Inhibitory Units: An Organizing Nidus for Feature-Selective Sub-Networks in Area V1

Sensory stimuli are encoded by the joint firing of neuronal groups composed of pyramidal cells and interneurons, rather than single isolated neurons (Uhlhaas et al, 2009, Buzsaki, 2010). However, the principles by which these groups are organized to encode information remain poorly understood. A leading hypothesis is that similarly tuned pyramidal cells that preferentially connect to each other may form multi-cellular encoding units yoked to a similar purpose. The existence of such groups would be reflected on the profile of spontaneous events observed in neocortical networks. We used 2-photon calcium imaging to study spontaneous population-burst events in layer 2/3 of mouse area V1 during postnatal maturation (postnatal day 8–52). Throughout the period examined both size and duration of spontaneously occurring population-bursts formed scale-free distributions obeying a power law. The same was true for the degree of “functional connectivity,” a measure of pairwise synchrony across cells. These observations are consistent with a hierarchical small-world-net architecture, characterized by groups of cells with high local connectivity (“small worlds”, cliques) connected to each other via a restricted number of “hub” cells” (Bonifazi et al., 2009, Sporns, 2011, Luce & Perry, 1949). To identify candidate “small world” groups we searched for cells whose calcium events had a consistent temporal relationship to events recorded from local inhibitory interneurons. This was guided by the intuition that groups of neurons whose synchronous firing represents a “temporally coherent computational unit” (or feature) ought to be inhibited together. This strategy allowed us to identify clusters of pyramidal neurons whose firing is temporally “linked” to one or more local interneurons. These “small-world” clusters did not remain static, during postnatal development: both cluster size and overlap with other clusters decreased over time as pyramidal neurons became progressively more selective, “linking” to fewer neighboring interneurons. Notably, pyramidal neurons in a cluster show higher tuning function similarity than expected with each other and with their “linked” interneurons. Our findings suggest that spontaneous population events in the visual cortex are shaped by “small-world” networks of pyramidal neurons that share functional properties and work in concert with one or more local interneurons. We argue that such groups represent a fundamental neocortical unit of computation at the population level.

[1]  G. Buzsáki,et al.  Interaction between neocortical and hippocampal networks via slow oscillations. , 2005, Thalamus & related systems.

[2]  Bo Yang Yu,et al.  Image processing and classification algorithm for yeast cell morphology in a microfluidic chip. , 2011, Journal of biomedical optics.

[3]  G. Miyoshi,et al.  Genetic Fate Mapping Reveals That the Caudal Ganglionic Eminence Produces a Large and Diverse Population of Superficial Cortical Interneurons , 2010, The Journal of Neuroscience.

[4]  Morgane M. Roth,et al.  Model-based analysis of pattern motion processing in mouse primary visual cortex , 2015, Front. Neural Circuits.

[5]  G. Buzsáki,et al.  Sequential structure of neocortical spontaneous activity in vivo , 2007, Proceedings of the National Academy of Sciences.

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

[7]  Daniel N. Hill,et al.  Development of Direction Selectivity in Mouse Cortical Neurons , 2011, Neuron.

[8]  T. Prescott,et al.  The brainstem reticular formation is a small-world, not scale-free, network , 2006, Proceedings of the Royal Society B: Biological Sciences.

[9]  R. Yuste,et al.  Imprinting and recalling cortical ensembles , 2016, Science.

[10]  Rafael Yuste,et al.  Fast nonnegative deconvolution for spike train inference from population calcium imaging. , 2009, Journal of neurophysiology.

[11]  G. DeAngelis,et al.  Parallel Input Channels to Mouse Primary Visual Cortex , 2010, The Journal of Neuroscience.

[12]  Edward T. Bullmore,et al.  Modular and Hierarchically Modular Organization of Brain Networks , 2010, Front. Neurosci..

[13]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[14]  C. Stosiek,et al.  In vivo two-photon calcium imaging of neuronal networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  C. Clopath,et al.  The emergence of functional microcircuits in visual cortex , 2013, Nature.

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

[17]  R. Douglas,et al.  A functional microcircuit for cat visual cortex. , 1991, The Journal of physiology.

[18]  B. Sakmann,et al.  In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain , 2002, Pflügers Archiv.

[19]  Alexander S. Ecker,et al.  Principles of connectivity among morphologically defined cell types in adult neocortex , 2015, Science.

[20]  Quanxin Wang,et al.  Area map of mouse visual cortex , 2007, The Journal of comparative neurology.

[21]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

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

[23]  Olaf Sporns,et al.  Small worlds inside big brains , 2006, Proceedings of the National Academy of Sciences.

[24]  D. Long Networks of the Brain , 2011 .

[25]  Nathalie L Rochefort,et al.  Sparsification of neuronal activity in the visual cortex at eye-opening , 2009, Proceedings of the National Academy of Sciences.

[26]  Ganna Palagina,et al.  Complex Visual Motion Representation in Mouse Area V1 , 2017, The Journal of Neuroscience.

[27]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[28]  György Buzsáki,et al.  Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.

[29]  Dario L Ringach,et al.  Spontaneous and driven cortical activity: implications for computation , 2009, Current Opinion in Neurobiology.

[30]  T. Tsumoto,et al.  Parvalbumin-expressing interneurons can act solo while somatostatin-expressing interneurons act in chorus in most cases on cortical pyramidal cells , 2017, Scientific Reports.

[31]  J. Tiago Gonçalves,et al.  Internally Mediated Developmental Desynchronization of Neocortical Network Activity , 2009, The Journal of Neuroscience.

[32]  John M. Beggs,et al.  Functional Clusters, Hubs, and Communities in the Cortical Microconnectome , 2014, Cerebral cortex.

[33]  T. Wiesel,et al.  Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[34]  J. Movshon,et al.  Dynamics of motion signaling by neurons in macaque area MT , 2005, Nature Neuroscience.

[35]  Michel A. Picardo,et al.  GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks , 2009, Science.

[36]  Rafael Yuste,et al.  Cooperative Subnetworks of Molecularly Similar Interneurons in Mouse Neocortex , 2016, Neuron.

[37]  György Buzsáki,et al.  Tasks for inhibitory interneurons in intact brain circuits , 2015, Neuropharmacology.

[38]  Michael L. Platt,et al.  What can developmental and comparative cognitive neuroscience tell us about the adult human brain? , 2009, Current Opinion in Neurobiology.

[39]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[40]  Allan R. Jones,et al.  A robust and high-throughput Cre reporting and characterization system for the whole mouse brain , 2009, Nature Neuroscience.

[41]  R. Luce,et al.  A method of matrix analysis of group structure , 1949, Psychometrika.

[42]  L F Lago-Fernández,et al.  Fast response and temporal coherent oscillations in small-world networks. , 1999, Physical review letters.

[43]  H. Markram,et al.  Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells , 2007, Neuron.

[44]  Quanxin Wang,et al.  Modularity in the Organization of Mouse Primary Visual Cortex , 2015, Neuron.

[45]  W. Singer,et al.  Neural Synchrony in Cortical Networks: History, Concept and Current Status , 2009, Front. Integr. Neurosci..

[46]  Y. Dan,et al.  Dissection of Cortical Microcircuits by Single-Neuron Stimulation In Vivo , 2012, Current Biology.

[47]  Ioannis Smyrnakis,et al.  Information Transfer Through Stochastic Transmission of a Linear Combination of Rates , 2013, Neural Computation.

[48]  J. M. Herrmann,et al.  Finite-size effects of avalanche dynamics. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  Rafael Yuste,et al.  Endogenous Sequential Cortical Activity Evoked by Visual Stimuli , 2015, The Journal of Neuroscience.

[50]  Henry Kennedy,et al.  Cortical High-Density Counterstream Architectures , 2013, Science.

[51]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[52]  M. Sur,et al.  Reliable sensory processing in mouse visual cortex through inhibitory interactions between Somatostatin and Parvalbumin interneurons , 2017, bioRxiv.

[53]  Rosa Cossart,et al.  Operational hub cells: a morpho-physiologically diverse class of GABAergic neurons united by a common function , 2014, Current Opinion in Neurobiology.

[54]  P. J. Sjöström,et al.  Functional specificity of local synaptic connections in neocortical networks , 2011, Nature.

[55]  Edward M. Callaway,et al.  Pattern and Component Motion Responses in Mouse Visual Cortical Areas , 2015, Current Biology.