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Inhibitory connectivity defines the realm of excitatory plasticity

Nature Neurosciencevolume 21pages14631470 (2018) | Download Citation


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.

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We thank L. Abbott and D. Hansel for their careful reading of our manuscript and their insightful comments. This work was performed in the framework of the the France-Israel Center for Neural Computation and was supported by the Israel Science Foundation (Grant No. 757/16, Y.L.), the DFG (CRC 1080, Y.L. and S.R.), the Gatsby Charitable Foundation (Y.L.), and by ANR (14-NEUC-0001-01 and 13-BSV4-0014-02, G.M.).

Author information


  1. Centre National de la Recherche Scientifique (CNRS), Paris, France

    • Gianluigi Mongillo
  2. Centre de Neurophysique, Physiologie et Pathologie (CNPP), Université Descartes, Paris, France

    • Gianluigi Mongillo
  3. Institute of Physiology, Focus Program Translational Neuroscience, University Medical Center, Johannes Gutenberg University, Mainz, Germany

    • Simon Rumpel
  4. Department of Neurobiology, the Federmann Center for the Study of Rationality and the Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel

    • Yonatan Loewenstein


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S.R. performed the experiments, G.M., S.R. and Y.L. analyzed the data, G.M. and Y.L. developed the theory, G.M. performed the numerical simulations, G.M., S.R. and Y.L. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Gianluigi Mongillo or Yonatan Loewenstein.

Integrated supplementary information

  1. Supplementary Figure 1 Sensitivity of the firing rate of neurons to the excitatory and inhibitory inputs.

    (a), The firing rate of the excitatory neuron of Fig. 4a in response to its excitatory input, measured in units of standard deviation relative to its mean. Line corresponds to average rate and shaded area denote mean ± 2 standard deviations. (b), same as in a for the inhibitory input. Note that the firing rate of the neuron is far more sensitive to its inhibitory input than to its excitatory input. (c), and (d), same as (a), and (b), for the inhibitory neuron in Fig. 4b.

  2. Supplementary Figure 2 Dependence of the effect of rewiring on the distributions of firing rates.

    The circles denote the correlation coefficients of the vectors of the firing rates of excitatory (blue) and inhibitory (red), as in Fig. 3, before and after the rewiring of the different synaptic types (from left to right: Top, E→E, E→I; Bottom, I→E, I→I), as a function of the relative averaged squared firing rate of the excitatory neurons. Each circle corresponds to a different value of pair of external inputs. Black diamonds correspond to cortical parameters (Fig. 3). The correlation coefficients and firing rates were computed using the mean field approximation.

  3. Supplementary Figure 3 The effect of different levels of heterogeneous addition of E→E connections to targeted neurons.

    Same as in Fig. 7d, the autocorrelogram of the vectors of firing rates of the excitatory ((a), blue) and inhibitory ((b), red) neurons as a function of the fraction of excitatory neurons that were targeted. Dotted line denotes a ΔC = 10% increase in the number of connections (probability of input from previously-unconnected excitatory neurons is 0.025); solid line denoted a ΔC = 20% increase in the number of connections (probability of new connections is 0.05, same as Fig. 7d); Dashed line denotes a ΔC = 40% increase (probability 0.1).

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