Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study

Abstract

Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological model of Hebbian plasticity at the location of the synapse. Our model describes the Hebbian condition of joint activity of pre-and postsynaptic neurons in a compact form as the interaction of the glutamate trace left by a presynaptic spike with the time course of the postsynaptic voltage. Instead of simulating the voltage, we test the model using experimentally recorded dendritic voltage traces in hippocampus and neocortex. We find that the time course of the voltage in the neighborhood of a stimulated synapse is a reliable predictor of whether a stimulated synapse undergoes potentiation, depression, or no change. Our computational model can explain the existence of different -at first glance seemingly paradoxical-outcomes of synaptic potentiation and depression experiments depending on the dendritic location of the synapse and the frequency or timing of the stimulation.

Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

“…In summary, we have investigated the phenomenon of synchronization on adaptive networks with heterogeneous plasticity rules. In particular, we have modeled systems with distance-dependent plasticity as they have been found in neuronal networks experimentally [64][65][66][67] as well as computational models [68]. For the realization, we have used a ring-like network architecture and associated the distance of two nodes with the distance of their placement on the ring.…”

“…Among all structural aspects, an important factor for the specific form of the plasticity rule is the distance between neurons [64][65][66]. More specifically, it has been found that the plasticity rule between proximal or distal neurons, respectively, can change from Hebbian-like to anti-Hebbian-like [67,68].…”

Synchronization in networks with heterogeneous adaptation rules and applications to distance-dependent synaptic plasticity

“…In summary, we have investigated the phenomenon of synchronization on adaptive networks with heterogeneous plasticity rules. In particular, we have modeled systems with distance-dependent plasticity as they have been found in neuronal networks experimentally [64,67,65,66] as well as computational models [68]. For the realization, we have used a ring-like network architecture and associated the distance of two nodes with the distance of their placement on the ring.…”

GluN2B-NMDAR subunit contribution on synaptic plasticity: A phenomenological model for CA3-CA1 synapses

“…We developed a model of synaptic modifications based on the well-established phenomenological models (Clopath et al, 2010 ; Meissner-Bernard et al, 2020 ) and integrated the separated influence of postsynaptic NMDAR subunits GluN2A-NMDAR and GluN2B-NMDAR to account for the crucial effect of GluN2B-NMDAR in hippocampal synaptic plasticity. We utilized two computational models of CA1 pyramidal neuron: a modified two-compartmental Pinsky-Rinzel model (Pinsky and Rinzel, 1994 ; Ferguson and Campbell, 2009 ) to validate the extended synaptic plasticity model, and a multicompartmental model (Migliore et al, 2018 ) to study the influence of GluN2B-NMDAR properties on synaptic strength modifications at a cluster of CA3-CA1 synapses distributed randomly onto apical dendrites of CA1 neuron.…”

“…Biophysical models, embedded into detailed compartmental models, account for the factors shaping synaptic plasticity—different membrane mechanisms of the dendritic tree, dendritic integration, morphological features, pattern of pre- and postsynaptic spiking (Poirazi and Papoutsi, 2020 ). The models include complex biochemical reactions of calcium induced kinase and phosphatase activation that underlie synaptic modifications (Bhalla and Iyengar, 1999 ; Graupner and Brunel, 2007 ; Saudargiene et al, 2015 ; Jędrzejewska-Szmek et al, 2017 ; Mäki-Marttunen et al, 2020 ), represent molecular cascades applying simplified functions, dependent on postsynaptic NMDAR-mediated intracellular calcium transients (Shouval et al, 2002 ; Graupner and Brunel, 2012 ; Standage et al, 2014 ; Chindemi et al, 2022 ), use formulation on the level of postsynaptic voltage (Clopath et al, 2010 ; Meissner-Bernard et al, 2020 ), utilize a kinetic model of synapse upregulation and downregulation mediated by NMDAR and based on the precise timing of pre and post spikes (Senn et al, 2001 ), or describe the weight change in a phenomenological way taking into account spike timing (Gerstner et al, 1996 ; Song and Abbott, 2000 ; Song et al, 2000 ). Phenomenological models are efficient, but lack biological realism; on the other hand, detailed models, sensitive to NMDAR functioning, are not easily applied in network simulations as they include many complex biochemical reactions, large parameter space, and are computationally expensive.…”