Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits

Understanding the relationship between external stimuli and the spiking activity of cortical populations is a central problem in neuroscience. Dense recurrent connectivity in local cortical circuits can lead to counterintuitive response properties, raising the question of whether there are simple arithmetical rules for relating circuits’ connectivity structure to their response properties. One such arithmetic is provided by the mean field theory of balanced networks, which is derived in a limit where excitatory and inhibitory synaptic currents precisely balance on average. However, balanced network theory is not applicable to some biologically relevant connectivity structures. We show that cortical circuits with such structure are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations, but maintained at a global level. This amplification, which can be quantified by a linear correction to the classical mean field theory of balanced networks, explains several response properties observed in cortical recordings and provides fundamental insights into the relationship between connectivity structure and neural responses in cortical circuits.

[1]  H. Sompolinsky,et al.  The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks , 2016, Neuron.

[2]  D. Fitzpatrick,et al.  Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex , 1997, The Journal of Neuroscience.

[3]  Evan S. Schaffer,et al.  Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression , 2009, Neuron.

[4]  V. Hutson Integral Equations , 1967, Nature.

[5]  Wulfram Gerstner,et al.  The quantitative single-neuron modeling competition , 2008, Biological Cybernetics.

[6]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

[7]  Brent Doiron,et al.  The mechanics of state-dependent neural correlations , 2016, Nature Neuroscience.

[8]  Brent Doiron,et al.  Kv7 channels regulate pairwise spiking covariability in health and disease. , 2014, Journal of neurophysiology.

[9]  A. Litwin-Kumar,et al.  Slow dynamics and high variability in balanced cortical networks with clustered connections , 2012, Nature Neuroscience.

[10]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[11]  A. Destexhe,et al.  The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.

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

[13]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[14]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[15]  Kenneth D Miller,et al.  Canonical computations of cerebral cortex , 2016, Current Opinion in Neurobiology.

[16]  Bard Ermentrout,et al.  Linearization of F-I Curves by Adaptation , 1998, Neural Computation.

[17]  Ilan Lampl,et al.  Imbalance between Excitation and Inhibition in the Somatosensory Cortex Produces Postadaptation Facilitation , 2013, The Journal of Neuroscience.

[18]  E. Halgren,et al.  Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex , 2014, Scientific Reports.

[19]  Nicolas Brunel,et al.  Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs , 2011, Front. Comput. Neurosci..

[20]  Wulfram Gerstner,et al.  Integrate-and-Fire models with adaptation are good enough , 2005, NIPS.

[21]  Albert Compte,et al.  Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT , 2015, Nature Communications.

[22]  Xiao-Jing Wang,et al.  Mean-Field Theory of Irregularly Spiking Neuronal Populations and Working Memory in Recurrent Cortical Networks , 2003 .

[23]  Yuzhi Chen,et al.  Sensory stimulation shifts visual cortex from synchronous to asynchronous states , 2014, Nature.

[24]  H. Adesnik,et al.  A neural circuit for spatial summation in visual cortex , 2012, Nature.

[25]  H. Adesnik,et al.  Input normalization by global feedforward inhibition expands cortical dynamic range , 2009, Nature Neuroscience.

[26]  E. Kuramoto,et al.  Cell Type-Specific Inhibitory Inputs to Dendritic and Somatic Compartments of Parvalbumin-Expressing Neocortical Interneuron , 2013, The Journal of Neuroscience.

[27]  Owen A. Ross,et al.  Linking the VPS35 and EIF4G1 Pathways in Parkinson’s Disease , 2015, Neuron.

[28]  M. Goldman,et al.  Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control , 2014, Journal of Neuroscience.

[29]  K. Svoboda,et al.  Channelrhodopsin-2–assisted circuit mapping of long-range callosal projections , 2007, Nature Neuroscience.

[30]  Daniel B. Rubin,et al.  The Stabilized Supralinear Network: A Unifying Circuit Motif Underlying Multi-Input Integration in Sensory Cortex , 2015, Neuron.

[31]  B. McNaughton,et al.  Paradoxical Effects of External Modulation of Inhibitory Interneurons , 1997, The Journal of Neuroscience.

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

[33]  Nicolas Brunel,et al.  Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates , 1999, Neural Computation.

[34]  B. Mandelbrot,et al.  RANDOM WALK MODELS FOR THE SPIKE ACTIVITY OF A SINGLE NEURON. , 1964, Biophysical journal.

[35]  A. Aertsen,et al.  Neuronal Integration of Synaptic Input in the Fluctuation-Driven Regime , 2004, The Journal of Neuroscience.

[36]  Brent Doiron,et al.  Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes. , 2016, Journal of neurophysiology.

[37]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

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

[39]  Maneesh Sahani,et al.  Inhibitory control of correlated intrinsic variability in cortical networks , 2016 .

[40]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[41]  H. Sompolinsky,et al.  Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[44]  K. Deisseroth,et al.  Millisecond-timescale, genetically targeted optical control of neural activity , 2005, Nature Neuroscience.

[45]  Hongkui Zeng,et al.  Differential tuning and population dynamics of excitatory and inhibitory neurons reflect differences in local intracortical connectivity , 2011, Nature Neuroscience.

[46]  R. Froemke,et al.  Oxytocin Enables Maternal Behavior by Balancing Cortical Inhibition , 2015, Nature.

[47]  Robert Rosenbaum,et al.  Highly connected neurons spike less frequently in balanced networks. , 2016, Physical review. E.

[48]  A. Reyes,et al.  Spatial Profile of Excitatory and Inhibitory Synaptic Connectivity in Mouse Primary Auditory Cortex , 2012, The Journal of Neuroscience.

[49]  D. McCormick,et al.  Neocortical Network Activity In Vivo Is Generated through a Dynamic Balance of Excitation and Inhibition , 2006, The Journal of Neuroscience.

[50]  Matt Kaeberlein,et al.  Correction: Corrigendum: Transcription errors induce proteotoxic stress and shorten cellular lifespan , 2015, Nature Communications.

[51]  Nicolas Brunel,et al.  Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise , 2014, Front. Comput. Neurosci..

[52]  W. Wildman,et al.  Theoretical Neuroscience , 2014 .

[53]  A. Hasenstaub,et al.  Asymmetric effects of activating and inactivating cortical interneurons , 2016, eLife.

[54]  M. Carandini,et al.  Parvalbumin-Expressing Interneurons Linearly Transform Cortical Responses to Visual Stimuli , 2012, Neuron.

[55]  Takuma Tanaka,et al.  Parvalbumin‐producing cortical interneurons receive inhibitory inputs on proximal portions and cortical excitatory inputs on distal dendrites , 2012, The European journal of neuroscience.

[56]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[57]  Michael Okun,et al.  Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities , 2008, Nature Neuroscience.

[58]  Alexander S. Ecker,et al.  Decorrelated Neuronal Firing in Cortical Microcircuits , 2010, Science.

[59]  D. Amit,et al.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. , 1997, Cerebral cortex.

[60]  H. Adesnik,et al.  Lateral competition for cortical space by layer-specific horizontal circuits , 2010, Nature.

[61]  K. Deisseroth Optogenetics: 10 years of microbial opsins in neuroscience , 2015, Nature Neuroscience.

[62]  J. Rinn,et al.  DeCoN: Genome-wide Analysis of In Vivo Transcriptional Dynamics during Pyramidal Neuron Fate Selection in Neocortex , 2015, Neuron.

[63]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

[64]  B. Doiron,et al.  Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections , 2013, 1308.6014.