Multi-level Human Brain Modeling

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Multi-levelHuman Brain Modeling

• Jerome Swartz
• The Swartz Foundation

Rancho Santa Fe 9/30/06
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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
• Dynamic brain modelingwill increasingly suffer
• from Information overload

Successful Modeling
New Dynamics Phenomena
New Measurements
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Brain Research Must Be Multi-level

• Brains are active and multi-scale/multi-level
• The dominant multi-level model the computers
  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!

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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

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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
• Todays (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 dont directly
  translate into behavioral performance
• Theorists interested in cognition predominantly
  use abstract mathematical models that are not
  constrained by neurobiology

the next research frontiers
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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

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Multi-level Modeling Futures III

• How do top-down cognitive signals interact with
  bottom-up 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 large-scale brain networks with interconnected
  areas/modules

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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.

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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

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Information Flow in the Levels-hierarchy
Organisms
behavior
Neurons
boundary condition
emergence
spikes
Membrane Protein Complexes
conformational changes
Macromolecules
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Human Multi-level (Brain Stack) Framework
Level
Components
Additional Description
Spatial Scale
(MM million)
Social Neuroscience (Neuro-anthropology)
mn (manymany) Global/Nation-States


Evolution-driven
Socio-Political (Geographical/Cyber)


1n (onemany) Regional/cities
km-MMm
Evolution/macro-plasticity
Human Interaction (Physical/Electronic)

11 (oneone) mirror neurons

Human Behavioral Levels
Evolution-driver
dm-MMm
Macroscopic

Emotion Language Decision making (Thin/thick
slices) Attention/awareness Sleep/awake
1self
Conscious sublevel (presentation sublevel)
Cognitive/ Psychological (Whole Brain)
Emotional/Rational/ Innerthought
1 m


Unconscious processing
Cortical hemispheres Cerebral cortex (ACC,PFC,
etc.) Thalamus/sensory afferents Hippocampus-worki
ng memory Sensorimotor system
Neurophysiological (Anatomical maps)
Network of Networks/CNS
1cm-dm


Information-Theoretic/System Levels
(1k neuron) Mini-columns Neo-cortical columns
(10-100k) Synfire chains
Mesoscopic
Network
Communication/System sublevels
1cm-dm


Cortical microcircuits Thalamocortical circuits
Circuit
Macrodynamics
1mm-cm




Interneuronal sublevel Synaptic/axonal/dendritic M
yelination/ganglia
Neuronal Synaptic
Cellular microdynamic level Spike time dependent
plasticity/Learning
1 µ -100 µ
Microscopic
Physical/Coding Levels





Neuromodulators Proteins Amino Acids
Neurogenetic sublevel Physical/coding sublevel
1 Å
Molecular
Closed System Interconnect Model