Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

By Tim P. Vogels, Henning Sprekeler, Friedemann Zenke, Claudia Clopath, Wulfram Gerstner. 2011

766 citations recorded by the source corpus.

Plasticity at inhibitory synapses maintains balanced excitatory and inhibitory synaptic inputs at cortical neurons. Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “For instance, there are cases in which induction of ISP alone can flip the sign of subsequent plasticity at excitatory synapses [89].”
Theories of Error Back-Propagation in the Brain (2019, background): “Nevertheless, Vogels and colleagues [60] demonstrated that a learning rule in which the direction of modi ﬁ cation depends on activity of neurons in equilibrium (Figure 1E), as in the predictive coding model, can arise from an alternate form of spike-time-dependent plasticity.”
Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex (2018, unknown): “Tuning similarity metrics for individual visual features were computed as follows: ∆TF = |T Fpre f −T F ′ pre f | (22) ∆SF = |SFpre f −SF ′ pre f | (23) ∆Speed = |(T Fpre f −SFpre f )− (T F ′ pre f −SF ′ pre f )| (24) ∆Ori = 90◦−||φ −φ ′|−90◦| (25) where φ and φ ′ are the preferred orientations of e”
Receptive field formation by interacting excitatory and inhibitory synaptic plasticity (2016, background): “The inhibitory plasticity rule aims to establish a stimulus-specific detailed balance, with mean firing rates that are close to the target rate ρ0 for all stimuli individually (Vogels et al., 2011).”
Plasticity of Cortical Excitatory-Inhibitory Balance (2015, background): “One form of inhibitory STDP was postulated by Vogels et al. (2011) to account for changes in synaptic tuning www.annualreviews.org • Cortical Synaptic Plasticity 201 A nn u.”
Inhibitory synaptic plasticity: spike timing-dependence and putative network function (2013, background): “For neocortical neurons, Vogels et al. (2011) and Luz and Shamir (2012) both hypothesized that iSTDP was the mechanism underlying the rebalancing of excitation and inhibition that occurs in A1 described above (Froemke et al., 2007).”
Models of Metaplasticity: A Review of Concepts (2015, background): “…is a crucial need for compensatory mechanisms that may interfere or act in concert with Hebbian learning to not only keep the neuron’s firing rate within a certain range, but also guarantee this balance (Vogels et al., 2011), or the irregularity of the spiking discharge (Pozzorini et al., 2013).”
Dendritic cortical microcircuits approximate the backpropagation algorithm (2018, background): “This learning rule of apical-targeting interneuron synapses can be thought of as a dendritic variant of the homeostatic inhibitory plasticity proposed by Vogels et al. (2011); Luz and Shamir (2012).”
Resting-state EEG correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study. (2024, background): “The balance between excitation and inhibition is essential for maintaining the dynamic range of neural responses (Froemke, 2015; Vogels et al., 2011), a fundamental aspect that underpins cognitive adaptability and the efficiency of attentional networks.”