Inhibitory connectivity defines the realm of excitatory plasticity

Recent experiments demonstrate substantial volatility of excitatory connectivity in the absence of any learning. This challenges the hypothesis that stable synaptic connections are necessary for long-term maintenance of acquired information. Here we measure ongoing synaptic volatility and use theoretical modeling to study its consequences on cortical dynamics. We show that in the balanced cortex, patterns of neural activity are primarily determined by inhibitory connectivity, despite the fact that most synapses and neurons are excitatory. Similarly, we show that the inhibitory network is more effective in storing memory patterns than the excitatory one. As a result, network activity is robust to ongoing volatility of excitatory synapses, as long as this volatility does not disrupt the balance between excitation and inhibition. We thus hypothesize that inhibitory connectivity, rather than excitatory, controls the maintenance and loss of information over long periods of time in the volatile cortex.Mongillo et al. use theoretical modeling to link structure & activity in a cortical network. They find that activity patterns are predominantly controlled by inhibitory connections, making the network robust to ongoing changes in excitatory synapses.

[1]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

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

[3]  M. Stryker,et al.  Local GABA circuit control of experience-dependent plasticity in developing visual cortex. , 1998, Science.

[4]  L. Maffei,et al.  BDNF Regulates the Maturation of Inhibition and the Critical Period of Plasticity in Mouse Visual Cortex , 1999, Cell.

[5]  G. Feng,et al.  Imaging Neuronal Subsets in Transgenic Mice Expressing Multiple Spectral Variants of GFP , 2000, Neuron.

[6]  Y. Ben-Ari,et al.  Long-term plasticity at GABAergic and glycinergic synapses: mechanisms and functional significance , 2002, Trends in Neurosciences.

[7]  K. Svoboda,et al.  Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex , 2002, Nature.

[8]  N. Kasthuri,et al.  Long-term dendritic spine stability in the adult cortex , 2002, Nature.

[9]  M. Poo,et al.  Coincident Pre- and Postsynaptic Activity Modifies GABAergic Synapses by Postsynaptic Changes in Cl− Transporter Activity , 2003, Neuron.

[10]  Misha Tsodyks,et al.  The Role of Inhibition in an Associative Memory Model of the Olfactory Bulb , 1997, Journal of Computational Neuroscience.

[11]  W. Gan,et al.  Development of Long-Term Dendritic Spine Stability in Diverse Regions of Cerebral Cortex , 2005, Neuron.

[12]  G. Shepherd,et al.  Transient and Persistent Dendritic Spines in the Neocortex In Vivo , 2005, Neuron.

[13]  Joseph E LeDoux,et al.  Postsynaptic Receptor Trafficking Underlying a Form of Associative Learning , 2005, Science.

[14]  K. Svoboda,et al.  Spine growth precedes synapse formation in the adult neocortex in vivo , 2006, Nature Neuroscience.

[15]  Jianfeng Feng,et al.  Computational neuroscience , 1986, Behavioral and Brain Sciences.

[16]  C. Schreiner,et al.  A synaptic memory trace for cortical receptive field plasticity , 2007, Nature.

[17]  T. Bonhoeffer,et al.  Massive restructuring of neuronal circuits during functional reorganization of adult visual cortex , 2008, Nature Neuroscience.

[18]  W. Gan,et al.  Stably maintained dendritic spines are associated with lifelong memories , 2009, Nature.

[19]  Willie F. Tobin,et al.  Rapid formation and selective stabilization of synapses for enduring motor memories , 2009, Nature.

[20]  K. Svoboda,et al.  Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window , 2009, Nature Protocols.

[21]  W. Gan,et al.  Dendritic spine dynamics. , 2009, Annual review of physiology.

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

[23]  C. Petersen,et al.  Membrane Potential Dynamics of GABAergic Neurons in the Barrel Cortex of Behaving Mice , 2010, Neuron.

[24]  Jun Noguchi,et al.  Structural dynamics of dendritic spines in memory and cognition , 2010, Trends in Neurosciences.

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

[26]  Y. Loewenstein,et al.  Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo , 2011, The Journal of Neuroscience.

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

[28]  Henning Sprekeler,et al.  Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks , 2011, Science.

[29]  D. Hansel,et al.  On the Distribution of Firing Rates in Networks of Cortical Neurons , 2011, The Journal of Neuroscience.

[30]  Elly Nedivi,et al.  Clustered Dynamics of Inhibitory Synapses and Dendritic Spines in the Adult Neocortex , 2012, Neuron.

[31]  Chris I. De Zeeuw,et al.  Elimination of Inhibitory Synapses Is a Major Component of Adult Ocular Dominance Plasticity , 2012, Neuron.

[32]  Yotam Luz,et al.  Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity , 2012, PLoS Comput. Biol..

[33]  W. Gerstner,et al.  Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. , 2012, Journal of neurophysiology.

[34]  P. Caroni,et al.  Structural plasticity upon learning: regulation and functions , 2012, Nature Reviews Neuroscience.

[35]  D. Kullmann,et al.  Plasticity of Inhibition , 2012, Neuron.

[36]  W. Gan,et al.  Opposite effects of fear conditioning and extinction on dendritic spine remodelling , 2012, Nature.

[37]  Mark Hübener,et al.  Critical-period plasticity in the visual cortex. , 2012, Annual review of neuroscience.

[38]  Pico Caroni,et al.  Parvalbumin-expressing basket-cell network plasticity induced by experience regulates adult learning , 2013, Nature.

[39]  Brice Bathellier,et al.  Dynamics of dendritic spines in the mouse auditory cortex during memory formation and memory recall , 2013, Proceedings of the National Academy of Sciences.

[40]  Arianna Maffei,et al.  GABAergic synapses: their plasticity and role in sensory cortex , 2014, Front. Cell. Neurosci..

[41]  G. Buzsáki,et al.  The log-dynamic brain: how skewed distributions affect network operations , 2014, Nature Reviews Neuroscience.

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

[43]  W. Gerstner,et al.  Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements , 2014, Neuron.

[44]  M. Brecht,et al.  Spiking Irregularity and Frequency Modulate the Behavioral Report of Single-Neuron Stimulation , 2014, Neuron.

[45]  William R. Gray Roncal,et al.  Saturated Reconstruction of a Volume of Neocortex , 2015, Cell.

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

[47]  Andreas Lüthi,et al.  Disinhibition, a Circuit Mechanism for Associative Learning and Memory , 2015, Neuron.

[48]  Amanda L. Loshbaugh,et al.  Labelling and optical erasure of synaptic memory traces in the motor cortex , 2015, Nature.

[49]  Yonatan Loewenstein,et al.  Predicting the Dynamics of Network Connectivity in the Neocortex , 2015, The Journal of Neuroscience.

[50]  Noam E. Ziv,et al.  Remodeling and Tenacity of Inhibitory Synapses: Relationships with Network Activity and Neighboring Excitatory Synapses , 2015, PLoS Comput. Biol..

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

[52]  L. Nathan Perkins,et al.  Unstable neurons underlie a stable learned behavior , 2016, Nature Neuroscience.

[53]  Wolfgang Maass,et al.  Searching for principles of brain computation , 2016, Current Opinion in Behavioral Sciences.

[54]  Christian K. Machens,et al.  Efficient codes and balanced networks , 2016, Nature Neuroscience.

[55]  Anna R. Chambers,et al.  A stable brain from unstable components: Emerging concepts and implications for neural computation , 2017, Neuroscience.

[56]  G. Mongillo,et al.  Intrinsic volatility of synaptic connections — a challenge to the synaptic trace theory of memory , 2017, Current Opinion in Neurobiology.

[57]  Selmaan N. Chettih,et al.  Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex , 2017, Cell.

[58]  Brenda J Butka.  Imaging , 2003, JAMA.