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When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Related Experiment Video

Updated: Mar 3, 2026

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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Intrinsically-generated fluctuating activity in excitatory-inhibitory networks.

Francesca Mastrogiuseppe1,2, Srdjan Ostojic1

  • 1Laboratoire de Neurosciences Cognitives, INSERM U960, École Normale Supérieure - PSL Research University, Paris, France.

Plos Computational Biology
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Summary

Excitation in neural networks significantly boosts firing rates and creates distinct fluctuating activity regimes. These findings, observed in rate and spiking neuron models, offer insights into brain dynamics.

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Area of Science:

  • Computational Neuroscience
  • Neural Network Dynamics

Background:

  • Recurrent neural networks exhibit diverse dynamics based on synaptic connectivity.
  • Chaotic activity is observed in random rate unit networks, but its mechanism in spiking neurons remains unclear.
  • Previous studies derived dynamical mean field (DMF) equations for rate networks but primarily analyzed purely inhibitory cases.

Purpose of the Study:

  • To investigate how excitation influences fluctuating activity in recurrent neural networks.
  • To compare dynamics in excitatory-inhibitory networks with those in purely inhibitory networks.
  • To explore the emergence of different dynamical regimes in response to varying coupling strengths.

Main Methods:

  • Utilized a simplified excitatory-inhibitory network architecture for tractable dynamical mean field (DMF) equation analysis.
  • Investigated networks with segregated excitatory and inhibitory populations and positive firing rates.
  • Extended findings to more general architectures and noisy rate networks mimicking spiking activity, and validated in integrate-and-fire neuron networks.

Main Results:

  • Excitation significantly increases mean firing rates compared to purely inhibitory networks.
  • Two distinct fluctuating regimes emerge: stabilization by recurrent inhibition (moderate coupling) or by activity bounds (strong coupling).
  • Signatures of the second dynamical regime were observed in integrate-and-fire spiking neuron networks.

Conclusions:

  • The presence of excitation qualitatively alters network dynamics, leading to enhanced firing rates and novel fluctuating regimes.
  • Network stability in excitatory-inhibitory systems can be achieved through different mechanisms depending on coupling strength.
  • Findings bridge the gap between rate and spiking neuron models, suggesting conserved dynamical principles.