Biology subjects

Markram, H.

Publications and source records attributed to Markram, H..

5 recordsLinked to original sources

bioRxiv2018

Objective Classification of Neocortical Pyramidal Cells

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies. This is because of a lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies: the shapes of somata, dendrites, and axons. Even for neurons that are visually different to non-experts, there is no common ground to consistently distinguish morphological types. We found that objective classification is possible with methods from algebraic topology, and that the dendritic arbor is sufficient for reliable identification of distinct types of PCs. We also provide a solution for the more challenging problem of whether two similar neurons belong to different types or to a continuum of the same type. Using this scheme, we objectively identify seventeen types of PCs in the rat somatosensory cortex. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

neuroscience↗

bioRxiv2018

Data-driven modeling of cholinergic modulation of neural microcircuits: bridging neurons, synapses, and network states

1Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network states. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the anatomy and physiology of cholinergic modulation of the neocortex. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to predict how ACh affects different types of neurons and synapses, and consequently alters global network states. Preliminary simulations not only corroborate the long-standing notion that ACh desynchronizes network activity, but also reveal a potentially finegrained control over a spectrum of neocortical states. We show that low levels of ACh, such as those during sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as those during wakefulness, govern fast oscillations and network asynchrony. In addition, network states modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the differential regulation of neurons and synapses caused by increasing levels of ACh that enhances cellular excitability by increasing neuronal activity and decreases the efficacy of local synaptic transmission by altering neurotransmitter release probability. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulation in neural information processing.

neuroscience↗

bioRxiv2018

Cortical Reliability Amid Noise and Chaos

Typical responses of cortical neurons to identical sensory stimuli are highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is constrained by the intrinsic variability of cortical circuits, which remains largely unexplored. Here, we quantified this intrinsic variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We found that stochastic neurotransmitter release is a critical component of this variability, which, amplified by recurrent connectivity, causes rapid chaotic divergence with a time constant on the order of 10-20 milliseconds. Surprisingly, weak thalamocortical stimuli can transiently overcome the chaos, and induce reliable spike times with millisecond precision. We show that this effect relies on recurrent cortical connectivity, and is not a simple effect of feed-forward thalamocortical input. We conclude that recurrent cortical architecture supports millisecond spike-time reliability amid noise and chaotic network dynamics, resolving a long-standing debate.

neuroscience↗

bioRxiv2017

Shaping dendritic NMDA spikes by timed synaptic inhibition: Implications for the I/O properties of cortical neurons

The pronounced, long lasting, regenerative NMDA-spike is initiated in individual dendritic branches of different types of neurons and is known to play a key role in dendritic computations and plasticity. Combining dynamic system theory and computational approaches, we systematically analyzed how timed synaptic inhibition activated during the NMDA-spike time-course, sculpts this spike and its associated current influx. When impinging on its early phase, individual GABAergic synapse activation transiently, but strongly, dampened the NMDA-spike; later inhibition prematurely terminated it. This inhibition reduced the NMDA-mediated Ca2+ current by up to 60%. NMDA-spikes in distal dendritic branches/spines are longer lasting and more resilient to inhibition, and thus enhance synaptic plasticity at these branches. Examination of this sensitivity of the NMDA-spike to well-timed synaptic inhibition suggests that NMDA-spikes are highly modifiable signals which enable sparse weak distal dendritic inhibition to finely tune both the neuron's output spikes as well as the branch's/spine's Ca2+ current associated with the local NMDA spike.

neuroscience↗

bioRxiv2017

Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

Background We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender.\n\nResults Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains [~]210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback.\n\nConclusion A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters.

neuroscience↗