Multi-level Human Brain Modeling Jerome Swartz The 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 Dynamics Phenomena

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 boundary condition behavior spikes conformational changes Information Flow in the Levels-hierarchy

Cortical hemispheres Cerebral cortex (ACC,PFC, etc.) Thalamus/sensory afferents Hippocampus-working memory Sensorimotor system Human Multi-level (“Brain Stack”) Framework Level Additional Description Components Spatial Scale Human Behavioral Levels Information-Theoretic/System Levels Physical/Coding Levels 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 sublevel Synaptic/axonal/dendritic Myelination/ganglia Neurogenetic sublevel Physical/coding sublevel Cortical microcircuits Thalamocortical circuits (1k neuron) Mini-columns Neo-cortical columns (10-100k) Synfire chains [ 1:self Conscious sublevel (presentation sublevel) Unconscious processing (MM: million) dm-MMm 1 m 1cm-dm 1mm-cm 1 μ -100 μ 1 Å ] [ Neuromodulators Proteins Amino Acids [ ] ] ] ] ] Emotion Language Decision making (“Thin/thick slices”) Attention/awareness Sleep/awake [ ] [ ][ [ ] 1:n (one:many) Regional/cities Cellular microdynamic level Spike time dependent plasticity/Learning [ [ ] [ [ Microscopic Mesoscopic Macroscopic [ [