Multi-level Human Brain Modeling

1. Multi-levelHuman Brain Modeling Jerome Swartz The Swartz Foundation Rancho Santa Fe 9/30/06

2. 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

3. 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!

4. 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

5. 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

6. 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

7. 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’

8. 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.

9. 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

10. Information Flow in the Levels-hierarchy Organisms behavior Neurons boundary condition emergence spikes Membrane Protein Complexes conformational changes Macromolecules

11. [ [ Level Components Additional Description Spatial Scale (MM: million) [ m:n (many:many) Global/Nation-States Social Neuroscience (Neuro-anthropology) ] Evolution-driven Human Multi-level (“Brain Stack”) Framework Socio-Political (Geographical/Cyber) [ 1:n (one:many) Regional/cities ] km-MMm Evolution/macro-plasticity Human Interaction (Physical/Electronic) [ 1:1 (one:one) “mirror neurons” ] Human Behavioral Levels Evolution-driver dm-MMm Macroscopic [ Emotion Language Decision making (“Thin/thick slices”) Attention/awareness Sleep/awake 1:self 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-working 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 Myelination/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