Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-like Systems, Sendhoff et al. Course: Robots Learning from Humans Baek, Da Som 09/11/2015 Biointelligence Laboratory School of Computer Science and Engineering Seoul National University

Contents Part 1 – Brain Complexity Part 2 – Brain Structure Part 3 – Brain Dynamics Summary 2

1. Brain Complexity “the most complicated material object in the known universe” 3 dimensions of brain complexity: Brain structure: heterogeneous components all connected Brain dynamics: Time-dependent brain structure and dynamics as brain grows and matures over time Embodiment: Binding of brain and environment through perception and action Complexity is necessary for flexible and robust intelligence Identify some potential avenues for utilizing our insights towards the design of intelligent systems © 2015, SNU CSE Biointelligence Lab., 3

2. Brain Structure “The anatomical structure of the brain is a product of evolution” Structures reflect species’ ecological demands, body structure, sensory and motor capabilities Very few nervous systems mapped at a cellular level Most neural structures uncharted Human brains have intricate networks Microscopic: Single neuronal cells Mesoscopic: Columns of cells Macroscopic: Nuclei or whole brain regions Anatomy is both constant and malleable Genetic control Individual variability, malleable structures Mapping the structure of the brain and pattern of connections Brain’s functional capacity Main source of functional flexibility and robustness? © 2015, SNU CSE Biointelligence Lab., 4

2. Brain Structure Anatomical patterns of cortical connectivity studied to question how large- scale brain networks are structurally organized Connectivity combines: Local communities (clusters, modules) Capacity to integrate operation across entire network Segregation High local specialization, dense local communities Important correlates of specific functions found in localized changes of neuronal activity Integration Communication and interaction between segregated regions Global integration across entire network i.e. functional integration of multiple neurons for coordinated activation in distributed system of cerebral cortex needs both © 2015, SNU CSE Biointelligence Lab., 5

3. Brain Dynamics Anatomical connectivity of the brain gives rise to functional connectivity patterns Statistical dependence, correlation or mutual information between areas Structural connectivity are substrate for functional connectivity Cerebral cortex surface of macaque monkey Nodes connected with fibers Nodes dynamically coupled Statistical relationship Time-dependency Dynamic interactions Coherent cognitive states Organized behavior Synchrony in cognitive function © 2015, SNU CSE Biointelligence Lab., 6 Figure 1: Structural and functional connectivity in the brain

3. Brain Dynamics Recent evidence suggest structural and functional connectivity in the human brain are highly interrelated Sparse structural network of fiber pathways fMRI correlation pattern (during rest) Structural and functional connections for 66 areas Strengths of connections © 2015, SNU CSE Biointelligence Lab., 7 Figure 2: Structural and functional connectivity in the brain

3. Brain Dynamics Complexity is dependent on the balance between segregation and integration Highly segregated but not dynamic couplings i.e. statistical relationships between elements Individual dynamics No higher-order structures No connections, mutual information Totally integrated means completely regular Some mutual information But information identical across all partitions Combination of local segregation and global integration Mutual information is high Context is rich and differentiated Connections © 2015, SNU CSE Biointelligence Lab., 8 Figure 3: Complexity and its dependency on the balance between segregation and integration of the functional elements of a system

Summary Brain networks contain specific structural patterns and motifs Structural attributes facilitate complex neural dynamics This integrates information for higher cognitive functions Complexity is crucial to the emergence of flexible and robust intelligence Understanding and harnessing this therefore is important to creating brain-like intelligence © 2015, SNU CSE Biointelligence Lab., 9