Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits

It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 ± 2.7% (mean ± SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits

A compact spike-timing-dependent-plasticity circuit for floating gate weight implementation

AbstractSpike timing dependent plasticity (STDP) forms the basis of learning within neural networks. STDP allows for the modification of synaptic weights based upon the relative timing of pre- and post-synaptic spikes. A compact circuit is presented which can implement STDP, including the critical plasticity window, to determine synaptic modification. A physical model to predict the time window for plasticity to occur is formulated and the effects of process variations on the window is analyzed. The STDP circuit is implemented using two dedicated circuit blocks, one for potentiation and one for depression where each block consists of 4 transistors and a polysilicon capacitor. SpectreS simulations of the back-annotated layout of the circuit and experimental results indicate that STDP with biologically plausible critical timing windows over the range from 10µs to 100ms can be implemented. Also a floating gate weight storage capability, with drive circuits, is presented and a detailed analysis correlating weights changes with charging time is given

Data-driven Neuroscience: Enabling Breakthroughs Via Innovative Data Management

Scientists in all disciplines increasingly rely on simulations to develop a better understanding of the subject they are studying. For example the neuroscientists we collaborate with in the Blue Brain project have started to simulate the brain on a supercomputer. The level of detail of their models is unprecedented as they model details on the subcellular level (e.g., the neurotransmitter). This level of detail, however, also leads to a true data deluge and the neuroscientists have only few tools to efficiently analyze the data. This demonstration showcases three innovative spatial management solutions that have substantial impact on computational neuroscience and other disciplines in that they allow to build, analyze and simulate bigger and more detailed models. More particularly, we visualize the novel query execution strategy of FLAT, an index for the scalable and efficient execution of range queries on increasingly detailed spatial models. FLAT is used to build and analyze models of the brain. We furthermore demonstrate how SCOUT uses previous query results to prefetch spatial data with high accuracy and therefore speeds up the analysis of spatial models. We finally also demonstrate TOUCH, a novel in-memory spatial join, that speeds up the model building process

Spatial Data Management Challenges in the Simulation Sciences

Scientists in many disciplines have progressively been using simulations to better understand the natural systems they study. Faster hardware, as well as increasingly precise instruments, allow the construction and simulation of progressively advanced models of various systems. Governed by algorithms and equations, the spatial models at the core of simulations are changed and updated at every simulation step through spatial queries, implementing massive updates. Therefore, the efficient execution of these numerous spatial queries is essential. Two reasons render current spatial indexes inadequate for simulation applications. First, to ensure quick access to data, most of the spatial models in simulations are stored in memory. Most spatial access methods, however, have been optimized for use on disk and are not efficient in memory. Second, in every time step of a simulation, almost all spatial elements change their position, challenging update mechanisms for spatial indexes. In this paper we discuss how these challenges create opportunities for exciting data management research

Doctor of Philosophy

dissertationIt is imperative to obtain a complete network graph of at least one representative retina if we are to fully understand vertebrate vision. Synaptic connectomics endeavors to construct such graphs. Though previously prevented by hardware and software limitations, the creation of customized viewing and analysis software, affordable data storage, and advances in electron imaging platform control now permit connectome assembly and analysis. The optimal strategy for building complete connectomes utilizes automated transmission electron imaging with 2 nm or better resolution, molecular tags for cell identification, open access data volumes for navigation, and annotation with open source tools to build three-dimensional cell libraries, complete network diagrams, and connectivity databases. In a few years, the first retinal connectome analyses reveal that many well-studied cells participate in much richer networks than expected. Collectively, these results impel a refactoring of the inner plexiform layer, while providing proof of concept for connectomics as a game-changing approach for a new era of scientific discovery

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions. In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORMERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8

Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column

The cerebral cortex occupies nearly 80% of the entire volume of the mammalian brain and is thought to subserve higher cognitive functions like memory, attention and sensory perception. The neocortex is the newest part in the evolution of the cerebral cortex and is perhaps the most intricate brain region ever studied. The neocortical microcircuit is the smallest Œecosystem‚ of the neocortex that consists of a rich assortment of neurons, which are diverse in both their morphological and electrical properties. In the neocortical microcircuit, neurons are horizontally arranged in 6 distinct sheets called layers. The fundamental operating unit of the neocortical microcircuit is believed to be the Neocortical Column (NCC). Functionally, a single NCC is an arrangement of thousands of neurons in a vertical fashion spanning across all the 6 layers. The structure of the entire neocortex arises from a repeated and stereotypical arrangement of several thousands of such columns, where neurons transmit information to each other through specialized points of information transfer called synapses. The dynamics of synaptic transmission can be as diverse as the neurons defining a connection and are crucial to foster the functional properties of the neocortical microcircuit. The Blue Brain Project (BBP) is the first comprehensive endeavour to build a unifying model of the NCC by systematic data integration and biologically detailed simulations. Through the past 5 years, the BBP has built a facility for a data-constraint driven approach towards modelling and integrating biological information across multiple levels of complexity. Guided by fundamental principles derived from biological experiments, the BBP simulation toolchain has undergone a process of continuous refinement to facilitate the frequent construction of detailed in silico models of the NCC. The focus of this thesis lies in characterizing the functional properties of in silico synaptic transmission by incorporating principles of synaptic communication derived through biological experiments. In order to study in silico synaptic transmission it is crucial to gain an understanding of the key players influencing the manner in which synaptic signals are processed in the neocortical microcircuit - ion channel kinetics and distribution profiles, single neuron models and dynamics of synaptic pathways. First, by means of exhaustive literature survey, I identified ion channel kinetics and their distribution profiles on neocortical neurons to build in silico ion channel models. Thereafter, I developed a prototype framework to analyze the somatic and dendritic features of single neuron models constrained by ion channel kinetics. Finally, within a simulation framework integrating the ion channels, single neuron models and dynamics of synaptic transmission, I replicated in vitro experimental protocols in silico, to characterize the transmission properties of monosynaptic connections. These synaptic connections, arising from the axo-dendritric apposition of neuronal arbours were sampled across many instances of in silico NCC models constructed a priori through the BBP simulation toolchain. In this thesis, I show that when principles of synaptic transmission derived from in vitro experiments are incorporated to model in silico synaptic connections, the resulting anatomy and physiology of synaptic connections modelled from elementary biological rules closely match in vitro data. This thesis work demonstrates that the average synaptic response properties in silico are robust to perturbations in the anatomical and physiological properties of modelled connections in the local neocortical microcircuit. A fundamental discovery through this thesis is an insight into the function of the local neocortical microcircuit by examining the effect of morphological diversity on in silico synaptic transmission. I demonstrate here that intrinsic morphological diversity confers an invariance to the average synaptic response properties in silico in the local neocortical microcircuit, termed "microcircuit level robustness and invariance"

Understanding the brain through its spatial structure

The spatial location of cells in neural tissue can be easily extracted from many imaging modalities, but the information contained in spatial relationships between cells is seldom utilized. This is because of a lack of recognition of the importance of spatial relationships to some aspects of brain function, and the reflection in spatial statistics of other types of information. The mathematical tools necessary to describe spatial relationships are also unknown to many neuroscientists, and biologists in general. We analyze two cases, and show that spatial relationships can be used to understand the role of a particular type of cell, the astrocyte, in Alzheimer's disease, and that the geometry of axons in the brain's white matter sheds light on the process of establishing connectivity between areas of the brain. Astrocytes provide nutrients for neuronal metabolism, and regulate the chemical environment of the brain, activities that require manipulation of spatial distributions (of neurotransmitters, for example). We first show, through the use of a correlation function, that inter-astrocyte forces determine the size of independent regulatory domains in the cortex. By examining the spatial distribution of astrocytes in a mouse model of Alzheimer's Disease, we determine that astrocytes are not actively transported to fight the disease, as was previously thought. The paths axons take through the white matter determine which parts of the brain are connected, and how quickly signals are transmitted. The rules that determine these paths (i.e. shortest distance) are currently unknown. By measurement of axon orientation distributions using three-point correlation functions and the statistics of axon turning and branching, we reveal that axons are restricted to growth in three directions, like a taxicab traversing city blocks, albeit in three-dimensions. We show how geometric restrictions at the small scale are related to large-scale trajectories. Finally we discuss the implications of this finding for experimental and theoretical connectomics

Identifying, tabulating, and analyzing contacts between branched neuron morphologies

Simulating neural tissue requires the construction of models of the anatomical structure and physiological function of neural microcircuitry. The Blue Brain Project is simulating the microcircuitry of a neocortical column with very high structural and physiological precision. This paper describes how we model anatomical structure by identifying, tabulating, and analyzing contacts between 104 neurons in a morphologically precise model of a column. A contact occurs when one element touches another, providing the opportunity for the subsequent creation of a simulated synapse. The architecture of our application divides the problem of detecting and analyzing contacts among thousands of processors on the IBM Blue Gene/L™ supercomputer. Data required for contact tabulation is encoded with geometrical data for contact detection and is exchanged among processors. Each processor selects a subset of neurons and then iteratively 1) divides the number of points that represents each neuron among column subvolumes, 2) detects contacts in a subvolume, 3) tabulates arbitrary categories of local contacts, 4) aggregates and analyzes global contacts, and 5) revises the contents of a column to achieve a statistical objective. Computing, analyzing, and optimizing local data in parallel across distributed global data objects involve problems common to other domains (such as three-dimensional image processing and registration). Thus, we discuss the generic nature of the application architecture