Publications

Abstract

Changes in the activity profile of cortical neurons are due to phenomena at the scale of local and long-range networks. Accordingly, the states of cortical neurons and their, often abrupt, transitions – a phenomenon known as Up/Down states – are attributed to variations in the afferent neurons’ activity. However, cellular physiology and morphology may also play a role. This study examines the impact of dendritic nonlinearities, in the form of voltage-gated NMDA receptors, on the response of cortical neurons to balanced excitatory/inhibitory synaptic inputs. Using a neuron model with two segregated dendritic compartments, we compare cells with and without dendritic nonlinearities. Our analysis shows that NMDA receptors boost somatic firing in the balanced condition and increase the correlation of membrane potentials across the three compartments of the neuron model. Then we introduce controlled fluctuations in excitatory inputs and quantify the ensuing bimodality of the somatic membrane potential. We show that dendritic nonlinearities are crucial for detecting these fluctuations and initiating Up-Down states whose shape and statistics closely resemble electrophysiological data. Our results provide new insights into the mechanisms underlying cortical bistability and highlight the complex interplay between dendritic integration and network dynamics in shaping neuronal behavior.

Significance statement In several physiological states, such as sleep or quiet wakefulness, the membrane of cortical cells shows a stereotypical bistability. The cell is either fully depolarized and ready to spike or in a silent, hyperpolarized state. This dynamics, known as Up-Down states, has often been attributed to changes in the network activity. However, whether cell-specific properties, such as dendritic nonlinearity, have a role in driving the neuron’s bistability remains unclear. This study examines the issue using a model of a pyramidal cell and reveals that the presence of dendritic NMDA receptors, drives the up-down states in response to small fluctuations in the network activity.

Abstract

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modelled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding, and we show that it solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod neuron can be used efficiently in simulations of larger cortical circuits.