Multi-levelHuman Brain Modeling

Jerome SwartzThe Swartz Foundation

Rancho Santa Fe

9/30/06

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 modeling

will increasingly suffer

from Information overload:

Successful Modeling

New Measurements

New DynamicsPhenomena

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 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’

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

Organisms

Neurons

Membrane Protein Complexes

Macromolecules

emergence boundarycondition

behavior

spikes

conformationalchanges

Information Flow in the Levels-hierarchy

Cortical hemispheres Cerebral cortex (ACC,PFC, etc.)Thalamus/sensory afferentsHippocampus-working memorySensorimotor system

Human Multi-level (“Brain Stack”) FrameworkLevel Additional Description Components Spatial Scale

Hum

an B

ehav

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l Lev

els

Info

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ion-

The

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yste

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cal/C

odin

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vels

Social Neuroscience(Neuro-anthropology)

Human Interaction(Physical/Electronic)

Cognitive/Psychological(Whole Brain)

Socio-Political(Geographical/Cyber)

Neurophysiological(Anatomical

“maps”)

Network

Circuit

Neuronal

Synaptic

Molecular

Evolution-driven m:n (many:many)Global/Nation-States

Closed System Interconnect Model

Evolution/macro-plasticity km-MMm

Emotional/Rational/Innerthought

1:1 (one:one)“mirror neurons”Evolution-driver

“Network of Networks”/CNS

Communication/System sublevels

Macrodynamics

Interneuronal sublevelSynaptic/axonal/dendriticMyelination/ganglia

Neurogenetic sublevel

Physical/coding sublevel

Cortical microcircuitsThalamocortical circuits

(1k neuron) Mini-columnsNeo-cortical columns (10-100k)Synfire chains

[

1:self Conscious sublevel (presentation sublevel)

Unconscious processing

(MM: million)

dm-MMm

1 m

1cm-dm

1cm-dm

1mm-cm

1 μ -100 μ

1 Å

]

[NeuromodulatorsProteinsAmino Acids

[ ]

]

]

]]

Emotion Language Decision making (“Thin/thick slices”)Attention/awarenessSleep/awake

[ ]

[

][

[ ]

1:n (one:many)Regional/cities

Cellular microdynamic levelSpike time dependent plasticity/Learning

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Mic

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opic

Mes

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Mac

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