Conference PaperPDF Available

Morphognosis: the shape of knowledge in space and time

Authors:

Abstract and Figures

Artificial intelligence research to a great degree focuses on the brain and behaviors that the brain generates. But the brain, an extremely complex structure resulting from millions of years of evolution, can be viewed as a solution to problems posed by an environment existing in space and time. The environment generates signals that produce sensory events within an organism. Building an internal spatial and temporal model of the environment allows an organism to navigate and manipulate the environment. Higher intelligence might be the ability to process information coming from a larger extent of space-time. In keeping with nature's penchant for extending rather than replacing, the purpose of the mammalian neocortex might then be to record events from distant reaches of space and time and render them, as though yet near and present, to the older, deeper brain whose instinctual roles have changed little over eons. Here this notion is embodied in a model called morphognosis (morpho = shape and gnosis = knowledge). Its basic structure is a pyramid of event recordings called a morphognostic. At the apex of the pyramid are the most recent and nearby events. Receding from the apex are less recent and possibly more distant events. A morphognostic can thus be viewed as a structure of progressively larger chunks of space-time knowledge. A set of morphognostics forms long-term memories that are learned by exposure to the environment. A cellular automaton is used as the platform to investigate the morphognosis model, using a simulated organism that learns to forage in its world for food, build a nest, and play the game of Pong.
Content may be subject to copyright.
Abstract
Artificial intelligence research to a great degree fo-
cuses on the brain and behaviors that the brain gen-
erates. But the brain, an extremely complex struc-
ture resulting from millions of years of evolution,
can be viewed as a solution to problems posed by an
environment existing in space and time. The envi-
ronment generates signals that produce sensory
events within an organism. Building an internal spa-
tial and temporal model of the environment allows
an organism to navigate and manipulate the environ-
ment. Higher intelligence might be the ability to pro-
cess information coming from a larger extent of
space-time. In keeping with nature’s penchant for
extending rather than replacing, the purpose of the
mammalian neocortex might then be to record
events from distant reaches of space and time and
render them, as though yet near and present, to the
older, deeper brain whose instinctual roles have
changed little over eons. Here this notion is embod-
ied in a model called morphognosis (morpho =
shape and gnosis = knowledge). Its basic structure is
a pyramid of event recordings called a morphognos-
tic. At the apex of the pyramid are the most recent
and nearby events. Receding from the apex are less
recent and possibly more distant events. A mor-
phognostic can thus be viewed as a structure of pro-
gressively larger chunks of space-time knowledge.
A set of morphognostics forms long-term memories
that are learned by exposure to the environment. A
cellular automaton is used as the platform to inves-
tigate the morphognosis model, using a simulated
organism that learns to forage in its world for food,
build a nest, and play the game of Pong.
1 Introduction
The human brain is the seat of intelligence. Thus when we
attempt to craft intelligence, naturally we turn to it as a guide.
Fortunately, neuroscience is proceeding at an astounding
pace [Kaiser, 2014; Stetka, 2016], methodically unpacking its
mysteries. Yet the complexity of the brain, with billions of
neurons and trillions of synapses, remains daunting. Teasing
apart which aspects and features of the brain are essential to
the function of intelligence and which are incidental is a cru-
cial and difficult task. Krakauer et al. [2017] recommend that
neuroscience work takes place after the study of related be-
haviors.
Unfortunately, the prospects of understanding complex
systems through examination and dissection are questionable
[Jonas and Kording, 2016]. And as for constructing a com-
plete precise brain model, it is possible, as John von Neu-
mann believed [Mühlenbein, 2009], that at a certain level of
complexity the simplest precise description of a thing is the
thing itself. In reaction to this, some efforts, such as The Hu-
man Brain Project [2015] and Numenta [Hawkins, 2004;
White paper, 2011], have taken the position that analysis
must be complemented with synthesis and simulation to
achieve a satisfactory level of understanding.
From an artificial intelligence (AI) viewpoint, we must
keep in mind that the purpose of a brain is to allow an organ-
ism to navigate and manipulate its environment. Thus it is a
solution to problems posed by the environment. While the
earlier days of AI seemed more focused on this viewpoint,
recently neuroscience has assumed perhaps an outsized role
in directing AI, even to the extent of governmental encour-
agement [Vogelstein, 2014].
Some researchers maintain that the environment largely
consists of a body for the brain to interact with. The embodied
brain will thus leverage the sensory and motor capabilities of
a body that are adapted to an environment. Robotics research-
ers such as Brooks [1999], Hoffmann and Pfeifer [2011] have
argued that true artificial intelligence can only be achieved by
machines that have sensory and motor skills and are con-
nected to the world through a body. However, this approach
belies the problem since the body, like the brain, is also a so-
lution to its environment.
Determining a model of an organism’s environment is
more tractable than creating a brain model of an environmen-
tal model. But it requires settling on what is in the world that
produces sensory events and reacts to motor responses. Con-
founding this is that we of course must use our brains to do
this. There is a common and somewhat ironic tendency to de-
scribe AI inputs and outputs in human cognitive terms, i.e.
post-processed brain output, such as symbolic variables.
Hoffman [2009] argues that evolution has shaped our
senses and perceptual machinery to only provide information
on events that are ancestrally significant, such as finding food
Morphognosis: the shape of knowledge in space and time
Thomas E. Portegys
Ernst & Young LLP New York, NY, USA
tom.portegys@ey.com
and safety. Other events in the environment that we cannot
directly sense must be mapped through technology onto our
sensory capabilities. For example, in the age of science the
existence and use of X-rays is important, but we sense them
only indirectly, as shadows on photographic film. Indeed,
Hoffman argues that reality may be more radically alien than
we can imagine.
Epistemological offerings would seem at best too abstract
to be useful for framing a sensory-response environment, and
at worst useless, as in the cases of nihilism and solipsism.
And physics has in recent times become increasingly muddier
on the “true” nature of reality:
The arrow of time may be related to the perception of
entropy [Halliwell, 1994]
String theory demands a number of extra infinitesimal
dimensions [Rickles, 2014].
The perception of space may be a holographic projection
[Bousso, 2002].
Reality could be a cellular automaton [Wolfram, 2002],
a graph [Wolfram, 2015], or a simulation [Moskowitz,
2016].
Despite these hazards, people universally experience the
environment as a space-time structure. And even if there is a
different underlying substructure, the model is empirically ef-
fective. The presence of mammalian brain structures for map-
ping spatial events [Vorhees and Williams, 2014] provides
evidence for the processing of this type of information. Sim-
ilarly, brain structures for sensing the passage of time have
also found support [Sanders, 2015].
Using space-time as a model, it can be speculated that
higher intelligence is the ability to process information aris-
ing from a larger extent of space-time. And in keeping with
nature’s penchant for extending rather than replacing, the
purpose of the mammalian neocortex might then be to record
events from distant reaches of space and time and render
them, as though yet near and present, to the older, deeper
brain whose instinctual roles have changed little over eons. If
this is so, these structures would be repurposed to embody
language and abstract concepts.
Figure 1 Morphognostic event pyramid
Building an internal spatial and temporal model of the envi-
ronment allows an organism to navigate and manipulate the
environment. This paper introduces a model called morphog-
nosis (morpho = shape and gnosis = knowledge). Its basic
structure is a pyramid of event recordings called a morphog-
nostic, as shown in Figure 1. At the apex of the pyramid are
the most recent and nearby events. Receding from the apex
are less recent and possibly more distant events.
Morphognosis is partially inspired by an abstract morpho-
genesis model called Morphozoic [Portegys et al., 2017].
Morphogenesis is the process of generating complex struc-
tures from simpler ones within an environment. Morphozoic
is based on hierarchically nested neighborhoods within a cel-
lular automaton. Morphozoic was found to be robust and
noise tolerant in reproducing a number of morphogenesis-
like phenomena, including Turing diffusion-reaction systems
[Turing, 1952], gastrulation, and neuron pathfinding. It is
also capable of image reconstruction tasks.
2 Description
The morphognosis model is demonstrated in three 2D cellular
environments: (1) a food foraging task, (2) a nest building
task, and (3) the game of Pong. The food foraging task is used
as a venue to further define the model.
2.1 Food foraging
In this task a virtual creature called a mox finds itself in a 2D
cellular world as shown in Figure 2. To find food the mox
must navigate around various obstacles of various types (col-
ors).
Figure 2 - Mox food foraging in a 2D cellular world.
Figure 3 shows a snapshot of a morphognostic describing the
space-time events, in this case obstacle encounters, while the
mox forages. In this case a neighborhood is configured as a
3x3 set of sectors.
Cell type densities are stored instead of raw cell values to
allow linear scaling of information as the hierarchy increases
since storing individual cell values would result in a geomet-
ric growth. The cell type density is only one of a number of
possible statistical or aggregation functions that could be
used. An alternative might be to look at the distribution of
cell types as an image processing operation, such as taking a
Laplacian, Sobel or other image operator.
2.1.1 Morphognostic spatial neighborhoods
A cell defines an elementary neighborhood:
neighborhood0 = cell
Figure 3 - Pyramid of obstacle type densities arranged as hierarchy of 3x3 cell neighborhoods.
A non-elementary neighborhood consists of an NxN set of
sectors surrounding a lower level neighborhood:
neighborhoodi = NxN(neighborhoodi-1)
where N is an odd positive number.
The value of a sector is a vector representing a histogram of
the cell type densities contained within it:
value(sector) = (density(cell-type0), density(cell-type1), …
density(cell-typen))
The number of cells contributing to the density histogram of
a sector of neighborhoodi = Ni-1xNi-1
2.1.2 Morphognostic temporal neighborhoods
A neighborhood contains events that occur between time
epoch and epoch + duration:
t10 = 0
t20 = 1
t1i = t2i-1
t2i = (t2i-1 * 3) + 1
epochi = t1i
durationi = t2i - t1i
2.1.3 Metamorphs
In order to navigate and manipulate the environment, it is
necessary for an agent to be able to respond to the environ-
ment. A metamorph embodies a morphognostic→response
rule. A set of metamorphs can be learned from a manual or
programmed sequence of responses within a world.
Metamorphs establish an important feedback:
Learned morphognostics shape responses.
Responses shape the learning of morphognostics.
Metamorph “execution” consists generating a morphognos-
tic for the current mox position and orientation then finding
the closest morphognostic contained in the learned meta-
morph set, where:
 


   

 
2.1.4 Artificial neural network implementation
In a complex environment, generating a large number of met-
amorphs may be prohibitive in terms of storage and search
processing. Alternatively, metamorphs can be used to train an
artificial neural network (ANN), as shown in Figure 4, to
learn responses associated with morphognostic inputs. Dur-
ing operation, a current morphognostic can be input to the
ANN to produce a learned response. The ANN also has these
advantages:
Faster.
More compact.
More noise tolerant.
2.1.5 Results
The mox were trained in worlds featuring a number of ran-
domly placed obstacles of various types. Training was done
by “autopiloting” the mox along an optimal path to the food.
This generated a set of metamorphs suitable for testing. Table
1 shows the results of varying the neighborhood hierarchy
depth in a 10x10 world. Success indicates the mean amount
of food eaten, so 1 is a perfect score. It can be observed that
more obstacles tend to improve performance. This is because
they tend to form unique landmark configurations to guide
the mox. Larger neighborhoods also tend to improve perfor-
mance.
Figure 4 Metamorph artificial neural network.
Neighborhoods
Obstacle types
Obstacles
Food
1
1
10
0.1
1
1
20
0.2
1
2
10
0
1
2
20
0
1
4
10
0
1
4
20
0
2
1
10
0.3
2
1
20
0.4
2
2
10
0.2
2
2
20
0.6
2
4
10
0.2
2
4
20
0.6
3
1
10
1
3
1
20
0.9
3
2
10
1
3
2
20
1
3
4
10
1
3
4
20
1
Table 1 Foraging in a 10x10 world.
The next test examines how well the model performs when
the test world is not a duplicate of a training world, but is
similar to a set of training worlds. Thus for this, multiple
training runs are used. Before each training run, the cell types
of all the cells are probabilistically modified to a random
value. A successful test run must then rely on a composite of
multiple training runs. The results are shown in Table 2. Of
note is how performance only begins to falter under heavy
noise and few training runs.
.
Noise
#Train
Food
0.1
1
1
0.1
5
1
0.1
10
1
0.25
1
0.9
0.25
5
1
0.25
10
1
0.5
1
0.6
0.5
5
0.8
0.5
10
0.9
Table 2 Foraging with noise.
2.2 Nest building
This task illustrates how the morphognosis model can be used
to not only navigate but also manipulate the environment.
Figure 5 Nest building with gathered stones.
Left: scattered stones. Right completed nest.
Figure 5 left shows an environment in which a nest will be
constructed out of 4 stones (reddish circles) on top of an ele-
vation depicted by the shaded cells. The mox must seek out
the stones, pick them up, and assemble them into the com-
pleted nest shown in Figure 5 right.
For this task, the mox is capable of sensing the presence of a
stone immediately in front of it, and sensing the elevation gra-
dient both laterally and in the forward-backward direction. In
addition to the forward and turning movements used by the
foraging task, the mox is capable of picking up a stone in
front of it and dropping the stone onto an unoccupied cell in
front of it. An internal sense allows the mox to know whether
it is carrying a stone.
Training was done by running the mox through 10
repetitions on “autopilot” to build a set of metamorphs. The
environment was then reset and the mox tested to discover
whether it is capable of building the nest. Over 50 trials were
performed with 100% success. Internally, the sensory infor-
mation from the stone, gradient and stone carry states were
sufficient to achieve success with a neighborhood hierarchy
of only one level.
2.3 Pong game
Much of the real world is nondeterministic, taking the form
of unpredictable or probabilistic events that must be acted
upon. If AIs are to engage such phenomena, then they must
be able to learn how to deal with nondeterminism. In this task
the game of Pong poses a nondeterministic environment. The
learner is given an incomplete view of the game state and un-
derlying deterministic physics, resulting in a nondeterminis-
tic game. This task has been found to be challenging for con-
ventional machine learning algorithms [Portegys, 2015].
2.3.1 Game details
The goal of the game is to vertically move a paddle to prevent
a bouncing ball from striking the right wall, as shown in Fig-
ure 6.
Ball and paddle move in a cellular grid.
Unseen deterministic physics moves ball in
grid.
Cell state: (ball state, paddle state)
Ball state: (empty, present, moving
left/right/up/down)
Paddle state: (true | false)
Learner orientation: (north, south, east, west)
Responses: (wait, forward, turn right/left)
If paddle present and orientation north or south, then for-
ward response moves paddle also.
Figure 6 The game of Pong.
2.3.2 Procedure and results
Learner was trained with multiple randomly generated initial
ball velocities.
When the ball moved left and right, the learner moved
with the ball.
When the ball moved up or down, the learner moved to
the paddle and moved it up or down.
This was the challenge: remembering ball state
while traversing empty cells to the paddle so as
to move it correctly, then to turn and return to
ball for next input.
Testing on random games: 100% successful.
3 Conclusion
This is an early exploration of the morphognosis model. The
novelty of the model is both the method for integrating
knowledge of events occurring in space and time dimensions
in linear complexity, and the method of expressing the behav-
ioral interplay of responses and sensory events. The goal of
this project is to model the environment as something that
could plausibly be in turn modeled by an artificial brain.
The positive results on the three tasks prompt future
investigation. Moving up the ladder of animal intelligence,
possible next tasks include:
Web building. Can a space-time memories of building
one or more training webs allow one to be built in a
quasi-novel environment?
Food foraging social signaling. Bees retain memories of
foraging food sources that they communicate to other
bees through instinctive dancing. Can this task be cast
into the model?
The Java code is available at https://github.com/porte-
gys/MoxWorx
References
[Bousso, 2002] R. Bousso. The holographic principle. Re-
views of Modern Physics. 74 (3): 825874. arXiv:hep-
th/0203101.2002.
[Brooks, 1999] Rodney Brooks. Cambrian Intelligence: The
Early History of the New AI. Cambridge MA: The MIT
Press. ISBN 0-262-52263-2. 1999.
[Halliwell, 1994] J. J. Halliwell. Physical Origins of Time
Asymmetry. Cambridge University Press. ISBN 0-521-
56837-4. 1994.
[Hawkins, 2004] Jeff Hawkins. On Intelligence (1 ed.).
Times Books. p. 272. ISBN 0805074562. 2004.
[Hoffman, 2009] D. D. Hoffman. The interface theory of per-
ception: Natural selection drives true perception to swift
extinction. In: Object Categorization: Computer and Hu-
man Vision Perspectives. Ed.: S.J. Dickinson, A.
Leonardis, B. Schiele & M.J. Tarr. Cambridge, Cam-
bridge University Press: 148-165. 2009.
[Hoffmann and Pfeifer, 2011] M. Hoffmann and R. Pfeifer.
The implications of embodiment for behavior and cogni-
tion: animal and robotic case studies, in W. Tschacher &
C. Bergomi, ed., 'The Implications of Embodiment: Cog-
nition and Communication', Exeter: Imprint Academic,
pp. 31-58. 2011.
[Human Brain Project, 2015] Human Brain Project, Frame-
work Partnership Agreement. https://www.humanbrain-
project.eu/documents/10180/538356/FPA++An-
nex+1+Part+B/41c4da2e-0e69-4295-8e98-
3484677d661f. 2015.
[Jonas and Kording, 2016] E. Jonas and K. Kording. Could a
neuroscientist understand a microprocessor? bioRxiv
055624; doi: https://doi.org/10.1101/055624. 2016.
[Kaiser, 2014] U. B. Kaiser. Editorial: Advances in Neuro-
science: The BRAIN Initiative and Implications for Neu-
roendocrinology. Molecular Endocrinology. 28(10),
15891591. http://doi.org/10.1210/me.2014-1288. 2014.
[Krakauer, et al., 2017] J. W. Krakauer, et al. Neuroscience
Needs Behavior: Correcting a Reductionist Bias. Neuron.
Volume 93, Issue 3, pp. 480 490. 2017
[Moskowitz, 2016] C. Moskowitz. Are We Living in a Com-
puter Simulation? Scientific American. 2016.
[Mühlenbein, 2009] H. Mühlenbein. Computational Intelli-
gence: The Legacy of Alan Turing and John von Neu-
mann, in Computational Intelligence Collaboration, Fu-
sion and Emergence. Editors: Mumford, C. L. (Ed.) Vol-
ume 1 of the series Intelligent Systems Reference Library
pp 23-43. 2009.
[Numenta White paper 2011] http://numenta.org. 2011.
[Portegys et al., 2017] T. Portegys, G. Pascualy, R. Gordon,
S. McGrew, B. Alicea. Morphozoic: cellular automata
with nested neighborhoods as a metamorphic representa-
tion of morphogenesis. In Multi-Agent Based Simulations
Applied to Biological and Environmental Systems. ISBN:
978-1-5225-1756-6. 2017.
[Portegys, 2015] T. Portegys. Training Artificial Neural Net-
works to Learn a Nondeterministic Game. ICAI'15: The
2015 International Conference on Artificial Intelligence
https://arxiv.org/abs/1507.04029. 2015.
[Rickles, 2014] D. Rickles. A Brief History of String Theory:
From Dual Models to M-Theory. Springer Science &
Business Media. ISBN 978-3-642-45128-7. 2014.
[Sanders, 2015] L. Sanders. How the brain perceives time.
ScienceNews. https://www.sciencenews.org/article/how-
brain-perceives-time. 2015.
[Stetka, 2016] B. Stetka. From Psychedelics To Alzheimer's,
2016 Was A Good Year For Brain Science.
http://www.npr.org/sections/health-
shots/2016/12/31/507133144/from-psychedelics-to-alz-
heimers-2016-was-a-good-year-for-brain-science. 2016.
[Turing, 1952] A. M. Turing. The chemical basis of morpho-
genesis. Phil. Trans. Roy. Soc. London B237, 37-72.
1952.
[Vogelstein, 2014] R. J. Vogelstein. Machine Intelligence
from Cortical Networks (MICrONS) Workshop. Intelli-
gence Advanced Research Projects Activity (IARPA).
https://www.iarpa.gov/index.php/research-programs/mi-
crons. 2014.
[Vorhees and Williams, 2014] C. V. Vorhees and M. T. Wil-
liams. Assessing Spatial Learning and Memory in Ro-
dents. ILAR Journal. 55(2), 310332.
http://doi.org/10.1093/ilar/ilu013. 2014.
[Wolfram, 2002] S. Wolfram. A New Kind of Science. Wolf-
ram Media. ISBN-10: 1579550088. 2002.
[Wolfram, 2015] S. Wolfram. What Is Spacetime, Really?
Stephen Wolfram Blog. http://blog.stephenwolf-
ram.com/2015/12/what-is-spacetime-really/. 2015.
... Introduced with several prototype tasks (Portegys 2017), Morphognosis has also modeled the locomotion and foraging of the C. elegans nematode worm (Portegys 2018) and the nest-building behavior of a pufferfish (Portegys 2019). Morphognosis is a temporal extension of a spatial model of morphogenesis ). ...
Chapter
Full-text available
Honey bees are social insects that forage for flower nectar cooperatively. When an individual forager discovers a flower patch rich in nectar, it returns to the hive and performs a “waggle dance” in the vicinity of other bees that consists of movements communicating the direction and distance to the nectar source. The dance recruits “witnessing” bees to fly to the location of the nectar to retrieve it, thus cooperatively exploiting the environment. Replicating such complex animal behavior is a step forward on the path to artificial intelligence. This project simulates the bee foraging behavior in a cellular automaton using the Morphognosis machine learning model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. Given a set of bee foraging and dancing exemplars, and exposing only the external input-output of these behaviors to the Morphognosis learning algorithm, a hive of artificial bees can be generated that forage as their biological counterparts do. A comparison of Morphognosis foraging performance with that of an artificial recurrent neural network is also presented.KeywordsHoney bee foraging danceMorphognosisArtificial animal intelligenceArtificial neural networkMachine learningArtificial life
... Introduced with several prototype tasks (Portegys 2017), Morphognosis has also modeled the locomotion and foraging of the C. elegans nematode worm (Portegys 2018) and the nest-building behavior of a pufferfish (Portegys 2019). Morphognosis is a temporal extension of a spatial model of morphogenesis ). ...
Preprint
Full-text available
A bstract Honey bees are social insects that forage for flower nectar cooperatively. When an individual forager discovers a flower patch rich in nectar, it returns to the hive and performs a “dance” in the vicinity of other bees that consists of movements communicating the direction and distance to the nectar source. The bees that receive this information then fly to the location of the nectar to retrieve it, thus cooperatively exploiting the environment. This project simulates this behavior in a cellular automaton using the Morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. Given a set of bee foraging and dancing exemplars, and exposing only the external input-output of these behaviors to the Morphognosis learning algorithm, a hive of artificial bees can be generated that forage as their biological counterparts do.
... These concepts are embodied in a model called Morphognosis (morpho = shape and gnosis = knowledge). Introduced with several prototype tasks (Portegys, 2017), Morphognosis has also modeled the locomotion and foraging of the C. elegans nematode worm (Portegys, 2018). ...
Preprint
Full-text available
A species of pufferfish builds fascinating circular nests on the sea floor to attract mates. This project simulates the nest building behavior in a cellular automaton using the Morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. By considering the biological neural network of the pufferfish as a black box, decomposing only its external behavior, an artificial counterpart can be generated. In this way a complex biological system producing a behavior can be filtered into a system containing only functions that are essential to reproduce the behavior. The derived system not only has intrinsic value as an artificial entity but also might help to ascertain how the biological system produces the behavior.
Article
Full-text available
A species of pufferfish builds fascinating circular nests on the sea floor to attract mates. This project simulates the nest building behavior in a cellular automaton using the morphognosis model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. By considering the biological neural network of the pufferfish as a black box, and decomposing only its external behavior, an artificial counterpart can be generated. In this way a complex biological system producing a behavior can be filtered into a system containing only functions that are essential to reproduce the behavior. The derived system not only has intrinsic value as an artificial entity but also might help to ascertain how the biological system produces the behavior.
Poster
Full-text available
For decades, the tiny nematode worm C. elegans has been and continues to be a fount of biological information. Like all living things, this relatively simple creature survives and propagates in an environment that is fundamentally structured in space and time. The environment generates sequences of stimuli that produce sensory events within an organism and the organism responds to spatially navigate and manipulate the environment. Thus space and time are universalities that all living things must in some way reflect and represent. In biological research, the general aim is to discover how a species functions in some way. For this project, however, the behavior of C. elegans is analyzed with the aim of testing the applicability of an underlying common informational mechanism that models aspects of animal behaviors in general. The model is called morphognosis (morpho = shape and gnosis = knowledge). Its basic structure is a pyramid of event recordings. At the apex of the pyramid are the most recent and nearby events. Receding from the apex are less recent and possibly more distant aggregated events. In this project, the locomotion and foraging behaviors of C. elegans are learned and reproduced by morphognosis. A cellular automaton implements morphognosis, meaning that the locomotion and foraging behaviors are decomposed into cellular automaton rules which are then learned by an artificial neural network that is capable of generalizing to handle novel environmental stimuli.
Article
Full-text available
There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.
Chapter
Full-text available
A cellular automaton model, Morphozoic, is presented. Morphozoic may be used to investigate the computational power of morphogenetic fields to foster the development of structures and cell differentiation. The term morphogenetic field is used here to describe a generalized abstraction: a cell signals information about its state to its environment and is able to sense and act on signals from nested neighborhood of cells that can represent local to global morphogenetic effects. Neighborhood signals are compacted into aggregated quantities, capping the amount of information exchanged: signals from smaller, more local neighborhoods are thus more finely discriminated, while those from larger, more global neighborhoods are less so. An assembly of cells can thus cooperate to generate spatial and temporal structure. Morphozoic was found to be robust and noise tolerant. Applications of Morphozoic presented here include: (1) Conway's Game of Life, (2) cell regeneration, (3) evolution of a gastrulation-like sequence, (4) neuron pathfinding, and (5) Turing's reaction-diffusion morphogenesis.
Technical Report
There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods. Author Summary Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used na•vely, fall short of producing a meaningful understanding.
Article
There are ever more compelling tools available for neuroscience research, ranging from selective genetic targeting to optogenetic circuit control to mapping whole connectomes. These approaches are coupled with a deep-seated, often tacit, belief in the reductionist program for understanding the link between the brain and behavior. The aim of this program is causal explanation through neural manipulations that allow testing of necessity and sufficiency claims. We argue, however, that another equally important approach seeks an alternative form of understanding through careful theoretical and experimental decomposition of behavior. Specifically, the detailed analysis of tasks and of the behavior they elicit is best suited for discovering component processes and their underlying algorithms. In most cases, we argue that study of the neural implementation of behavior is best investigated after such behavioral work. Thus, we advocate a more pluralistic notion of neuroscience when it comes to the brain-behavior relationship: behavioral work provides understanding, whereas neural interventions test causality.
Article
The jewel beetle Julodimorpha bakewelli is category challenged (Gwynne and Rentz 1983; Gwynne 2003). For the male of the species, spotting instances of the category “desirable female” is a pursuit of enduring interest and, to this end, he scours his environment for telltale signs of a female's shiny, dimpled, yellow-brown elytra (wing cases). Unfortunately for him, many males of the species Homo sapiens, who sojourn in his habitats within the Dongara area of Western Australia, are attracted by instances of the category “full beer bottle” but not by instances of the category “empty beer bottle,” and are therefore prone to toss their emptied bottles (stubbies) unceremoniously from their cars. As it happens, stubbies are shiny, dimpled, and just the right shade of brown to trigger, in the poor beetle, a category error. Male beetles find stubbies irresistible. Forsaking all normal females, they swarm the stubbies, genitalia everted, and doggedly try to copulate despite repeated glassy rebuffs. Compounding misfortune, ants of the species Iridomyrmex discors capitalize on the beetles' category errors; the ants sequester themselves near stubbies, wait for befuddled beetles, and consume them, genitalia first, as they persist in their amorous advances. Categories have consequences. Conflating beetle and bottle led male J. bakewelli into mating mistakes that nudged their species to the brink of extinction. © Sven J. Dickinson, Ales Leonardis, Bernt Schiele, and Michael J. Tarr 2009.
Article
Maneuvering safely through the environment is central to survival of almost all species. The ability to do this depends on learning and remembering locations. This capacity is encoded in the brain by two systems: one using cues outside the organism (distal cues), allocentric navigation, and one using self-movement, internal cues and nearby proximal cues, egocentric navigation. Allocentric navigation involves the hippocampus, entorhinal cortex, and surrounding structures; in humans this system encodes allocentric, semantic, and episodic memory. This form of memory is assessed in laboratory animals in many ways, but the dominant form of assessment is the Morris water maze (MWM). Egocentric navigation involves the dorsal striatum and connected structures; in humans this system encodes routes and integrated paths and, when overlearned, becomes procedural memory. In this article, several allocentric assessment methods for rodents are reviewed and compared with the MWM. MWM advantages (little training required, no food deprivation, ease of testing, rapid and reliable learning, insensitivity to differences in body weight and appetite, absence of nonperformers, control methods for proximal cue learning, and performance effects) and disadvantages (concern about stress, perhaps not as sensitive for working memory) are discussed. Evidence-based design improvements and testing methods are reviewed for both rats and mice. Experimental factors that apply generally to spatial navigation and to MWM specifically are considered. It is concluded that, on balance, the MWM has more advantages than disadvantages and compares favorably with other allocentric navigation tasks.
Article
In the world about us, the past is distinctly different from the future. More precisely, we say that the processes going on in the world about us are asymmetric in time or display an arrow of time. Yet this manifest fact of our experience is particularly difficult to explain in terms of the fundamental laws of physics. Newton's laws, quantum mechanics, electromagnetism, Einstein's theory of gravity, etc., make no distinction between past and future - they are time-symmetric. Reconciliation of these profoundly conflicting facts is the topic of this volume. It is an interdisciplinary survey of the variety of interconnected phenomena defining arrows of time, and their possible explanations in terms of underlying time-symmetric laws of physics.