Biology subjects

Quaresima, A.

Publications and source records attributed to Quaresima, A..

3 recordsLinked to original sources

bioRxiv2024

Nonlinear dendritic integration supports Up-Down states in single neurons

Changes in the activity profile of cortical neurons are due to effects at the scale of local and long-range networks. Accordingly, abrupt transitions in the state of cortical neurons--a phenomenon known as Up-Down states--have been attributed to variation in the activity of afferent neurons. However, cellular physiology and morphology may also play a role in causing Up-Down states. This study examines the impact of dendritic nonlinearities, particularly those mediated by voltage-dependent NMDA receptors, on the response of cortical neurons to balanced excitatory/inhibitory synaptic inputs. Using a neuron model with two segregated dendritic compartments, we compared cells with and without dendritic nonlinearities. NMDA receptors boosted somatic firing in the balanced condition and increased the correlation between membrane potentials across the compartments of the neuron model. Dendritic nonlinearities elicited strong bimodality in the distribution of the somatic potential when the cell was driven with cortical-like input. Moreover, dendritic nonlinearities could detect small input fluctuations and lead to Up-Down states whose statistics and dynamics closely resemble electrophysiological data. Up-Down states also occurred in recurrent networks with oscillatory firing activity, as in anaesthetized animal models, when dendritic NMDA receptors were partially disabled. These findings suggest that there is a dissociation between cellular and network-level features that could both contribute to the emergence of Up-Down states. Our study highlights the complex interplay between dendritic integration and activity-driven dynamics in the origin of cortical bistability. Significance statementIn several physiological states, such as sleep or quiet wakefulness, the membrane of cortical cells shows characteristic bistability. Cells are 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 network activity. However, whether cell-specific properties, such as dendritic nonlinearities, play a role in neuronal bistability remains unclear. This study uses a dendritic model of a pyramidal cell and shows that the presence of NMDA receptors drives the Up-Down states in response to small fluctuations in network activity. Thus, single cells can enter the Up-Down state dynamics independently of the ongoing network activity.

neuroscience↗

bioRxiv2023

Dendrites support formation and reactivation of sequential memories through Hebbian plasticity

Storage and retrieval of sequences require memory that is sensitive to the temporal order of features. For example, in human language, words that are stored in long-term memory are retrieved based on the order of phonemes. It is currently unknown whether Hebbian learning supports the formation of memories that are structured in time. We investigated whether word-like memories can emerge in a network of neurons with dendritic structures. Dendrites provide neuronal processing memory on the order of 100 ms and have been implicated in structured memory formation. We compared a network of neurons with dendrites and two networks of point neurons that have previously been shown to acquire stable long-term memories and process sequential information. The networks were equipped with voltage-based, spike-timing dependent plasticity (STDP) and were homeostatically balanced with inhibitory STDP. In the learning phase, networks were exposed to phoneme sequences and word labels, which led to the formation of overlapping cell assemblies. In the retrieval phase, networks only received phoneme sequences as input, and we measured the firing activity of the corresponding word populations. The dendritic network correctly reactivated the word populations with a success rate of 80%, including words composed of the same phonemes in a different order. The networks of point neurons reactivated only words that contained phonemes that were unique to these words and confused words with shared phonemes (success rate below 20%). These results suggest that the slow timescale and non-linearity of dendritic depolarization allowed neurons to establish connections between neural groups that were sensitive to serial order. Inhibitory STDP prevented the potentiation of connections between unrelated neural populations during learning. During retrieval, it maintained the dendrites hyperpolarized and limited the reactivation of incorrect cell assemblies. Thus, the addition of dendrites enables the encoding of temporal relations into associative memories.

neuroscience↗

bioRxiv2022

The Tripod neuron: a minimal structural reduction of the dendritic tree

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 dendritic computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modeled 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 neuron encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding and we show that the neuron solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod neuron can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod model can be used efficiently in simulations of larger cortical circuits.

neuroscience↗