Identifying, tabulating, and analyzing contacts between branched neuron morphologies

Kozloski, James; Sfyrakis, Konstantinos; Hill, Sean; Schürmann, Felix; Peck, Charles; Markram, Henry

doi:10.1147/rd.521.0043

Cited by 26 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sites of close appositions between neurons were identified using a collision detection algorithm running on a supercomputer. The algorithm identified all appositions between potential preand postsynaptic elements, including axons, dendrites, and somata, within a threshold distance (35) comprising the statistical connectivity of the model cortical microcircuit. The histograms of the positions of these potential synapse locations for different preand postsynaptic neuron types (called predicted innervation patterns) characterize the statistical structural connectivity.…”

Section: Resultsmentioning

confidence: 99%

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

Hill

Wang

Riachi

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

147

132

View full text Add to dashboard Cite

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.connectome | microcircuitry | neocortex | neuronal connectivity D uring development, a broad range of molecular signaling mechanisms act to form the neocortical layers (1, 2), lay down guidance tracts for neurons to position themselves and grow their axonal and dendritic arbors (3, 4), shape the morphologies of different types of neurons (5), guide input axons to specific layers (6), trigger differentiation and clustered arborizations of axonal arbors (7), prune axonal arbors in an activitydependent manner (8), guide axonal growth along the axon initial segment (9), and selectively form synapses between specific types of neurons (10-12). Molecular mechanisms also contribute to the activity-triggered formation of synapses between specific pairs of neurons (13). The mechanisms determining this selective connectivity have been a subject of active debate for over a century (3,(14)(15)(16)(17), with proposals ranging from the idea of "chemical relations" between connected neurons in the work by Langley (18) to the "chemoaffinity hypothesis" (the idea that precise synaptic positioning is specified by "highly specific cytochemical affinities" between neurons) in the work by Sperry (19). The result is an intriguing map of synaptic connections between all neurons and specific types of neurons, commonly called the connectome. How the connectome arises is still debated (20). A question that is at the core of the debate is to what extent do chemospecific mechanisms specifically align neuronal arbors between neurons before synaptic connections are formed.Spines are dynamic s...

show abstract

Section: Resultsmentioning

confidence: 99%

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

Hill

Wang

Riachi

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

147

132

View full text Add to dashboard Cite

show abstract

“…To determine the locations of synapses they perform a diskbased spatial join between two types of neurons (or their corresponding cylinders), axons and dendrites. Wherever an axon intersects with a dendrite, a synapse is placed [7]. The amounts of data involved in the join make it necessary for the join to be based on disk.…”

Section: B Motivating Applicationmentioning

confidence: 99%

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

Abstract-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. TRANSFORM-ERS 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.

show abstract

“…In this computation the neuroscientists determine where to place synapses, the structure thats permit an electrical impulse to leap over between neurons, in the model. Experiments in the wet lab have shown that it suffices to place synapses where branches of neurons intersect [7] to obtain a biorealisitc model of the brain. Touch detection therefore needs to find cylinders (compare with Figure 1, left) of different neurons that overlap/intersect with each other, translating this process into an in-memory spatial join.…”

Section: In-memory Spatial Join For Model Buildingmentioning

confidence: 99%

“…Synapses, i.e., the structures where impulses leap over between neurons, are placed wherever two cylinder of different neurons intersect [7]. Placing synapses therefore is equivalent to an in-memory spatial join where all neurons are tested for intersection.…”

Section: Introductionmentioning

confidence: 99%

Computational Neuroscience Breakthroughs through Innovative Data Management

Tauheed

Nobari

Biveinis

et al. 2013

Advances in Databases and Information Systems

View full text Add to dashboard Cite

Abstract. Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges today's spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.

show abstract

Identifying, tabulating, and analyzing contacts between branched neuron morphologies

Cited by 26 publications

References 14 publications

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

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

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Computational Neuroscience Breakthroughs through Innovative Data Management

Contact Info

Product

Resources

About