1. Cognitive Computing
From Brain Modelling to Large
Scale Machine Learning
Dariusz Plewczynski, PhD
ICM, University of Warsaw
D.Plewczynski@icm.edu.pl

2. How the brain works?
P. Latham
P. Dayan

3. Simulating the Brain
neuron introduced by Heinrich von Waldeyer-Hartz 1891
http://en.wikipedia.org/wiki/Neuron

4. Simulating the Brain
synapse introduced by Charles Sherrington 1897
http://en.wikipedia.org/wiki/Synapse

5. How the brain works?
Cortex numbers:
1 mm^2
1 mm3 of cortex:
50,000 neurons
10,000 connections/neuron
(=> 500 million connections)
4 km of axons
whole brain (2 kg):
1011 neurons
1015 connections
8 million km of axons
P. Latham
P. Dayan

6. How the brain really learns?
Time & Learning:
• You have about 1015 synapses.
• If it takes 1 bit of information to set a synapse, you need 1015
bits to set all of them.
• 30 years ≈ 109 seconds.
• To set 1/10 of your synapses in 30 years, you must absorb
100,000 bits/second.
Learning in the brain is almost completely unsupervised!
P. Latham
P. Dayan

7. Neurons: Models
E. Izhikevich

8. Levels of details
E. Izhikevich

9. Something Smaller ...
Mammalian thalamo-cortical
System by E. Izhikevich:
The simulation of a model that has the size of the human
brain: a detailed large-scale thalamocortical model based
on experimental measures in several mammalian species.
The model exhibits behavioral regimes of normal brain activity
that were not explicitly built-in but emerged spontaneously as the
result of interactions among anatomical and dynamic processes. It
describes spontaneous activity, sensitivity to changes in individual
neurons, emergence of waves and rhythms, and functional
connectivity on different scales.
E. Izhikevich

10. ...and Less Complicated
E. Izhikevich

11. ... and Even More ...
E. Izhikevich

12. Smaller?
Large-Scale Model of
Mammalian Thalamocortical System
The model has 1011 neurons and almost 1015 synapses.
It represents 300x300 mm2 of mammalian thalamo-cortical
surface, and reticular thalamic nuclei, and spiking neurons with
firing properties corresponding to those recorded in the
mammalian brain.
The model simulates one million multicompartmental spiking
neurons calibrated to reproduce known types of responses
recorded in vitro in rats. It has almost half a billion synapses with
appropriate receptor kinetics, short-term plasticity, and long-term
dendritic spike-timing-dependent synaptic plasticity (dendritic
STDP). E. Izhikevich

13. Why?
<<Why did I do that?>>
“Indeed, no significant contribution to neuroscience could
be made by simulating one second of a model, even if it
has the size of the human brain. However, I learned what it
takes to simulate such a large-scale system.
Implementation challenges:
Since 2^32 < 10^11, a standard integer number cannot
even encode the indices of all neurons.
To store all synaptic weights, one needs 10,000 terabytes.
How was the simulation done? Instead of saving synaptic
connections, I regenerated the anatomy every time step (1
ms).” E. Izhikevich

14. Time ...
<<Why did I do that?>>
“Question: When can we simulate the human brain in real
time?
Answer: The computational power to handle such a
simulation will be available sooner than you think. “
His benchmark:
1 sec = 50 days on 27 3GHz processors
“However, many essential details of the anatomy and
dynamics of the mammalian nervous system would
probably be still unknown.”
Size doesn't matter; it's what you put into your model and
how you embed it into the environment (to close the loop).
E. Izhikevich

15. ... and Space
Spiking activity of the human brain
model
Connects three drastically different scales:
o It is based on global (white-matter) thalamocortical anatomy
obtained by means of diffusion tensor imaging (DTI) of a human
brain.
o It includes multiple thalamic nuclei and six-layered cortical
microcircuitry based on in vitro labeling and three-dimensional
reconstruction of single neurons of cat visual cortex.
o It has 22 basic types of neurons with appropriate laminar
distribution of their branching dendritic trees.
E. Izhikevich

16. Less Complicated?
Spiking activity of the human brain
model
Connects three drastically different scales:
o Single neurons with branching dendritic morphology
(pyramidal, stellate, basket, non-basket, etc.); synaptic dynamics with
GABA, AMPA, and NMDA kinetics, short-term and long-term synaptic
plasticity (in the form of dendritic STDP); neuromodulation of plasticity by
dopamine; firing patterns representing 21 basic types found in the rat cortex
and thalamus (includes Regular Spiking, Intrinsically
Bursting, Chattering, Fast Spiking, Late Spiking, Low-Threshold
Spiking, Thamamic Bursting, etc., types);
o 6-Layer thalamo-cortical microcircuitry based on quantitative anatomical
studies of cat cortex (area 17) and on anatomical studies of thalamic
circuitry in mammals;
o Large-scale white-matter anatomy using human DTI (diffusion tensor
imaging) and fibertraking methods.
E. Izhikevich

17. so - is it less complicated?
E. Izhikevich

18. Less Complicated?
E. Izhikevich

19. Goal
Large-Scale Computer model of
the whole human brain
“This research has a more ambitious goal than the the Blue Brain
Project conducted by IBM and EPFL (Lausanne, Switzerland).
The Blue Brain Project builds a small part of the brain that
represents a cortical column, though to a much greater detail than
the models I develop. In contrast, my goal is a large-scale
biologically acurate computer model of the whole human brain. At
present, it has only cortex and thalamus; other subcortical
structures, including hippocampus, cerebellum, basal
ganglia, etc., will be added later. Spiking models of neurons in
these structures have already being developed and fine-tuned.”
E. Izhikevich

20. Another Approach: Cortical Simulator
C2 Simulator IBM Research
D. Modha

21. Cortical Simulator: C2 simulator
C2 Simulator incorporates:
1.Phenomenological spiking neurons by Izhikevich, 2004;
2.Phenomenological STDP synapses model of spike-timing
dependent plasticity by Song, Miller, Abbot, 2000;
3.axonal conductance delays 1-20 ms;
4.80% excitatory neurons and 20% inhibitory neurons;
5.a certain random graph of neuronal interconnectivity, like
Mouse-scale by Braitenberg & Schuz, 1998:
 16x106 neurons
 8x103 synapses per neuron
 0.09 local probability of connection
D. Modha

22. Cortical Simulator: Connectivity
Micro Anatomy
Gray matter, short-distance, statistical/random;
• Binzeggar, Douglas, Martin, 2004 (cat anatomy);
• 13% I + 87% E with 77% E->E; 10% E->I; 11%I->E; 2% I->I
Macro Anatomy
White matter, long-distance, specific;
• CoCoMac www.cocomac.org (Collations of Connectivity data
on the Macaque brain);
• 413 papers;
• two relationships: subset (20,000) and connectivity (40,000)
• roughly 1,000 areas with 10,000 connections:
The first neuro-anatomical graph of this size
D. Modha
Emerging dynamics, small world phenomena

23. Cortical Simulator
Cortical network puts together neurons connected via an
interconnections. The goal: to understand the information
processing capability of such networks.
1. For every neuron:
a. For every clock step (say 1 ms):
i. Update the state of each neuron
ii. If the neuron fires, generate an event for each synapse that
the neuron is post-synaptic to and pre-synaptic to.
2. For every synapse:
When it receives a pre- or post-synaptic event,
update its state and, if necessary, the state of the
post-synaptic neuron
D. Modha

24. Cortical Simulator in action
D. Modha

25. Cortical Simulator in action
C2 Simulator is demonstrating how information percolates and
propagates. It is NOT learning!
D. Modha

26. yet, Cortical Simulator is not Brain
The cortex is an
analog, asynchronous, parallel, biophysical, fault-tolerant, and
distributed memory machine. Therefore, a biophysically-realistic
simulation is NOT the focus of C2!
The goal is to simulate only those details that lead us towards
insights into brain's high-level computational principles. C2
represents one logical abstraction of the cortex that is suitable
for Its simulation on modern distributed memory
multiprocessors.
Simulated high-level principles will hopefully guide us to novel
cognitive systems, computing architectures, programming
paradigms, and numerous practical applications.
D. Modha

27. Brain: Physical Structure
P. Latham
P. Dayan

28. Brain: Connectivity
Red - Interhemispheric fibers projecting
between the corpus callosum and frontal
cortex.
Green - Interhemispheric fibers projecting
between primary visual cortex and the
corpus callosum.
Yellow - Interhemispheric fibers projecting
from corpus callosum and not Red or Green.
Brown - Fibers of the superior longitudinal
fasciculus, connecting regions critical for
language processing.
Orange - Fibers of inferior longitudinal
fasciculus and uncinate fasciculus,
connecting regions to cortex responsible for
memory.
Purple - Projections between parietal lobe
and lateral cortex
Blue - Fibers connecting local regions of the
frontal cortex
D. Modha

29. but what about Brain in Action?
P. Latham
P. Dayan

30. Very Long Story: Artificial Intelligence
Q. What is artificial intelligence?
A. It is the science and engineering of
making intelligent machines, especially
intelligent computer programs. It is related
to the similar task of using computers to
understand human intelligence, but AI does
not have to confine itself to methods that
are biologically observable.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of
the ability to achieve goals in the world.
Varying kinds and degrees of intelligence
occur in people, many animals and some
machines.
Q. Isn't there a solid definition of
intelligence that doesn't depend on relating
it to human intelligence?
A. Not yet.
John McCarthy
http://en.wikiversity.org/wiki/Artificial_Intelligence

31. Less Ambitious: Machine Learning
The goal of machine learning is to build computer systems that can
adapt and learn from their experience.
Different learning techniques have been developed for different
performance tasks.
The primary tasks that have been investigated are SUPERVISED
LEARNING for discrete decision-making, supervised learning for
continuous prediction, REINFORCEMENT LEARNING for sequential
decision making, and UNSUPERVISED LEARNING
T. Dietterich

32. Machine Learning
Problems:
• High dimensionality of data;
• Complex rules;
• Amount of data;
• Automated data processing;
• Statistical analysis;
• Pattern construction;
Examples:
• Support Vector Machines
• Artificial Neural Networks
• Boosting
• Hidden Markov Models
• Random Forest
• Trend Vectors ...

33. Machine Learning Tasks
Learning Task
• Given: Set of cases verified by experiments to be positives
and the given set of negatives
• Compute: A model distinguishing if an item has prefered
characteristic or not
Classification Task
• Given: calculated characteristics of a new case + a learned
model
• Determine: If new case is a positive or not.
black box Predictive
Training data (learning machine) Model
Model

34. Machine Learning Tasks
Training data
• GivenCases with known class membership (Positives) +
background preferences (Negatives)
Model
• The model which can distinguish between active and non-
active cases. Can be used for prediction.
Knowledge
• Rules and additional information (databases and ontologies)
Training data Acquired
Discovery Model
Process Knowledge
Existing
Knowledge Emad, El-Sebakhy: Ensemble Learning
Learning

35. A Single Model
Goal is always to minimize the probability of model errors
on future data!
Motivation - build a single good model.
• Models that don’t adhere to Occam’s razor:
o Minimax Probability Machine (MPM)
o Trees
o Neural Networks
o Nearest Neighbor
o Radial Basis Functions
• Occam’s razor models: The best model is the simplest one!
o Support Vector Machines
o Bayesian Methods G. Grudic
o Other kernel based methods (Kernel Maching Pursuit ...)

36. An Ensemble of Models
Motivation - a good single model is difficult to compute
(impossible?), so build many and combine them. Combining
many uncorrelated models produces better predictors...
• don’t use randomness or use directed randomness:
o Boosting
o Specific cost function
o Gradient Boosting
o Derive a boosting algorithm for any cost function
• Models that incorporate randomness:
o Bagging: Bootstrap Sample by uniform random sampling
o Stochastic Gradient Boosting: Bootstrap Sample: Uniform
random sampling (with replacement)
o Random Forests uniform random sampling & randomize Grudic
G.
inputs

37. Machine Learning Software
• WEKA
 University of Waikato, New Zealand
• YALE • RAPIDMINER (->YALE)
 University of Dortmund, Germany  Rapid-I GmbH
• MiningMart • SCRIPTELLA ETL
 University of Dortmund, Germany  ETL (Extract-Transform-Load)
• Orange • JAVA MACHINE LEARNING LIBRARY
 University of Ljubljana, Slovenia  machine learning library
• Rattle • IBM Intelligent MINER
 Togaware  IBM
• AlphaMiner • MALIBU
 University of Hong Kong  University of Illinois
• Databionic ESOM Tools • KNime
 University of Marburg, Germany  University of Konstanz
• MLC++ • SHOGUN
 SGI, USA  Friedrich Miescher Laboratory
• MLJ • ELEFANT
 Kansas State University  National ICT Australia
• BORGELT • PLearn
 University of Magdeburg, Germany  University of Montreal
• GNOME DATA MINE o TORCH
 Togaware  Institut de Recherche Idiap
• TANAGRA • PyML/MLPy
 University of Lyon 2  Colorado State University /Fondazione Bruno Kessler
• XELOPES
 Prudsys
• SPAGOBI
 ObjectWeb, Italy
• JASPERINTELLIGENCE
 JasperSoft http://mloss.org/
• PENTAHO
 Pentaho

38. Consensus Learning
Consensus Approaches in Machine Learning
Consensus: A general agreement about a matter of opinion.
Learning: Knowledge obtained by study.
Machine Learning: Discover the relationships between the
variables of a system (input, output and hidden) from direct
samples of the system.
Brainstorming: A method of solving problems in which all the
members of a group suggest ideas and then discuss them (a
brainstorming session).
Oxford Advanced Learner’s Dictionary

39. Consensus Learning
Motivation
Distributed artificial intelligence (DAI): the strong need to equip
multi-agent systems with a learning mechanism so as to adapt
them to a complex environment;
Goal: improve the problem-solving abilities of individual agents
and the overall system in complex multi-agent systems.
Using learning approach, agents can exchange their ideas with
each other and enrich their knowledge about the domain
problem;
Solution: the consensus learning approach. Oxford Advanced Learner’s Dictionary

40. Consensus Learning
diferent data representations + ensemble of ML algorithms
Build the expert system using various input data representations
& different learning algorithms:
• Support Vector Machines
• Artificial Neural Networks
• Boosting
• Hidden Markov Models
• Decision Trees
• Random Forest
• ….

41. Brainstorming
INPUT: objects & features
Representations
annotations generation
Similarity Text External External External
Structure Properties
PCA/ICA Mining Tools Annotations Databases …
SQL Training Database
feature
decomposition
Scores, Structural Features
Thresholds Similarity Similarity
OUTPUT:
Support Model
Neural Random Decision
Vector Features
Networks Forest Trees
Machine Decision
Reliability Score
machine learning
MLcons
consensus

42. Brainstorming
Advantages
• Statistical If training data is very small, there are many
hypotheses which describe it. Consensus reduces the
chance of selecting a bad classifier.
• Computational Each ML might get stuck in local minima of
training errors. Consensus reduces the chance of getting
stuck in wrong minima.
• Representational Consensus may represent a complex
classifier which was not possible to formulate in the original
set of hypotheses.

43. Brainstorming
Uwe Koch &
Stephane Spieser
Pawel G Sadowski, Tom
Dariusz Plewczyński Kathryn S Lilley
darman@icm.edu.pl Marcin von Grotthuss
Krzysztof Ginalski
kginal@icm.edu.pl
Leszek Rychlewski
Adrian Tkacz
Jan Komorowski & Marcin
Kierczak
Lucjan Wyrwicz

44. Back to the Brain
Cortical simulator puts together neurons connected via an
interconnections. The goal: to understand the information
processing capability of such networks.
1. For every neuron:
a. For every clock step (say 1 ms):
i. Update the state of each neuron
ii. If the neuron fires, generate an event for each synapse that
the neuron is post-synaptic to and pre-synaptic to.
2. For every synapse:
When it receives a pre- or post-synaptic event,
update its state and, if necessary, the state of the
post-synaptic neuron
D. Modha

45. Cognitive Network
Cognitive network CN similarly puts together single learning
units connected via an interconnections.
The goal: to understand the learning as the spatiotemporal
information processing and storing capability of such networks.
1. For every LU (learning unit):
a. For every clock step:
i. Update the state of each LU if data is changing
ii. If the LU retraining was performed with success, generate
an event for each coupling that the LU is
post-event coupled to and pre-event coupled to.
2. For every TC (time coupling):
When it receives a pre- or post-event,
update its state and, if necessary, the state of the
post-event LUs

46. Spatial Cognitive Networks
Cellular automata has only two states and do not retrain the
individual LUs
Each cell in cellular automata static model represent single
machine learning algorithm, or certain combination of
parameters, and optimization conditions affecting the
classification output of this particular method.

47. Spatial Cognitive Networks
I call this single learner by the term “learning agent”, each
characterized for example by its prediction quality on a selected
training dataset.
The coupling of individual learners is described by short-
, medium- or long-range interaction strength, so called learning
coupling.
The actual structure, or topology of coupling between various
learners is described using term “learning space”, and can
have different representations, such as Cartesian space, fully
connected or hierarchical geometry.
The result of the evolution, dynamics of such system given by its
stationary state is defined here as the consensus equilibration.
The majority of learners define, in the stationary limit, the
“learning consensus” outcome of the meta-learning procedure.

48. Spatial Cognitive Networks
1) Binary Logic
I assume the binary logic of individual learners, i.e. we deal
with cellular automata consisting of N agents, each holding
one of two opposite states (“NO” or “YES”). These states are
binary , similarly to Ising model of ferromagnet. In most cases
the machine learning algorithms that can model those
agents, such as support vector machines, decision
trees, trend vectors, artificial neural networks, random
forest, predict two classes for incoming data, based on
previous experience in the form of trained models. The
prediction of an agent answers single question: is a query
data contained in class A (“YES”), or it is different from items
gathered in this class (“NO”).

49. Spatial Cognitive Networks
2) Disorder and random strength parameter
Each learner is characterized by two random parameters:
persuasiveness and supportiveness that describe how
individual agent interact with others. Persuasiveness
describes how effectively the individual state of agent is
propagated to neighboring agents, whereas supportiveness
represent self-supportiveness of single agent.

50. Spatial Cognitive Networks
3) Learning space and learning metric
Each agent is characterized by a location in the learning
space, therefore one can calculate the abstract learning
distance of two learners i and j.
The strength of coupling between two agents tend to
decrease with the learning distance between them.
Determination of the learning metric is a separate
problem, and the particular form of the metric and the
learning distance function should be empirically
determined, and in principle can be a very peculiar geometry.
For example: the fully connected learning space, where all
distances between agents are equal usefull in the case of
simple consensus between different, yet not organized
machine learning algorithms.

51. Spatial Cognitive Networks
4) Learning coupling
Agents exchange their opinions by biasing others toward
their own classification outcome. This influence can be
described by the total learning impact I that ith agent is
experiencing from all other learners.
Within the cellular automata approach this impact is the difference
between positive coupling of those agents that hold identical
classification outcome, relative to negative influence of those who
share opposite state, and can be formalized as: where Ip and Is are
the functions of persuasiveness and supportiveness impact of the
other agents on the i-th agent.

52. Spatial Cognitive Networks
5) Meta-Learning equations
The equation of dynamics of the learning model defines the
state of ith individual at the next time step as follows:
with rescaled learning influence:
I assume a synchronous dynamics, i.e. states of all agents
are updated in parallel. In comparison to standard Monte
Carlo methods the synchronous dynamics takes shorter time
to equilibrate than serial methods, yet it can be trapped into
periodic asymptotic states with oscillations between
neighboring agents.

53. Spatial Cognitive Networks
6) Presence of noise
The randomness of state change (phenomenological
modeling of various random elements in the learning
system, and training data) is given by introducing noise into
dynamics:
where h is the site-dependent white noise, or a uniform white
noise. In the first case h are random variables independent
for different agents and time instants, whereas in the second
case h are independent for different time instants.

54. Back to AI Cognitive Networks
Cognitive networks CN are inspired by
brain structure and performed cognitive functions
CN put together single machine learning units connected via an
interconnections. The goal is to understand the learning as the
spatiotemporal information processing and storing capability of
such networks (Meta-Learning!).
1. Space: for every LU (learning unit):
a. For every time step:
i. Update the state of each LU using changed training data
ii. If the LU learning was performed with success,
generate an event for each coupling that the LU is
post-event coupled to and pre-event coupled to.
2. Time: For every TC (time coupling):
When it receives a pre- or post-event,

55. and Cortical Simulator
Cortical simulator puts together neurons connected via an
interconnections. The goal: to understand the information
processing capability of such networks.
1. For every neuron:
a. For every clock step (say 1 ms):
i. Update the state of each neuron
ii. If the neuron fires, generate an event for each synapse that
the neuron is post-synaptic to and pre-synaptic to.
2. For every synapse:
When it receives a pre- or post-synaptic event,
update its state and, if necessary, the state of the
post-synaptic neuron
D. Modha

56. both are Information Processing
NETWORKS
Network puts together single units connected via an
interconnections. The goal is to understand the information
processing of such network, as well as the spatiotemporal
characteristics and ability to store information in such networks
(Learning).

57. Network: technical challenges
Memory constrains to achieve near real-time simulation
times, the state of all learning units LUs and time couplings TCs
must fit in the random access memory of the system.
In real brain synapses far outnumber the neurons, therefore the
total available memory divided by the number of bytes per
synapse limits the number of synapses that can be modeled.
The rat-scale model (~55 million neurons, ~450 billion
synapses) needs to store state for all synapses and neurons
where later being negligible in comparison to the former.
D. Modha

58. Network: technical challenges
Communication constrains that is on an average, each LU fires
once a second.
Each neuron connects to roughly 8,000 other neurons, and,
therefore each neuron would generate 8,000 spikes
(“messages" or so called neurobits) per second.
This amounts to a total of 448 billion messages/neurobits per
second.
D. Modha

59. Network: technical challenges
Computation constrains: on an average, each LU fires once a
second.
In brain modelling, on an average, each synapse would be
activated twice: once when its pre-synaptic neuron fires and
once when its post-synaptic neuron fires.
This amounts to 896 billion synaptic updates per second.
Let us assume that the state of each neuron is updated every
millisecond. This amounts to 55 billion neuronal updates per
second. Synapses seem to dominate the computational cost.
D. Modha

60. Network: hardware
Hardware solution: a state-of-the-art supercomputer
BlueGene/L with 32,768 CPUs, 256MB of memory per
processor (8 TB in total), and 1.05GB/sec of in/out
communication bandwidth per node.
To meet the above three constraints, one has to design data
structure and algorithms that require no more than 16 bytes of
storage per TC, 175 Flops per TC per second, and 66 bytes per
neurobit (spike message).
A rat-scale brain, near real-time learning network!
D. Modha

61. Network: software
Software solution: a massively parallel learning network
simulator, LN, that runs on distributed memory multiprocessors.
Algorithmic enhancements:
1. a computationally efficient way to simulate learning units in a
clock-driven ("synchronous") and couplings in an event-
driven ("asynchronous") fashion;
2. a memory efficient representation to compactly represent the
state of the simulation;
3. a communication efficient way to minimize the number of
messages sent by aggregating them in several ways and by
mapping message exchanges between processors onto
judiciously chosen MPI primitives for synchronization.
D. Modha

62. Computational Size of the problem
BlueGene/L can simulate the 1 sec of model in 10 sec at 1Hz
firing rate and 1ms simulation resolution using random stimulus.
MOUSE HUMAN BlueGene/L
Neurons 2x8x106 2x50x109 3x104
CPUs
Synapses 128x109 1015 109
CPUs pairs
Communication 128x109 1015 1.05 GB/sec
(66 B/spike) Spikes/sec Spikes/sec 64x109 b/sec
in/out
Computation 2x128x109 2x1015 45 TF
(350 synapse/sec synapse/sec 4x1013F
F/synapse/sec) 8,192 CPUs
Memory O(128x109) O(1015) 4 TB
(32 B/synapse) O(64x1012)
(D. Modha: assuming neurons fire at 1Hz)

63. D. Modha
Progress in large-scale cortical simulations

64. IBM BlueGene
D. Modha

65. D. Modha
Future of cortical simulations

66. D. Modha
C2 Simulator vs Human Brain:
The human cortex has about 22 billion neurons which is
roughly a factor of 400 larger than the rat-scale model
which has 55 million neurons. They used a BlueGene/L
with 92 TF and 8 TB to carry out rat-scale simulations in
near real-time [one tenth speed].
So, by naïve extrapolation, one would require at least a
machine with a computation capacity of 36.8 PF and a
memory capacity of 3.2 PB. Furthermore, assuming that
there are 8,000 synapses per neuron, that neurons fire at
an average rate of 1 Hz, and that each spike message can
be communicated in, say, 66 Bytes. One would need an
aggregate communication bandwidth of ~ 2 PBps. D. Modha

67. SyNAPSE
The goal is to create new
electronics hardware and
architecture that can
understand, adapt and respond to
an informative environment in ways
that extend traditional computation
to include fundamentally
different capabilities found in
biological brains.
Stanford University: Brian A. Wandell, H.-S. Philip Wong
Cornell University: Rajit Manohar
Columbia University Medical Center: Stefano Fusi
University of Wisconsin-Madison: Giulio Tononi
University of California-Merced: Christopher Kello
IBM Research: Rajagopal Ananthanarayanan, Leland Chang, Daniel
Friedman, Christoph Hagleitner, Bulent Kurdi, Chung Lam, Paul
Maglio, Stuart Parkin, Bipin Rajendran, Raghavendra Singh

68. NeuralEnsemble.org
NeuralEnsemble is a multilateral
effort to coordinate and organize
Neuroscience software
development efforts into a larger
meta-simulator software system, a
natural and alternate approach to
incrementally address what is
known as the complexity
bottleneck, presently a major
roadblock for neural modelling.
"Increasingly, the real limit on what computational
scientists can accomplish is how quickly and reliably
they can translate their ideas into working code."
Gregory V. Wilson, Where's the Real Bottleneck in
Scientific Computing?

69. Emergent
A comprehensive, full-featured neural network simulator that
allows for the creation and analysis of complex, sophisticated
models of the brain.
It has high level drag-and-drop programming interface, built on
top of a scripting language that has full introspective access to
all aspects of networks and the software itself, allows one to
write programs that seamlessly weave together the training of
a network and evolution of its environment without ever typing
out a line of code.
Networks and all of their state variables are visually inspected
in 3D, allowing for a quick "visual regression" of network
dynamics and robot behavior.

70. Cognitive Computers The Future
Today algorithms and computers deals with structured data
(age, salary, etc.) and semi-structured data (text and web
pages).
No mechanisms exists that is able to act in a context-
dependent fashion while integrating ambiguous information
across different senses (sight, hearing, touch, taste, and
smell) and coordinating multiple motor modalities.
Cognitive computing targets the boundary between digital and
physical worlds where raw sensory information abounds.
For example, while instrumenting the outside world’s with some sensors, and
streaming this information in the real-time manner to a cognitive computer that
may be able to detect spatio-temporal correlations.

71. Niels Bohr
Predicting is very difficult
especially about the
future…

72. Brainstorming
Uwe Koch &
Stephane Spieser
Pawel G Sadowski, Tom
Dariusz Plewczyński Kathryn S Lilley
darman@icm.edu.pl Marcin von Grotthuss
Krzysztof Ginalski
kginal@icm.edu.pl
Leszek Rychlewski
Adrian Tkacz
Jan Komorowski & Marcin
Kierczak
Lucjan Wyrwicz