A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.

Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.

Inference of synaptic connectivity and external variability in neural microcircuits.

A major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is in practice often closely related to synaptic connectivity. This relation becomes more pronounced when the spatial structure of neuronal variability is jointly considered.

Magnetophoretic Conductors and Diodes in a 3D Magnetic Field.

We demonstrate magnetophoretic conductor tracks that can transport single magnetized beads and magnetically labeled single cells in a 3-dimensional time-varying magnetic field. The vertical field bias, in addition to the in-plane rotating field, has the advantage of reducing the attraction between particles, which inhibits the formation of particle clusters. However, the inclusion of a vertical field requires the re-design of magnetic track geometries which can transport magnetized objects across the substrate. Following insights from magnetic bubble technology, we found that successful magnetic conductor geometries defined in soft magnetic materials must be composed of alternating sections of positive and negative curvature. In addition to the previously studied magnetic tracks taken from the magnetic bubble literature, a drop-shape pattern was found to be even more adept at transporting small magnetic beads and single cells. Symmetric patterns are shown to achieve bi-directional conduction, whereas asymmetric patterns achieve unidirectional conduction. These designs represent the electrical circuit corollaries of the conductor and diode, respectively. Finally, we demonstrate biological applications in transporting single cells and in the size based separation of magnetic particles.

Open Data In Neurophysiology: Advancements, Solutions & Challenges.

Across the life sciences, an ongoing effort over the last 50 years has made data and methods more reproducible and transparent. This openness has led to transformative insights and vastly accelerated scientific progress1,2. For example, structural biology3 and genomics4,5 have undertaken systematic collection and publication of protein sequences and structures over the past half-century, and these data have led to scientific breakthroughs that were unthinkable when data collection first began (e.g.6). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. To this end, new social structures along with active and open scientific communities are essential7 to facilitate and expand the still limited adoption of open science practices in our field8. Unified by shared values of openness, we set out to organize a symposium for Open Data in Neuroscience (ODIN) to strengthen our community and facilitate transformative neuroscience research at large. In this report, we share what we learned during this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

Selective Electroporation of Tumor Cells Under AC Radiofrequency Stimulation - A Numerical Study.

Self-consistent evaluations of membrane electroporation along with local heating in single spherical cells arising from external AC radiofrequency electrical stimulation have been carried out. The present numerical study seeks to determine whether healthy and malignant cells exhibit separate electroporative responses with regards to operating frequency. It is shown that cells of Burkitt's lymphoma would respond to frequencies >4.5 MHz, while normal B-cells would have negligible porative effects in that higher frequency range. Similarly, a frequency separation between the response of healthy T-cells and malignant species is predicted with a threshold of about 4 MHz for cancer cells. The present simulation technique is general and so would be able to ascertain the beneficial frequency range for different cell types. The demonstration of higher frequencies to induce poration in malignant cells, while having minimal affecting healthy ones, suggests the possibility of selective electrical targeting for tumor treatments and protocols. It also opens the doorway for tabulating selectivity enhancement regimes as a guide for parameter selection towards more effective treatments while minimizing deleterious effects on healthy cells and tissues.

Correlated states in balanced neuronal networks.

Understanding the magnitude and structure of interneuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations, defining the "asynchronous state." Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent network produces much larger correlations. We provide an in-depth analysis of the resulting "correlated state" in balanced networks and show that, unlike the asynchronous state, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

Characterizing the Switching Thresholds of Magnetophoretic Transistors.

The switching thresholds of magnetophoretic transistors for sorting cells in microfluidic environments are characterized. The transistor operating conditions require short 20-30 mA pulses of electrical current. By demonstrating both attractive and repulsive transistor modes, a single transistor architecture is used to implement the full write cycle for importing and exporting single cells in specified array sites.