The Scientific Case for Brain Simulators

A key element of the European Union’s Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons. Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different levels of biological detail should therefore be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should predict not only action potentials, but also electric, magnetic, and optical signals measured at the population and system levels

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Chronic and acute implants of multi-electrode arrays that cover several mm$^2$ of neural tissue provide simultaneous access to population signals like extracellular potentials and the spiking activity of 100 or more individual neurons. While the recorded data may uncover principles of brain function, its interpretation calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Such models can facilitate the search of mechanisms underlying observed spatiotemporal activity patterns in cortex. Multi-layer spiking neuron network models of local cortical circuits covering ~1 mm$^2$ have been developed, integrating experimentally obtained neuron-type specific connectivity data and reproducing features of in-vivo spiking statistics. With forward models, local field potentials (LFPs) can be computed from the simulated spiking activity. To account for the spatial scale of common neural recordings, we extend a local network and LFP model to 4x4 mm$^2$. The upscaling preserves the neuron densities, and introduces distance-dependent connection probabilities and delays. As detailed experimental connectivity data is partially lacking, we address this uncertainty in model parameters by testing parameter combinations within biologically plausible bounds. Based on model predictions of spiking activity and LFPs, we find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations observed in sensory cortex. In contrast with the weak spike-train correlations, the correlation of LFP signals is strong and distance-dependent, compatible with experimental observations. Enhanced spatial coherence in the low-gamma band may explain the recent experimental report of an apparent band-pass filter effect in the spatial reach of the LFP.Comment: 44 pages, 9 figures, 5 table

Non-Penetrating Microelectrode Interfaces for Cortical Neuroprosthetic Applications with a Focus on Sensory Encoding: Feasibility and Chronic Performance in Striate Cortex

abstract: Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity. The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue. In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50 -- 2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics like finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly simulate a model of a local cortical microcircuit consisting of eight neuron types. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations

How models of canonical microcircuits implement cognitive functions

Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of fundamental neural elements, called canonical microcircuits. A mechanistic understanding of the interaction of these canonical microcircuits promises a better comprehension of cognitive functions as well as their potential disorders and corresponding treatment techniques. This thesis establishes a generative modeling framework that rests on canonical microcircuits and employs it to investigate composite mechanisms of cognitive functions. A generic, biologically plausible neural mass model was derived to parsimoniously represent conceivable architectures of canonical microcircuits. Time domain simulations and bifurcation and stability analyses were used to evaluate the model’s capability for basic information processing operations in response to transient stimulations, namely signal flow gating and working memory. Analysis shows that these basic operations rest upon the bistable activity of a neural population and the selectivity for the stimulus’ intensity and temporal consistency and transiency. In the model’s state space, this selectivity is marked by the distance of the system’s working point to a saddle-node bifurcation and the existence of a Hopf separatrix. The local network balance, in regard of synaptic gains, is shown to modify the model’s state space and thus its operational repertoire. Among the investigated architectures, only a three-population model that separates input-receiving and output-emitting excitatory populations exhibits the necessary state space characteristics. It is thus specified as minimal canonical microcircuit. In this three-population model, facilitative feedback information modifies the retention of sensory feedforward information. Consequently, meta-circuits of two hierarchically interacting minimal canonical microcircuits feature a temporal processing history that enables state-dependent processing operations. The relevance of these composite operations is demonstrated for the neural operations of priming and structure-building. Structure-building, that is the sequential and selective activation of neural circuits, is identified as an essential mechanism in a neural network for syntax parsing. This insight into cognitive processing proves the modeling framework’s potential in neurocognitive research. This thesis substantiates the connectionist notion that higher processing operations emerge from the combination of minimal processing elements and advances the understanding how cognitive functions are implemented in the neocortical matter of the brain.Kognitive Fähigkeiten wie Sprache, Gedächtnis und Entscheidungsfindung resultieren vermutlich aus der Interaktion vieler fundamentaler neuronaler Elemente, sogenannter kanonischer Schaltkreise. Eine vertiefte Einsicht in das Zusammenwirken dieser kanonischen Schaltkreise verspricht ein besseres Verständnis kognitiver Fähigkeiten, möglicher Funktionsstörungen und Therapieansätze. Die vorliegende Dissertation untersucht ein generatives Modell kanonischer Schaltkreise und erforscht mit dessen Hilfe die Zusammensetzung kognitiver Fähigkeiten aus konstitutiven Mechanismen neuronaler Interaktion. Es wurde ein biologisch-plausibles neuronales Massenmodell erstellt, das mögliche Architekturen kanonischer Schaltkreise generisch berücksichtigt. Anhand von Simulationen sowie Bifurkations- und Stabilitätsanalysen wurde untersucht, inwiefern das Modell grundlegende Operationen der Informationsverarbeitung, nämlich Selektion und temporäre Speicherung einer transienten Stimulation, unterstützt. Die Untersuchung zeigt, dass eine bistabile Aktivität einer neuronalen Population und die Beurteilung der Salienz des Signals den grundlegenden Operationen zugrunde liegen. Die Beurteilung der Salienz beruht dabei hinsichtlich der Signalstärke auf dem Abstand des Arbeitspunktes zu einer Sattel-Knoten-Bifurkation und hinsichtlich der Signalkonsistenz und-–vergänglichkeit auf einer Hopf-Separatrix im Zustandsraum des Systems. Die Netzwerkbalance modifiziert diesen Zustandsraum und damit die Funktionsfähigkeit des Modells. Nur ein Drei-Populationenmodell mit getrennten erregenden Populationen für Signalempfang und -emission weist die notwendigen Bedingungen im Zustandsraum auf und genügt der Definition eines minimalen kanonischen Schaltkreises. In diesem Drei-Populationenmodell erleichtert ein Feedbacksignal die Speicherfähigkeit für sensorische Feedforwardsignale. Dementsprechend weisen hierarchisch interagierende minimale kanonische Schaltkreise ein zeitliches Verarbeitungsgedächtnis auf, das zustandsabhängige Verarbeitungsoperationen erlaubt. Die Bedeutung dieser konstitutiven Operationen wird für die neuronalen Operationen Priming und Strukturbildung verdeutlicht. Letztere wurde als wichtiger Mechanismus in einem Netzwerk zur Syntaxanalyse identifiziert und belegt das Potential des Modellansatzes für die neurokognitive Forschung. Diese Dissertation konkretisiert die konnektionistische Ansicht höhere Verarbeitungsoperationen als Ergebnis der Kombination minimaler Verarbeitungselemente zu verstehen und befördert das Verständnis für die Frage wie kognitive Fähigkeiten im Nervengewebe des Gehirns implementiert sind

The Computational Properties of a Simplified Cortical Column Model

<div><p>The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.</p></div