Colonne corticale | EPFL Graph Search

vignette|Reconstruction 3D de cinq colonnes corticales dans le cortex vibratoire du rat Une colonne corticale est un groupe de neurones situés dans le cortex cérébral dont les champs de réception sont identiques. D’ailleurs, si on introduit une microélectrode perpendiculairement à travers les différentes couches du cortex visuel, on ne rencontrera par exemple que des neurones qui ont la même préférence d’orientation, qu’ils aient des champs récepteurs simples ou complexes. Il s'agit dans cet exemple d'une colonne d'orientation. On distingue également les colonnes de dominance oculaire, ainsi que les blobs et interblobs (ou taches et intertaches) au sein des hypercolonnes du cortex visuel primaire. Cortex cérébral des mammifères Cortex cérébral Le cortex cérébral des mammifères, la matière grise qui encapsule la matière blanche, est composé de couches. Le cortex humain mesure entre 2 et d'épaisseur. Le nombre de couches est le même chez la plupart des mammifères, mais vari

Sensory memory in a model of a cortical column

Julien Mayor

Sensory memory is the first step of a complex memorization process. Sensory information is buffered in a "sensory store" for a short period. Then part of it is transferred to working memory, where information can be actively processed and, eventually, through its rehearsal, can be memorized and stored in long-term memory. Sensory memory is characterized by a high capacity and a short temporal extension. In this work we show that such a transient buffering of information is well captured in models of cortical microcircuits. Sensory information stays for a short time in the perturbations it induced to the neural activity. Two fundamental characteristics of cortical columns appear to be useful to this purpose. The first one is an approximate balance of neuronal excitation and inhibition. The second one is the sparseness of cells recurrent connections. Together, these two discriminants ensure a broad set of complex dynamical behaviours of the neural network along with the presence of a phase of sustained non-oscillatory activity with very irregular spike trains in individual neurons. We show that at the vicinity of a phase transition from a quiescent state to a phase of chaotic spiking activity, global stimuli can be buffered in the "macroscopic" neuronal activity. However if the sensory input only excites a subpart of the network, efficient information buffering is made using the detailed spiking activity of every neuron in the network. The "microscopic" structure of the network is therefore needed in order to buffer sensory information. In most situations, addition of noise enhances the buffering performance and the computational power of the network. This effect is known in the physical literature as "stochastic resonance". If the network has to achieve complex transformation of its inputs, stochastic resonance is shown to be an emergent property of the population of neurons. Both the very irregular spiking activity present in vivo and the balanced excitation and inhibition measured in cortical columns have a new functional relevance in being the neuronal substrate of an efficient sensory memory .

EPFL2005

Analysing Neuronal Network Architectures

Christian Tomm

The connections cortical neurons forms are different in each individual human or animal. Although there are known and determined large scale connections between areas of the brain that are common across individuals, the local connectivity on smaller scales varies between individuals. Connections between neurons in a single cortical column are seemingly random and were thus modeled in theoretical studies using the principle of sparse random networks. In this thesis I investigate how the simplifications of sparse random networks affect the behaviour and plausibility of the network. First, I focus on the global weight distribution in random sparse networks and test the impact of various weight distributions on network excitability. Randomsparse networks also commonly assume an independent distribution of connections in the network. Experimental results indicate that this assumption is not true in biological neuronal networks. In the second part of this thesis it is shown how changes in degree distributions of the network can be employed to improve the similarity of random networks to biological observations. A third aspect studied in this work is the impact of connection strengths on a local level. Network responses to larger stimuli in in vitro experiments are not reproducible by classical sparse randomnetworks. It is shown how changes in the distributions of local connection strengths can be used to improve the network response behaviour with respect to these experimental findings. Finally, these network adjustments are combined and the adjusted networks are tested on further data of in vivo recordings. The adjusted networks show a biologically more plausible behaviour than the random sparse network topologies in all tested scenarios.

EPFL2012

Structural architecture of the Neuronal-Glial-Vascular system

Eleftherios Zisis

Brain functionality relies on the neuronal-glial-vascular (NGV) ensemble for energy support. However, the details of the complex biological mechanisms involved in these processes and the microscopic interactions between these three components are not yet fully understood. Astrocytes, an essential component of the NGV ensemble, extend ramified processes to the vasculature and to neuronal synapses to provide neuronal energy support, homeostatic control, and multi-directional signaling with the vasculature, neurons, and neighboring astrocytes. As key links between the components of the NGV circuitry, the astrocytes are essential for drug delivery in the brain and are involved in the progression of neurodegenerative diseases. Therefore, comprehensive models of the neuronal-glial-vascular (NGV) ensemble are essential for understanding the role of astrocytes in the formation and function of the complex networks within the brain and its pathological conditions. A complete computational model of this ensemble has not yet been developed. This thesis presents the first model of a data-driven digital reconstruction of the neuronal-glial-vascular (NGV) ensemble at a micrometer anatomical resolution. Combining the sparse literature data and the few available detailed biological reconstructions, I have computationally generated the structural architecture of a neocortical NGV circuit that forms a functional column of the juvenile rat neocortex and consists of neurons, protoplasmic astrocytes, and the microvasculature, including their pairwise connectivities. This data-driven approach allows for incremental refinement as more experimental data become available, new biophysical models get published, and new questions arise. The NGV circuit is validated against a plethora of literature sources to ensure its biological fidelity: it successfully reproduces the spatial organization of the astrocytes, their morphological characteristics as well as their volume occupancy, and the overlap with their neighbors. The power of the computational model of NGV lies in its ability to serve as a framework for addressing long-standing questions that cannot be experimentally investigated due to the complexity of the microscopic systems and the limitations of current techniques to observe all components simultaneously. In this thesis, I have performed experiments to investigate why astrocytes acquire their particular shapes, the organizational principles of NGV that lead to the observed biological network architectures, and the effect of astrocytic density on endfoot organization. The circuit's structural analysis showed that astrocytes optimize their positions and spacing from each other to provide the vasculature with a uniform coverage for trophic support and signaling. By increasing the density of astrocytes in NGV circuits, I discovered a limit in astrocyte's ability to make perivascular projections because of the vascular spatial occupancy, which constrains the extent of astrocyte morphology. Thus, their role in linking vasculature to neurons constrains their organization via the proportional relationship of density and microdomain shrinkage due to contact spacing. By addressing these questions, I demonstrated how this model can serve as an exploratory tool that provides a window into the complexity of the NGV architecture.

EPFL2021