Mapping of Functionally Characterized Cell Classes onto Canonical Circuit Operations in Primate Prefrontal Cortex

Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3–7 Hz (theta) and 12–20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations.

[1]  William B. Levy,et al.  Energy Efficient Neural Codes , 1996, Neural Computation.

[2]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[3]  Helen Barbas,et al.  The Prefrontal Cortex and Flexible Behavior , 2007, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[4]  Ming-Yen Cheng,et al.  Calibrating the excess mass and dip tests of modality , 1998 .

[5]  John O'Keefe,et al.  Independent rate and temporal coding in hippocampal pyramidal cells , 2003, Nature.

[6]  T. Preuss Do Rats Have Prefrontal Cortex? The Rose-Woolsey-Akert Program Reconsidered , 1995, Journal of Cognitive Neuroscience.

[7]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[8]  Christopher F. Parmeter,et al.  Modes, Weighted Modes, and Calibrated Modes: Evidence of Clustering Using Modality Tests* , 2008 .

[9]  Martin Vinck,et al.  Attentional Modulation of Cell-Class-Specific Gamma-Band Synchronization in Awake Monkey Area V4 , 2013, Neuron.

[10]  J. Hartigan,et al.  The Dip Test of Unimodality , 1985 .

[11]  Theodoros P. Zanos,et al.  Removal of spurious correlations between spikes and local field potentials. , 2011, Journal of neurophysiology.

[12]  P. Goldman-Rakic,et al.  Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. , 2003, Journal of neurophysiology.

[13]  Rafael Yuste,et al.  A blanket of inhibition: functional inferences from dense inhibitory connectivity , 2014, Current Opinion in Neurobiology.

[14]  L.F. Abbott,et al.  Gating Multiple Signals through Detailed Balance of Excitation and Inhibition in Spiking Networks , 2009, Nature Neuroscience.

[15]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[16]  K. Koepsell,et al.  Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies , 2010, Proceedings of the National Academy of Sciences.

[17]  Earl K Miller,et al.  Cortical circuits for the control of attention , 2012, Current Opinion in Neurobiology.

[18]  Jude F. Mitchell,et al.  Differential Attention-Dependent Response Modulation across Cell Classes in Macaque Visual Area V4 , 2007, Neuron.

[19]  Ricardo J. G. B. Campello,et al.  Relative clustering validity criteria: A comparative overview , 2010, Stat. Anal. Data Min..

[20]  Stefan Everling,et al.  Specific Contributions of Ventromedial, Anterior Cingulate, and Lateral Prefrontal Cortex for Attentional Selection and Stimulus Valuation , 2011, PLoS biology.

[21]  H. Tamura,et al.  Regional and laminar differences in in vivo firing patterns of primate cortical neurons. , 2005, Journal of neurophysiology.

[22]  Stephen J. Gotts,et al.  Cell-Type-Specific Synchronization of Neural Activity in FEF with V4 during Attention , 2012, Neuron.

[23]  M. Scanziani,et al.  Equalizing Excitation-Inhibition Ratios across Visual Cortical Neurons , 2014, Nature.

[24]  Concha Bielza,et al.  New insights into the classification and nomenclature of cortical GABAergic interneurons , 2013, Nature Reviews Neuroscience.

[25]  Osman Ratib,et al.  OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images , 2004, Journal of Digital Imaging.

[26]  H. Markram,et al.  Interneurons of the neocortical inhibitory system , 2004, Nature Reviews Neuroscience.

[27]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[28]  Shigeru Shinomoto,et al.  Differences in Spiking Patterns Among Cortical Neurons , 2003, Neural Computation.

[29]  Eric L. Denovellis,et al.  Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex , 2012, Neuron.

[30]  R. Passingham,et al.  The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight , 2012 .

[31]  Alexander S. Ecker,et al.  Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness , 2014, Nature Neuroscience.

[32]  Adam Kepecs,et al.  From circuit motifs to computations: mapping the behavioral repertoire of cortical interneurons , 2014, Current Opinion in Neurobiology.

[33]  A. Burkhalter Many Specialists for Suppressing Cortical Excitation , 2008, Front. Neurosci..

[34]  Emad N. Eskandar,et al.  Achieving behavioral control with millisecond resolution in a high-level programming environment , 2008, Journal of Neuroscience Methods.

[35]  J. Lisman,et al.  Position reconstruction from an ensemble of hippocampal place cells: contribution of theta phase coding. , 2000, Journal of neurophysiology.

[36]  Xiaolong Jiang,et al.  The organization of two new cortical interneuronal circuits , 2013, Nature Neuroscience.

[37]  L. Gentet Functional diversity of supragranular GABAergic neurons in the barrel cortex , 2012, Front. Neural Circuits.

[38]  P. Goldman-Rakic,et al.  A role for inhibition in shaping the temporal flow of information in prefrontal cortex , 2002, Nature Neuroscience.

[39]  T. Redman The Impact of , 1998 .

[40]  A. Zaitsev,et al.  Electrophysiological differences between neurogliaform cells from monkey and rat prefrontal cortex. , 2007, Journal of neurophysiology.

[41]  J. Rogers Immunohistochemical markers in rat cortex: co-localization of calretinin and calbindin-D28k with neuropeptides and GABA , 1992, Brain Research.

[42]  Stefan Everling,et al.  Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition. , 2015, Cerebral cortex.

[43]  Erik De Schutter,et al.  Impact of Neuronal Properties on Network Coding: Roles of Spike Initiation Dynamics and Robust Synchrony Transfer , 2013, Neuron.

[44]  Martin Vinck,et al.  The pairwise phase consistency: A bias-free measure of rhythmic neuronal synchronization , 2010, NeuroImage.

[45]  Stefan Everling,et al.  Burst Firing Synchronizes Prefrontal and Anterior Cingulate Cortex during Attentional Control , 2014, Current Biology.

[46]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[47]  Eran Stark,et al.  In vivo optogenetic identification and manipulation of GABAergic interneuron subtypes. , 2023, ArXiv.

[48]  J. Csicsvari,et al.  Oscillatory Coupling of Hippocampal Pyramidal Cells and Interneurons in the Behaving Rat , 1999, The Journal of Neuroscience.

[49]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[50]  Stephen J. Gotts,et al.  Cell-Type-Specific Synchronization of Neural Activity in FEF with V 4 during Attention , 2022 .

[51]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[52]  Nicolas Brunel,et al.  Sensory neural codes using multiplexed temporal scales , 2010, Trends in Neurosciences.

[53]  Stefano Panzeri,et al.  Analysis of Slow (Theta) Oscillations as a Potential Temporal Reference Frame for Information Coding in Sensory Cortices , 2012, PLoS Comput. Biol..

[54]  Emad N. Eskandar,et al.  A flexible software tool for temporally-precise behavioral control in Matlab , 2008, Journal of Neuroscience Methods.

[55]  G. Laurent,et al.  Adaptive regulation of sparseness by feedforward inhibition , 2007, Nature Neuroscience.

[56]  K. D. Punta,et al.  An ultra-sparse code underlies the generation of neural sequences in a songbird , 2002 .

[57]  Sylvain Crochet,et al.  Synaptic Computation and Sensory Processing in Neocortical Layer 2/3 , 2013, Neuron.

[58]  K. H. Britten,et al.  Power spectrum analysis of bursting cells in area MT in the behaving monkey , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[59]  Byron M. Yu,et al.  Roles of monkey premotor neuron classes in movement preparation and execution. , 2010, Journal of neurophysiology.

[60]  Dimitri M. Kullmann,et al.  Oscillatory multiplexing of population codes for selective communication in the mammalian brain , 2014, Nature Reviews Neuroscience.

[61]  J. Barry Richmond,et al.  Neural Coding , 2014, Encyclopedia of Computational Neuroscience.

[62]  E. P. Gardner,et al.  Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex , 2008, Nature Reviews Neuroscience.

[63]  G. Fishell,et al.  Interneuron cell types are fit to function , 2014, Nature.

[64]  William R. Softky,et al.  Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. , 1996, Journal of neurophysiology.

[65]  Christos Constantinidis,et al.  A Neural Circuit Basis for Spatial Working Memory , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[66]  P S Goldman-Rakic,et al.  Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[67]  J. Lisman Bursts as a unit of neural information: making unreliable synapses reliable , 1997, Trends in Neurosciences.

[68]  C. Barnes,et al.  Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex. , 2015, Cerebral cortex.

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

[70]  Kikuro Fukushima,et al.  Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex , 2009, PLoS Comput. Biol..

[71]  T. Sejnowski,et al.  Cortical oscillations arise from contextual interactions that regulate sparse coding , 2014, Proceedings of the National Academy of Sciences.

[72]  P. Fries A mechanism for cognitive dynamics: neuronal communication through neuronal coherence , 2005, Trends in Cognitive Sciences.

[73]  E. Miller,et al.  Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices , 2007, Science.

[74]  Ned T. Sahin,et al.  Dynamic circuit motifs underlying rhythmic gain control, gating and integration , 2014, Nature Neuroscience.

[75]  Stefan Everling,et al.  Monkey Prefrontal Cortical Pyramidal and Putative Interneurons Exhibit Differential Patterns of Activity Between Prosaccade and Antisaccade Tasks , 2009, The Journal of Neuroscience.

[76]  A. Nieder,et al.  Complementary Contributions of Prefrontal Neuron Classes in Abstract Numerical Categorization , 2008, The Journal of Neuroscience.

[77]  Christian K. Machens,et al.  Variability in neural activity and behavior , 2014, Current Opinion in Neurobiology.

[78]  Martin Vinck,et al.  Improved measures of phase-coupling between spikes and the Local Field Potential , 2011, Journal of Computational Neuroscience.

[79]  Y. Kubota,et al.  GABAergic cell subtypes and their synaptic connections in rat frontal cortex. , 1997, Cerebral cortex.

[80]  Tatiana Pasternak,et al.  Flexibility of Sensory Representations in Prefrontal Cortex Depends on Cell Type , 2009, Neuron.

[81]  W. Singer,et al.  Orientation selectivity and noise correlation in awake monkey area V1 are modulated by the gamma cycle , 2012, Proceedings of the National Academy of Sciences.

[82]  Yang Dan,et al.  Interneuron subtypes and orientation tuning , 2014, Nature.

[83]  P. Goldman-Rakic,et al.  Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[84]  M. Scanziani,et al.  How Inhibition Shapes Cortical Activity , 2011, Neuron.

[85]  P. Goldman-Rakic,et al.  Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. , 2002, Journal of neurophysiology.

[86]  Alexander Kraskov,et al.  Large Identified Pyramidal Cells in Macaque Motor and Premotor Cortex Exhibit “Thin Spikes”: Implications for Cell Type Classification , 2011, The Journal of Neuroscience.

[87]  D. V. van Essen,et al.  Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.

[88]  R. Desimone,et al.  High-Frequency, Long-Range Coupling Between Prefrontal and Visual Cortex During Attention , 2009, Science.

[89]  Mu-ming Poo,et al.  Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System , 2012 .

[90]  Edmund T. Rolls,et al.  What determines the capacity of autoassociative memories in the brain? Network , 1991 .

[91]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[92]  Frank C. Hoppensteadt,et al.  Bursts as a unit of neural information: selective communication via resonance , 2003, Trends in Neurosciences.

[93]  B. Willmore,et al.  Sparse coding in striate and extrastriate visual cortex. , 2011, Journal of neurophysiology.

[94]  Stefan Everling,et al.  Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors , 2010, Proceedings of the National Academy of Sciences.

[95]  Xiao-Jing Wang,et al.  A Tweaking Principle for Executive Control: Neuronal Circuit Mechanism for Rule-Based Task Switching and Conflict Resolution , 2013, The Journal of Neuroscience.