Three operational regimes. ALeft: stationary input potential resulting from the Green’s function of a ball-and-stick model receiving input currents localized to the soma (X = 0,Is= 5) and in the dendrite (X= 8, Id= 5). Right: Schematic illustration of spike-triggered effects. A somatic spike causes a refractory period in the soma and a BAP along the dendrite. A dendritic spike creates a short-lived depolarization in the soma. b,vsFiring frequency (b) and CV (vs) against the somatic input current for three dendritic input forces. The black dotted line corresponds to the absence of dendritic spikes. The black points correspond to the points obtained by Monte Carlo simulation. dCV versus firing frequency for three dendritic input forces. eSchematic of the two-compartment model with noisy inlet injections. Fthe f–I and CV–I curves highlighting the transition between three operational regimes: the Cox regime where the average somatic intake ( Idet) the more the amplitude of the dendritic spikes is below the threshold, the bursting regime where the average somatic input plus the amplitude of the dendritic spikes is above the threshold, and the average-driven regime where the average somatic input alone is greater than the threshold. For this simulation, the amplitude of the dendritic spikes was set to D = 0.3. gVoltage traces corresponding to the three regimes. h, The strength of the somatic input associated with the CV peak (Idet∗{I}_{\rm{det}}^{* }) displayed relative to the amplitude of the dendritic peak. Credit: Nature Computational Science(2023). DOI: 10.1038/s43588-023-00580-6
The inner workings of the human brain are an unfolding mystery, and Dr. Richard Naud of the University of Ottawa’s Faculty of Medicine has led a compelling new study that brings us closer to answering these big questions.
The study’s findings have important implications for theories of learning and working memory and could potentially contribute to future developments in artificial intelligence (AI), as AI developers and programmers monitor the work from Dr. Naud and other leading neuroscientists.
Published inNature Computational Sciencestudy addresses the multi-layered mystery of the ‘response variability’ of neurons, brain cells that use electrical signals and chemicals to process information, and gives the green light to all the remarkable aspects of consciousness human.
The results reveal the details of how neuronal variability is controlled by dendrites, the antenna that extends from each neuron to receive synaptic inputs into our own personal neuronal communication networks. The rigorous study establishes properties of dendrites that powerfully control flow variability, a property that controls synaptic plasticity in the brain.
“The intensity of a neuron’s response is controlled by the inputs sent to its nucleus, but the variability of a neuron’s response is controlled by the inputs sent to its small antennae, the dendrites,” explains Dr. Naud , associate professor in the department of the Faculty of Medicine. of Cellular and Molecular Medicine and the Department of Physics at the University of Ottawa.
“This study further establishes how individual neurons can have this crucial property of controlling response variability with their inputs.”
Dr. Naud suspected that if the mathematical framework he had used to describe the cell bodies of neurons was extended to take into account their dendrites, then they might have the chance to effectively simulate networks of neurons with active dendrites.
Check out the contribution of Zachary Friedenberger, a Ph.D. student in the Department of Physics and member of Dr. Naud’s laboratory, with a background in theoretical physics to solve theoretical and mathematical challenges in record time. Fast forward to the completed study: the model predictions were validated through analysis of in vivo recording data and observed across a wide range of model parameters.
“He was able to solve mathematical problems in record time and solve a number of theoretical challenges that I had not anticipated,” says Dr. Naud.
Dr. Naud believed their technique could provide insight into the neuronal response to varying inputs. So they began working on a technique capable of calculating statistics from a neuronal model with an active dendrite.
One of the work’s reviewers noted that the theoretical analysis “provides key insights into biological computation and will be of interest to a broad audience of computational and experimental neuroscientists.”
More information:
Zachary Friedenberger et al, Dendritic excitability controls overdispersion,Nature Computational Science(2023). DOI: 10.1038/s43588-023-00580-6
Provided by the University of Ottawa
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