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• W2263778208 abstract "By dynamically orchestrating a plethora of mechanisms, the brain generates, integrates, and transmits electrochemical signals that ultimately allow sophisticated control of behavior on the basis of sensory input. To understand such complexity, it is important to focus on the mechanisms that are necessary to generate emergent phenomena. Computational modeling could greatly benefit our understanding of the brain by making conceptual assumptions explicit and by quickly testing new hypotheses. Here we present three classes of models that work at distinct structural-functional scales (drug-protein interaction, neuronal morphology, and network connectivity) to gain insight on how neurons integrate signals. Neuro-active drugs typically work at the molecular level by affecting one or more aspects of neuronal physiology. Robust quantitative models exist describing all major active membrane properties such as voltage-dependent ionic channels, synaptic receptors, and G protein-coupled signaling pathways. Our simulations show compelling examples that elucidate and predict the effects of chemical agonists, antagonists, and modulators in the nervous system. Once an exogenous or endogenous signal reaches the postsynaptic neuron it needs to be integrated and propagated, and dendritic morphology plays a key role in this process. In cortical pyramidal cells, individual dendritic branches store multiple features of complex synaptic input and can independently regulate the coupling between dendritic and somatic spikes. The wide range of sizes and shapes of dendritic branch points in apical trees may indicate a mechanism for locally tuning neuronal excitability. Therefore we quantified the morphological variability of these branch points from two-photon images of CA1 pyramidal neurons. We then investigated how different geometrical features might affect spike initiation and propagation using a computational model validated with corresponding glutamate uncaging experiments. Our simulations show that farther inputs on thinner dendrites were more effective at generating dendritic spikes, but less at propagating them. Given the magnitude of this effect, even subtle membrane readjustments at the branch point could drastically alter the ability of synaptic input to generate action potentials. Finally, to better understand the relationship between local connectivity and function, we investigated the role of feed-forward inhibition in the dentate gyrus Granule-MOPP cells circuitry. Our results suggest that feed-forward inhibition can change a steeply sigmoid I/O curve into a double-sigmoid curve typical of buffer systems. The addition of an intermediate plateau stabilizes the output (spiking response) over a broad dynamic range of synaptic input frequency, ensuring robust integration of noisy synaptic signals." @default.
• W2263778208 created "2016-06-24" @default.
• W2263778208 creator A5029341025 @default.
• W2263778208 creator A5087672831 @default.
• W2263778208 date "2012-01-01" @default.
• W2263778208 modified "2023-09-27" @default.
• W2263778208 title "Dynamical multiscale models to formulate testable predictions of neuronal computational properties" @default.
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