Topological analysis of the connectome of digital reconstructions of neural microcircuits

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.

[1]  H. C. LONGUET-HIGGINS,et al.  Non-Holographic Associative Memory , 1969, Nature.

[2]  A. Peters,et al.  The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. I. General description , 1976, Journal of neurocytology.

[3]  A. Peters Projection of Lateral Geniculate Nucleus to Area 17 of the Rat Cerebral Cortex , 1976 .

[4]  A. Peters,et al.  The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex, IV terminations upon spiny dendrites , 1977, Journal of neurocytology.

[5]  A. Peters,et al.  The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. V. Degenerating axon terminals synapsing with Golgi impregnated neurons , 1979, Journal of neurocytology.

[6]  Professor Moshe Abeles,et al.  Local Cortical Circuits , 1982, Studies of Brain Function.

[7]  M. Abeles Local Cortical Circuits: An Electrophysiological Study , 1982 .

[8]  B. Bollobás The evolution of random graphs , 1984 .

[9]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[10]  James R. Munkres,et al.  Elements of algebraic topology , 1984 .

[11]  E. Bienenstock A model of neocortex , 1995 .

[12]  Carlos D. Brody,et al.  Correlations Without Synchrony , 1999, Neural Computation.

[13]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[14]  W. Singer,et al.  Temporal binding and the neural correlates of sensory awareness , 2001, Trends in Cognitive Sciences.

[15]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[16]  E. Berger,et al.  Eigenvalues and homology of flag complexes and vector representations of graphs , 2003 .

[17]  Pulin Gong,et al.  Evolution to a small-world network with chaotic units , 2004 .

[18]  A. Aertsen,et al.  On the significance of correlations among neuronal spike trains , 2004, Biological Cybernetics.

[19]  Bartlett W. Mel,et al.  Cortical rewiring and information storage , 2004, Nature.

[20]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[21]  D. Chklovskii,et al.  Neurogeometry and potential synaptic connectivity , 2005, Trends in Neurosciences.

[22]  G. Shepherd,et al.  Geometric and functional organization of cortical circuits , 2005, Nature Neuroscience.

[23]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[24]  H. Markram,et al.  Spontaneous and evoked synaptic rewiring in the neonatal neocortex , 2006, Proceedings of the National Academy of Sciences.

[25]  K. Svoboda,et al.  Experience-dependent structural synaptic plasticity in the mammalian brain , 2009, Nature Reviews Neuroscience.

[26]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[27]  H. S. Meyer,et al.  Cell Type–Specific Thalamic Innervation in a Column of Rat Vibrissal Cortex , 2010, Cerebral cortex.

[28]  P. Dayan,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits , 2022 .

[29]  Günther Palm,et al.  Memory Capacities for Synaptic and Structural Plasticity G ¨ Unther Palm , 2022 .

[30]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[31]  Thomas K. Berger,et al.  A synaptic organizing principle for cortical neuronal groups , 2011, Proceedings of the National Academy of Sciences.

[32]  Lav R. Varshney,et al.  Structural Properties of the Caenorhabditis elegans Neuronal Network , 2009, PLoS Comput. Biol..

[33]  Matthew Kahle,et al.  Sharp vanishing thresholds for cohomology of random flag complexes , 2012, 1207.0149.

[34]  Dietmar Plenz,et al.  The organization of strong links in complex networks , 2011, Nature Physics.

[35]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[36]  R. Yuste,et al.  Visual stimuli recruit intrinsically generated cortical ensembles , 2014, Proceedings of the National Academy of Sciences.

[37]  Eric Shea-Brown,et al.  Local paths to global coherence: cutting networks down to size. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Günther Palm,et al.  Cell assemblies in the cerebral cortex , 2014, Biological Cybernetics.

[39]  G. Petri,et al.  Homological scaffolds of brain functional networks , 2014, Journal of The Royal Society Interface.

[40]  Ulrich Bauer,et al.  Clear and Compress: Computing Persistent Homology in Chunks , 2013, Topological Methods in Data Analysis and Visualization.

[41]  Rasmus S. Petersen,et al.  Efficient population coding of naturalistic whisker motion in the ventro-posterior medial thalamus based on precise spike timing , 2015, Front. Neural Circuits.

[42]  James G. King,et al.  Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.

[43]  James G. King,et al.  An algorithm to predict the connectome of neural microcircuits , 2015, Front. Comput. Neurosci..

[44]  G. Shepherd,et al.  The neocortical circuit: themes and variations , 2015, Nature Neuroscience.

[45]  B. McNaughton,et al.  Packet-based communication in the cortex , 2015, Nature Reviews Neuroscience.

[46]  E. Pastalkova,et al.  Clique topology reveals intrinsic geometric structure in neural correlations , 2015, Proceedings of the National Academy of Sciences.

[47]  James G. King,et al.  The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex , 2015, Front. Neural Circuits.

[48]  Jason N. MacLean,et al.  Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks , 2016, PLoS Comput. Biol..

[49]  M. A. Smith,et al.  The spatial structure of correlated neuronal variability , 2016, Nature Neuroscience.

[50]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[51]  Ulrich Bauer,et al.  Phat - Persistent Homology Algorithms Toolbox , 2014, J. Symb. Comput..