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Multi-level
Human Brain Modeling
Rancho Santa Fe
9/30/06
Jerome Swartz
The Swartz Foundation
Multi-level Brain Modeling
• Everyone agrees there ARE multiple levels of description
• Science IS modeling
• Science is intrinsically multi-level in nature (e.g. neurons
– behavior; genes – disease; atoms – molecules; etc.)
• Understanding how the brain works means modeling the
dynamics of multi-level Information flow (not so easy!)
• Defining the Information processed by each brain
element at each Level is essential
Successful Modeling
• Dynamic brain modeling
will increasingly suffer
New Dynamics
Phenomena
from Information overload:
New
Measurements
Brain Research Must Be Multi-level
• Brains are active and multi-scale/multi-level
• The dominant multi-level model: the computer’s physical/
logical hierarchy (viz OSI computer ‘stack’ multi-level
description)
• Scientific collaboration is needed
–
–
–
–
Across spatial scales
Across time scales
Across measurement techniques
Across models
• Current field borders should not remain boundaries
…Curtail Scale Chauvinism!
Level Chauvinism is Endemic…
• Dirac on discovering the positron: “the rest is chemistry”… molecular
structure is an epiphenomenon!
• Systems neuroscience & neural networks: ‘the molecular level is
implementational detail’… neural oscillations are epiphenomena
• Genetics/Evolutionary Psychology: genetic basis for behavior
• Cognitive Psychology: largely ignores the brain itself
• Almost everyone: quantum phenomena are irrelevant to biology
To progress beyond this, we must ask if there are any invariant
mathematical principles underlying biological multiple level interaction
Multi-level Modeling Futures I
• 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
• Today’s (‘hardcore’ neurobiological) large scale computational models do
not (yet) explain cognitive functions and complex behavior…. Stay tuned!
• Circuit modelers mostly work on simple *physiological phenomena* that
don’t directly translate into behavioral performance
• Theorists interested in cognition predominantly use abstract mathematical
models that are not constrained by neurobiology
… the next research frontiers
Multi-level Modeling Futures II
• Microcircuit models of cognitive processes (relating
microscopic-to-macroscopic) to link the biology of
synapses and neurons to behavior through network
dynamics
• Cognitive-type circuit models detailed enough to account
for neuronal data and high-level enough to reproduce
behavioral events correlated to EEG and fMRI
measurement and provide a unified framework
• Linear filter models are powerful for sensory processing,
but cognitive-type computations involving nonlinear
dynamical systems, multiple attractors, bifurcations, etc.,
will play an important role
Multi-level Modeling Futures III
• How do top-down ‘cognitive’ signals interact with bottomup external stimuli? How do signals flow in a reciprocal
loop between thalamocortical sensory circuits and
working memory/‘decision’ circuits
• Another challenge is to expand circuit modeling to largescale brain networks with interconnected areas/‘modules’
Multi-level Open Questions I
• Is there a corresponding (comparable?) temporal scale
to our spatially-scaled Multi-level description ?
• At what time scales does Information flow between
levels (how fast up & down?)?
• Are local field synchronies multi-scale?
• Do local fields index shape synchronicity?
• Are there any direct relationships between these
processes and nonconscious/conscious mental
processing…. e.g. ‘Aha!’/‘eureka’; ‘REST’; selective
attention; decision-making; problem solving; etc.
Multi-level Open Questions II
• How does Information cross spatial scales?
– Up
•
•
•
•
Spike & decision ‘ramp-to-threshold’
Stochastic resonance?
Avalanche behavior?
Within & between area synchronization avalanches?
– Down
• Synaptic reshaping
• Frequency nesting
• Ephaptic and neuromodulator influences
Information Flow in the
Levels-hierarchy
Organisms
behavior
emergence
Neurons
spikes
Membrane Protein Complexes
conformational
changes
Macromolecules
boundary
condition
Macroscopic
Human Behavioral Levels
Mesoscopic
Information-Theoretic/System Levels
Microscopic
Physical/Coding Levels
Social Neuroscience
(Neuro-anthropology)
m:n (many:many)
[Global/Nation-States
]
(one:many)
[ 1:n
Regional/cities ]
Evolution-driven
Socio-Political
(Geographical/Cyber)
Evolution/macro-plasticity
Human Interaction
(Physical/Electronic)
Evolution-driver
Cognitive/
Psychological
(Whole Brain)
[
1:self
Conscious sublevel
(presentation sublevel)
Emotional/Rational/
Innerthought
“Network of Networks”/CNS
Network
Communication/System sublevels
Circuit
Macrodynamics
Synaptic
Molecular
[
[
Unconscious processing
[
[
km-MMm
Emotion
Language
Decision making
(“Thin/thick slices”)
Attention/awareness
Sleep/awake
]
]
(1k neuron) Mini-columns
Neo-cortical columns (10-100k)
Synfire chains
Cortical microcircuits
Thalamocortical circuits
[
][
] [
Physical/coding sublevel
dm-MMm
Cortical hemispheres
Cerebral cortex (ACC,PFC, etc.)
Thalamus/sensory afferents
Hippocampus-working memory
Sensorimotor system
[
Cellular microdynamic level
Spike time dependent plasticity/Learning
Neurogenetic sublevel
(MM:
million)
1:1 (one:one)
[ “mirror
neurons” ]
Neurophysiological
(Anatomical
“maps”)
Neuronal
Spatial Scale
Components
Additional Description
]
]
]
Interneuronal sublevel
Synaptic/axonal/dendritic
Myelination/ganglia
Neuromodulators
Proteins
Amino Acids
Closed System Interconnect Model
[
Level
Human Multi-level (“Brain Stack”) Framework
1m
1cm-dm
1cm-dm
1mm-cm
1 μ -100 μ
1Å