Transcript Introduction - Pete Mandik

Neural
Representation,
Embodied and
Evolved
Pete Mandik
Chairman, Department of Philosophy
Coordinator, Cognitive Science Laboratory
William Paterson University, New Jersey USA
[email protected]
Abstract:
What could representational content be such that
appeal to it can be explanatory? I tackle such
questions by addressing how representations that
explain intelligent behavior might be acquired
through processes of Darwinian evolution. I present
the results of computer simulations of evolved
neural network controllers and discuss the similarity
of the simulations to real world examples of neural
network control of animal behavior. I argue that
focusing on the simplest cases of evolved intelligent
behavior, in both simulated and real organisms,
reveals that evolved representations must carry
information about the creatures’ environments and
further can do so only if their neural states are
appropriately isomorphic to environmental states.
Further, these informational and isomorphism
relations are what are tracked by content
attributions in folk-psychological and cognitive
scientific explanations of these intelligent behaviors.
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Mental reps in folk-psych
George is opening the fridge
because:
George desires that he
drinks some beer
George sees that the fridge
is in front of him
George remembers that he
put some beer in the
fridge
George’s psychological
states cause his behavior
nGeorge’s psychological
states have
representational content
n
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Chemotaxis is a
representation-hungry problem
1-Sensor
Creature
Sensor 
n
n
n
left/right stimulus
location
underdetermined
by sensor activity
only proximity
directly perceived
Brain 
Steering
Muscles 
Adding memory
can help compute
gradient
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Single sensor chemotaxis
from the point of view of
folk-psychology
Suppose you are literally in a fog so dense
that while you can ascertain how dense it
is where you are, you cannot ascertain in
which direction the fog gets less dense.
After walking for a while you notice that the
fog is much less dense than it was
previously.
By comparing your current perception of a
less dense fog to your memory of a more
dense fog, you to infer that you are
moving out of the area of greatest
concentration.
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Chemotaxis in Caenorhabditis
Elegans
Effectively utilizing
only a single sensor
Orientation network
contains reciprocal
connections,
possibly
implementing
memory sufficient
for computing the
time derivative of
the sensor activity
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Artificial Life Experiment
n
Experimental Set Up
u
u
u
u
u
3 orientation networks: Feedforward, Recurrent, and Blind
Five runs each, for 240 million steps
Mutations allowed only for neural
weights
Fitness defined as lifetime distance
Initial weights: Evolved CPGs with
un-evolved (zero weights) orienting
networks
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Synthetic C. Elegans.
On the left, front view. On the right, top view.
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Neural network for the
synthetic C. Elegans.
Neurons include one sensor (s) and several motor
neurons (m) and interneurons (i). Single-headed
arrows indicate flow of information from one
neuron to the next. A double-headed arrow
between two neurons indicates both a feed-forward
and a feedback connection between them.
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Results
Results of the experiment comparing
recurrent, feed-forward, and blind
networks in an evolutionary
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Discussion
Worms without recurrent connections
were conferred no advantage by
sensory input.
Without the recurrent connections to
constitute a memory, the worms are
missing a crucial representation for
the computation of the change of the
local concentration over time.
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Heading in the gradient is determined by a
computation that takes as inputs both a
sensory representation that encodes
information about the current local
concentration and a memory
representation that encodes information
about the past local concentration.
The existence of a memory mechanism was
predicted by the folk psychological
explanation and supported by the
simulation experiments.
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What the representations are
sensory representations - states of
activations in the chemo-sensory
input neuron
memory representations - signals
conveyed along recurrent
connections
motor representations - states of
activation in neurons that output to
muscles.
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What is represented
In the sensory case: current local
concentration.
In the memory case: past local
concentration.
In the motor case: level of muscular
contraction
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In what consists representing
The correlating
causally of
elements in
isomorphic
structures
(like mercury
column heights
and
temperatures)
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The sensory and memory states are able drive
successful chemotaxis in virtue of the informational
relationships that they enter into with current and
past levels of local chemical concentration, but they
are able to enter into those informational relations
because of their participation in isomorphsims
between structures defined by ensembles of neural
states and structures defined by ensembles of
environmental states.
In brief, in order to have representational contents
that they have they must carry the information that
they do and in order to carry the information that
they do they must enter into the isomorphisms that
they do.
The evolvablity of information bearing states is due to
the isomorphisms of their embedding structures.
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