1. Cognitive Computing…. Computational Neuroscience
Jerome Swartz
The Swartz Foundation
May 10, 2006

2. Large Scale Brain Modeling
Science IS modeling
Models have power
To explain
To predict
To simulate
To augment
Why model the brain?

3. Brains are not computers …
But they are supported by the same physics
Energy conservation
Entropy increase
Least action
Time direction
Brains are supported by the same logic,
but implemented differently…
Low speed; parallel processing; no symbolic software layer; fundamentally adaptive / interactive; organic vs. inorganic

4. Brain research must be multi-level
Scientific collaboration is needed
Across spatial scales
Across time scales
Across measurement techniques
Current field borders should not remain boundaries… Curtail Scale Chauvinism!

5. …both scientifically and mathematically
To understand, both theoretically and practically, how brains support behavior and experience
To model brain / behavior dynamics as Active requires
Better behavioral measures and modeling
Better brain dynamic imaging / analysis
Better joint brain / behavior analysis

6. … the next research frontier
Brains are active and multi-scale / multi-level
The dominant multi-level model: Computers
… with their physical / logical computer hierarchy
the OSI stack
physical / implementation levels
logical / instruction levels

7. A Multi-Level View of Learning
UNIT
INTERACTIONS
LEARNING
society
organism
behaviour
ecology
predation,
symbiosis
natural selection
sensory-motor
learning
cell
spikes
synaptic plasticity
protein
molecular forces
gene expression,
protein recycling
voltage, Ca
bulk molecular changes
synapse
amino acid
direct,V,Ca
molecular changes
( = STDP)
Increasing
Timescale
LEARNING at a LEVEL is CHANGE IN INTERACTIONS between its UNITS,
implemented by INTERACTIONS at the LEVEL beneath, and by extension
resulting in CHANGE IN LEARNING at the LEVEL above.
Interactions=fast
Learning=slow
Separation of timescales allows INTERACTIONS at one LEVEL
to be LEARNING at the LEVEL above.

9. A Multi-Level View of Learning
T.Bell
LEVEL
UNIT
DYNAMICS
LEARNING
society
organism
behaviour
ecology
predation,
symbiosis
natural selection
sensory-motor
learning
cell
spikes
synaptic plasticity
protein
molecular forces
gene expression,
protein recycling
voltage, Ca
bulk molecular changes
synapse
amino acid
direct,V,Ca
molecular changes
( = STDP)
Increasing
Timescale
LEARNING at one LEVEL is implemented by
DYNAMICS between UNITS at the LEVEL below.
Dynamics=fast
Learning=slow
Separation of timescales allows DYNAMICS at one LEVEL
to be LEARNING at the LEVEL above.

10. ? = the Levels Hypothesis: Learning in the brain is:
What idea will fill in the question mark?
T.Bell
physiology (of STDP)
physics of self-organisation ?
(STDP=spike timing-
dependent plasticity)
probabilistic machine learning
? = the Levels Hypothesis: Learning in the brain is:
-unsupervised probability density estimation across scales
the smaller (molecular) models the larger (spikes)….
suggested by STDP physiology, where information flow
from neurons to synapses is inter-level….

11. Multi-level modeling:
network of neurons
network of 2 brains
1 cell
1 brain
network of protein complexes
(e.g., synapses)
network of macromolecules
Networks within networks

12. Infomax between Levels. (eg: synapses density-estimate spikes) 1
T.Bell
Infomax between Levels.
(eg: synapses density-estimate spikes) 1
ICA/Infomax between Layers.
(eg: V1 density-estimates Retina) 2 t
all neural spikes
retina V1
synaptic
weights x y
synapses,
dendrites y
all synaptic readout
between-level
includes all feedback
molecular net models/creates
social net is boundary condition
permits arbitrary activity dependencies
models input and intrinsic together
within-level
feedforward
molecular sublevel is ‘implementation’
social superlevel is ‘reward’
predicts independent activity
only models outside input
pdf of all spike times
pdf of all synaptic ‘readouts’
ICA transform minimises statistical
dependence between outputs. The
bases produced are data-dependent,
not fixed as in Fourier or Wavelet
transforms.
If we can
make this
pdf uniform
then we have a model
constructed from all synaptic and dendritic causality

13. The Infomax principle/ICA algorithms
T.Bell
The Infomax principle/ICA algorithms
Many applications (6 international ICA workshops)…
audio separation in real acoustic environments (as above)
biomedical data-mining -- EEG,fMRI,
image coding
Cognitive Computing…Computational Neuroscience