Voltage-based inhibitory synaptic plasticity: network regulation, diversity, and flexibility

Abstract

Neural networks are highly heterogeneous while homeostatic mechanisms ensure that this heterogeneity is kept within a physiologically safe range. One of such homeostatic mechanisms, inhibitory synaptic plasticity, has been observed across different brain regions. Computationally, however, inhibitory synaptic plasticity models often lead to a strong suppression of neuronal diversity. Here, we propose a model of inhibitory synaptic plasticity in which synaptic updates depend on presynaptic spike arrival and postsynaptic membrane voltage. Our plasticity rule regulates the network activity by setting a target value for the postsynaptic membrane potential over a long timescale. In a feedforward network, we show that our voltage-dependent inhibitory synaptic plasticity (vISP) model regulates the excitatory/inhibitory ratio while allowing for a broad range of postsynaptic firing rates and thus network diversity. In a feedforward network in which excitatory and inhibitory neurons receive correlated input, our plasticity model allows for the development of co-tuned excitation and inhibition, in agreement with recordings in rat auditory cortex. In recurrent networks, our model supports memory formation and retrieval while allowing for the development of heterogeneous neuronal activity. Finally, we implement our vISP rule in a model of the hippocampal CA1 region whose pyramidal cell excitability differs across cells. This model accounts for the experimentally observed variability in pyramidal cell features such as the number of place fields, the fields sizes, and the portion of the environment covered by each cell. Importantly, our model supports a combination of sparse and dense coding in the hippocampus. Therefore, our voltage-dependent inhibitory plasticity model accounts for network homeostasis while allowing for diverse neuronal dynamics observed across brain regions.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

“…However, implementing a mechanism that dynamically shifts these thresholds in the opposite directions for excitatory vs. inhibitory plasticity based on experimental evidence (Keck et al, 2017), suggests that this match is not needed at all times. An interesting consequence from this dynamic threshold shift is a diversity of firing rates, which agrees with experimental data (Buzsáki and Mizuseki, 2014) and has recently been also achieved in different types of models (Pedrosa and Clopath, 2020; Agnes and Vogels, 2021).…”

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

“…For an inhibitory plasticity rule to successfully stabilize postsynaptic excitatory firing rates, it needs to implement a negative feedback mechanism whereby for high postsynaptic firing rates the inhibitory synaptic strength increases, while for low rates the inhibitory strength decreases, as is the case for our rule as well as others [44][45][46]. The nonlinear inhibitory plasticity we propose in our study is probably closest to a recent implementation of inhibitory plasticity via the voltage rule [80], since the voltage rule has a nonlinear dependency on postsynaptic firing rates [81].…”

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications

“…However, we speculate that learning an E/I balance in such systems would be comparatively slow as negative deviations are still bounded from below. While we have employed a homeostatic plasticity rule that establishes a target for the total input, we assume that plasticity rules processing deviations from a target membrane potential ( 56 , 57 ) can be equally used. While we did not investigate all forms of plasticity, we note that plasticity rules that do not establish a homeostatic firing rate in PCs may be inappropriate to learning nPE and pPE neurons ( SI Appendix , Fig.…”