The In-Silico Neocortical Microcircuit

The neocortex is one of the most important brain regions, occupying more than 80% of the brain volume in mammals and contributing massively to its capability of perceiving and interpreting then environment. While it has been extensively studied, it is still largely unclear how it accomplishes the enormous computational tasks it is faced with. A number of structural features are conserved across the cortex, such as its vertical organization in six individual layers with a large number of morphologically unique neuron types. The exceptional horizontal uniformity, lead to the hypothesis of horizontally repeated unitary microcircuits, the idea that the cortex consists of a number of relatively uniform horizontally repeated modules, each with their own function in the concert of neural activity. While a rough mapping of function to cortical region is virtually identical between individuals and very similar between species, the extraordinary plasticity of the cortex allows functions of damaged parts to be taken over by neighboring regions. This indicates that flexible, "reprogrammable" modules implement the functions of the cortex. The Blue Brain Project (BBP) aims to build a facility for the collection and integration of available data on the fundamental structure and operating principles of the cortex to build and continuously refine a detailed, bottom-up model of the unitary microcircuit. It can then be used as a building block for the construction and simulation of larger cortical regions. A bottom-upmodel such as the one of the BBP aims to match the object being represented primarily on an underlying physical level. As such it is guaranteed to be anchored in real world physical constraints, this however comes with the disadvantage that on a higher level the behavior of the model is undetermined. The goal of this thesis is to study and characterize the model to learnmore about its function and what determines it. In order to ensure the biological relevance, this endeavour was to be tightly linked with comparisons to and validations against relevant experimental data. I concluded that the simulation of extracellular electrical signals are the right tool to link characterization and validation, due to their great importance in in-vitro work. I authored several improvements to the model to ensure the biological accuracy of the computed extracellular signals, the most important being the derivation of the local connectome inside the modeled microcircuit. Next, I implemented software for the calculation of extracellular signals during the course of a simulation on a BlueGene/P supercomputer. The method for the calculation is flexible and can discern between the contributions of individual cells. In addition to validating the signal against in-vitro data, this allowed us to analyze the composition of the signals in a way that is impossible outside of a simulation. The method revealed the crucial role of active currents in the local field potential, i.e. the relatively slow deflections in extracellular signals. We also extensively analyzed and quantified the varying contributions of individual cell types and membrane mechanisms to extracellular signals at different frequencies.

Network Activity and Plasticity

Vincent Delattre

The cortex must maintain balanced levels of neural activity to correctly integrate inputs and to provide contextually meaningful outputs. Neuronal excitation is counterbalanced by various forms of inhibition such as spike frequency adaptation, short- and long-term depression at excitatory synapses, and inhibitory cell coupling. When excitatory activity is low, homeostatic mechanisms enhance network excitability. When excitation is stronger than inhibition, network bursts of activity arise that also need to be dampened lest they run out of control. A disruption of the fine balance between excitatory and inhibitory systems is observed in an increasing numbers of brain disorders such as epilepsy, mental retardation and autism. This thesis aims to study aspects of the regulation of excitation in cortical networks at a molecular, cellular, network and disease level of investigation. We present in the first chapter an investigation of the impact of network bursts on standard spike-timing dependent plasticity (STDP) events in acute cortical brain slices. We discovered that these two kinds of events, which are the most well-established ways to induce synaptic plasticity in vitro, can block each other as well as cooperate to change the amplitude and direction of plasticity. Because the outcome of the interaction depends on the precise timing of network bursts relative to a STDP event in a single synaptic pathway, the observed network-timing dependent plasticity provides a novel mechanism for the modulation of local synaptic plasticity by the network level of activity, a form of global control of local synaptic plasticity. We hypothesized that network bursts consume extracellular resources needed to potentiate, restricting potentiation to active synapses at the burst onset while yielding depression of synapses activated at later times. As we demonstrate with simulation, this simple mechanism supports paradigm-shifting implications for models of learning, solving a persistent and fundamental problem since BCM (Bienenstock Cooper Munro, 1981) to incorporate synaptic learning into realistic neuronal networks while simultaneously establishing and maintaining excitatory-inhibitory balance. The proposed mechanism also links experimental evidence for interactions between STDP and network activity to observations of self-organized criticality in neural systems, a regime known to be optimal for information coding. In the second chapter, we investigated the effect of the loss of NLGN4 protein on synapse function and on network response to electrical stimulation. The neuroligin family of genes encodes for trans-synaptic proteins involved in synaptic differentiation and maturation. Former studies have reported that both NLGN1-KO and NLGN2-KO give rise to pathologic deregulations of the balance between excitation and inhibition in neural networks, and the presence of loss-of-function mutations in NLGN4 in a small number of autistic patients indicates a possible link between Neuroligin-4 gene and autism. Using extra-cellular stimulation and intra-cellular recordings in acute brain slices, we observed a decreased response of both excitatory and inhibitory response in NLGN4-KO animals, suggesting that NLGN4 is expressed at both excitatory and inhibitory synapses. Furthermore, we observed that the NLGN4-KO resulted in a significantly larger decrease of the excitatory response than of the inhibitory response, thus yielding a hypo-excitable network. The final chapter consists of the characterization of excitatory pathways in acute slices of the somato-sensory cortex. In the first study, we used chemical stimulation to promote the emergence of spontaneous recurrent activity and observed waves of activity originating from the cortical layer five. In the second study, we performed whole-cell patch-clamping of layer five pyramidal cells along with recording local field potentials (LFP). With the systematic layer-wise application of electrical stimulation at various frequencies coupled with LFP recordings, we obtained a frequency-dependent mapping of cortical inputs at all cortical layers at both the cellular and the network levels. Using this data, we developed a virtual framework for performing an analogous mapping in the virtual cortical column of the Blue Brain Project (BBP). This helped us understand the contribution of each cell population in the network response to stimulation. The framework also served as a validation test for the BBP cortical models.

EPFL2013

Emergent Connectivity Principles in the Neocortex

Imad Riachi

The neocortex makes up over 80% of the mammalian brain and is responsible for higher cognitive functions, processing of sensory inputs and orchestration of complex motor outputs. It is a 6-layered structure composed of billions of morphologically and electrically diverse neurons. Functionally, the basic unit is the neocortical column (NCC), a vertical structure of 0.5 mm wide, repeated millions of times across the neocortex and connected in an intricate but consistent way. In this thesis I investigated the role of morphologies (neurogeometry) in shaping the elaborate connectivity scheme within a column. First, I suggest a morphological basis for the higher than expected reciprocal connectivity reported experimentally between connected pairs, using a newly defined measure: the Reciprocity Index (RI), which arises from a purely mathematical concept and can be derived from the morphometric statistics of neurons. Second, I show that most experimentally reported synaptic patterns between different classes of neurons can be directly computed from the statistics of their morphologies, while some pathways would require additional functional mechanisms to refine an even more specific synaptic pattern. My thesis is done within the Blue Brain Project, the first comprehensive attempt to reverse-engineer the mammalian brain, starting with the somatosensory NCC of a P14 rat. I present in this thesis my contribution in constructing the framework for building biologically accurate circuitry. I explain how we start from 3D neuron morphologies obtained in vitro, repair them for slicing artefacts, build circuits and accurately detect potential synapses. I also explore how modelling of the experimental procedures can help us characterize biases and predict in vivo data from in vitro data. I finally present recent exploratory work on how to use the supercomputing power to design novel in silico protocols to investigate the emergent dynamics of the neocortical column.

EPFL2010

Tight Coupling of Astrocyte pH Dynamics to Epileptiform Activity Revealed by Genetically Encoded pH Sensors

Henry Markram

Astrocytes can both sense and shape the evolution of neuronal network activity and are known to possess unique ion regulatory mechanisms. Here we explore the relationship between astrocytic intracellular pH dynamics and the synchronous network activity that occurs during seizure-like activity. By combining confocal and two-photon imaging of genetically encoded pH reporters with simultaneous electrophysiological recordings, we perform pH measurements in defined cell populations and relate these to ongoing network activity. This approach reveals marked differences in the intracellular pH dynamics between hippocampal astrocytes and neighboring pyramidal neurons in rodent in vitro models of epilepsy. With three different genetically encoded pH reporters, astrocytes are observed to alkalinize during epileptiform activity, whereas neurons are observed to acidify. In addition to the direction of pH change, the kinetics of epileptiform-associated intracellular pH transients are found to differ between the two cell types, with astrocytes displaying significantly more rapid changes in pH. The astrocytic alkalinization is shown to be highly correlated with astrocytic membrane potential changes during seizure-like events and mediated by an electrogenic Na+/HCO3- cotransporter. Finally, comparisons across different cell-pair combinations reveal that astrocytic pH dynamics are more closely related to network activity than are neuronal pH dynamics. This work demonstrates that astrocytes exhibit distinct pH dynamics during periods of epileptiform activity, which has relevance to multiple processes including neurometabolic coupling and the control of network excitability.