Cortical column | EPFL Graph Search

A cortical column is a group of neurons forming a cylindrical structure through the cerebral cortex of the brain perpendicular to the cortical surface. The structure was first identified by Mountcastle in 1957. He later identified minicolumns as the basic units of the neocortex which were arranged into columns. Each contains the same types of neurons, connectivity, and firing properties. Columns are also called hypercolumn, macrocolumn, functional column or sometimes cortical module. Neurons within a minicolumn (microcolumn) encode similar features, whereas a hypercolumn "denotes a unit containing a full set of values for any given set of receptive field parameters". A cortical module is defined as either synonymous with a hypercolumn (Mountcastle) or as a tissue block of multiple overlapping hypercolumns. Cortical columns are proposed to be the canonical microcircuits for predictive coding, in which the process of cognition is implemented through a hierarchy of identical microcircui

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

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