification(recognition)
Grasping actions
Visual space Motor space
Object affordances (priors)
Exp. I(visual)
Exp. I I(visual)
Exp. I I I(visual)
Exp. I V(motor)
Training
# Sequences 16 24 64 24
# of view points 1 1 4 1
Classif ication rate
100% 100% 97% 98%
# Features 5 5 5 15
# Modes 5-7 5-7 5-7 1-2
Test
# Sequences 8 96 32 96
# of view points 1 4 4 4
Classif ication rate
100% 30% 80% 97%
Individual A Individual BVMMVMM
Vision Classifier
Fv, OkGi
Vision ClassifierVMM
Fv, Ok Fm, OkGi
Learned by backpropagation ANN
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Action and speech perceptionaudio
features extraction
classifier
AMM
speech
b,p,d,t
audiofeatures
motorfeatures
w/ motor audio
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Giorgio’s answers to the questions Does the brain use symbolic representation in a fashion similar to our symbolic AI?
Why? What lesson can we learn?Not necessarily, but the brain can “build” punctuated representations of events and objects via mechanisms similar to affordances (links between generated actions, target objects and perceived effects).
Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?
Manipulation of “symbols” (affordances) is reasoning and this manipulation can be implemented by means of artificial neural networks.
How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?A possible architecture (also similar to Shanahan’s) sees two levels of generalization: first from sensorimotor patters to affordances and then from affordances to planning/reasoning.
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Embodied ConnectionismAngelo Cangelosi
Centre for Robotics and Neural Systems
University of Plymouth, UK
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Embodied Cognition: The Case of Action and Language
There are two opposing theoretical approaches to the study of language and cognition (in humans and cognitive systems/robots)
1. Language is autonomous and amodal (e.g. Fodor, Chomsky, Landauer & Dumais)
2. Language is integrated with cognition and grounded in the world/body (eg. Cangelosi & Harnad, Gallese & Lakoff, Pulvermuller, Glenberg, Barsalou, Ellis et al., Coventry & Garrod ...)
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Embodied Connectionism
“Classical” Connectionism: 1.Fixed semantic feature representation of the environment (input and output units). 2.Network (organism) is passive and is exposed to frequency-based training protocols3.Subsymbolic representations for symbol manipulation tasks
Glenberg, A., (2005). Lessons from the embodiment of language: Why simulating human language comprehension is hard. In Cangelosi A., Bugmann G. & Borisyuk R. (Eds.) (2005). Modeling Language, Cognition and Action: Proceedings of the 9th Neural Computation and Psychology Workshop. World Scientific.
“No fixed set of semantic features can capture the [language embodiment] phenomena; instead the features needed for
understanding (e.g., that a tractor affords raising the body, or that a crutch affords transfer of a soccer ball) arise from an
interaction between types of bodies, human goals, and components of the situation.” (Glenberg, 2005)
Embodied Connectionism (Neuro-robotics): 1.Direct encoding of sensorimotor features (proprioception, vision, motors/actuators…)2.Organism actively explores and interacts with the environment3.Experiments on the development of language and action/language integration
Seidenberg, Plaut et al.
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Experiments on action and language and embodied connectionism
Cangelosi et al (2010). Integration of action and language knowledge: A roadmap for developmental robotics. IEEE Trans. on Autonomous Mental Development..
The Modi experiment: Thinking with your body (Morse et al. 2010).
Stimulus-Response Compatibility Effects and Microaffordance (Ellis et al. 2008; Macura et al. 2009).
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Embodied Connectionism: Answers to the Questions
Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?
There are no AI-type symbols in the brain, but there are embodied representations and PDP mechanisms that guide symbol-manipulation phenomena. E.g. Words (symbols) are grounded in sensorimotor experience.
Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?
Yes. Neuro and developmental robotics allows us to experiment the integration and embodiment of higher-order cognitive skills (e.g. language, action, emotions)
How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?
Embodiment, evolution and development can explain the learning of sensorimotor and higher-order cognition capabilities
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Mental Development and RepresentationBuilding through Motivated Learning
Janusz A. Starzyk
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Motor processing
Sensory processing
Rewards and subconscious
processing
Planning and thinking
Motivation and goal selection
Episodic memory
Semanticmemory
Action monitoring
Attention switching
Sensory inputs Motor outputs
Central executive
Top-Down Bottom-Up Interaction
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The goal is to reduce the dominant pain Abstract goals are created to reduce abstract pains Motivation is pain driven
- +
PainDual pain
+
Thirsty
Abstract pain
“glass of water” –
sensory input to abstract pain
center
Sensory pathway(perception, sense)
Motor pathway(action, reaction)
Primitive Level
Level I
Level IIwell
-
w. glass
draw
drink
ActivationStimulationInhibitionReinforcementEchoNeedExpectation
Connectionists Concept Creation
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Motivation Driven Learning
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What is Between Top & Bottomin the Brain?
John G. Taylor
Department of Mathematics,
King’s College London
Email: [email protected]
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In-between Land In the bottom: the ‘dwarves’ working at the coal-face carving
out features & sculpting actions In the top: Consciousness Goals Decisions Emotion values In between: Internal models Attention
SC
Parietal
A
Thal
ACGSF
G
NBM
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Sites of In-Between Land
Inferior Parietal (SMG+AG + …) (?) IPS & Superior Parietal (attn IMC( Superior Temporal (affordances/int models) Supplementary & Premotor Cortices (internal models/mirror
neurons) Function: attn: to awareness Function: int models: to mental simulation/reasoning
(GNOSYS review: JGT et al, Imag & Vision Comp 27:1641-1657, 2009)
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Attention circuits: Ventral & Dorsal (Corbetta et al 2002, 5, 8)
Have 2 BOLD-based circuits:
Dorsal(Endogenous)
Ventral(Exogenous)-As circuit breaker?- By NE?
[Corbetta et al, Neuron 58:306, 2008)]
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Attention Copy Model of Consciousness (CODAM)
IN
ReportSpeed input to reportInhibit distracters
Attention Attention
copy signal(precursor)
IN
OUT
Owner
Amplifyinput
Model of content + owner activity(CODAM, JGT 2000+)
Bias
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Overall Structure
Use functionally-defined modules (IMC, STM, feature analysers, action generators, corollary discharge systems& forward models, goals)
Connectivity genetically by chemical gradients More data to support this from brain imaging (Bressler,
Desimone) Apply architecture to cognitive machines (JGT)
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What is Between Top and Bottom in the Brain ?
Narayan Srinivasa
HRL Laboratories LLC
Malibu, CA USA
Email: [email protected]
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Brain: Computation and Function• Top-down and Bottom-up processing of signals in the brain is a mere abstraction
• Activity at any level can be transmitted to any other level via ascending and descending pathways
• Inhibition plays key roles in termination of a particular computation and enables temporal coordination by balancing excitation
• Signals propagated between brain areas via spikes with oscillations serving as means for packaging spike information
• Neuronal groups that are synchronized in spike activity form dynamic internal representations within brain areas – these groups are degenerate – more than one group can give same output
• Synaptic efficacy modified via spike timing and modulated via rewards or punishments affects the composition of neuronal groups and also the cortical gains during signal transmission
• Re-entrant signaling enabled by large number of reciprocal connections serves to bind activity of neuronal groups in various brain areas in space and time - either forced oscillations, resonant loops or via transient oscillatory coupling
• Four key functions performed by the brain: categorization, analogies, association and prediction
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Key Aspects of Brain Computation
Primary and secondary cortex in each modality
NONSELF World signals
• Laminar Cortical Architecture • Thalamocortical, Corticothalamic, Corticocortical Loops• Degenerate representations
Brainstem,hypothalamus,
autonomic centers
SELF Homeostatic systems & Proprioception
• Neural registration of internal signals (hormones) • Neural registration of muscles & joint states
Internal State Categorization Perceptual Categorization
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Brainstem,hypothalamus,
autonomic centers
Primary and secondary cortex in each modality
Internal State Categorization Perceptual Categorization
Correlation in septum, amygdala
and hippocampus etc
• Signals from self and non-self are associated• Memory of rewards and punishments during these associations are formed
Value-Category Memory
Key Aspects of Brain Computation
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Primary and secondary cortex in each modality
Perceptual CategorizationCorrelation in
septum, amygdalaand hippocampus etc
Value-CategoryMemory
Memory Formationin Frontal, Temporal and Parietal Areas
• Laminar Cortical Architecture• Corticocortical Loops• Degenerate representations• Associations between value- category memory and perceptual categorizations
Reentrant loop
Conceptual Categorization
Key Aspects of Brain Computation
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Embodiment & Emergence of Behaviors
Synaptic plasticity combined within laminar and subcortical structure interactions caused by embodiment in a non-stationary environment leads to emergence of complex behaviors
Brainstem,Hypothalamus,
autonomiccenters
Primary and secondary cortex in each modality
Correlation in septum, amygdala
and hippocampus etc
Memory Formationin Frontal, Temporal and Parietal Areas
SELF NON-SELF
BasalGanglia
Cerebellum
Motor ControlCircuits
Action
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Brain’s Internal Representation: Mixtures of Sensory and Motor Signals
Juyang Weng
Embodied Intelligence LaboratoryDepartment of Computer Science
Cognitive Science Program
Neuroscience Program
Michigan State University
East Lansing MI 48824 USA
http://www.cse.msu.edu/~weng/
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A1 for Q1: There Seems No Symbol in the Brain
• Representation source from environment:
• Receptors• Muscles and glands
• No master mapsDiff. from: Treisman 80; VanEssen 93, Tsotsos 95
• No meaning boundaryDiff. from: Hawkins 09 and many other symbolic methods
Weng IJCNN 2010
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A3 for Q3: Brain’s Representation
QuickTime™ and a decompressor
are needed to see this picture.
Weng & Luciw TAMD 2009
{ (x, z) | x in X, y in Y }Top Z: actions
Bottom X: receptors (e.g., pixels)
In-between: Y as
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A2 for Q2: ED Network for
Intent-Directed Reasoning
Epigenetic Developer (ED) Network:Each area: map (x, y) with (xi, yi) for all i
FA: Finite Automaton andIts probabilistic variants
Proved a relation: From any FA, there is an ED Network
=> ED Networks can reason
Marvin Minsky: ANNs cannot reason well
Weng IJCNN 2010
QuickTime™ and a decompressor
are needed to see this picture.
Luciw &Weng IJCNN 2010
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IJCNN 2010 Panel
Between Bottom-up and Top-DownWhat Is “the Much in-between”?
Wednesday July 21, 4:50pm, Room 123Discussion Panel Members:
Paolo ArenaEdgar Koerner
Asim Roy Ron Sun Yan Meng
Angelo CangelosiJanusz A. Starzyk John G. Taylor
Narayan SrinivasaJuyang Weng
Nik Kasabov