A computational view of short-term plasticity and its implications for E-I balance.

Short-term plasticity (STP) and excitatory-inhibitory balance (EI balance) are both ubiquitous building blocks of brain circuits across the animal kingdom. The synapses involved in EI are also subject to short-term plasticity, and several experimental studies have shown that their effects overlap. Recent computational and theoretical work has begun to highlight the functional implications of the intersection of these motifs. The findings are nuanced: while there are general computational themes, such as pattern tuning, normalization, and gating, much of the richness of these interactions comes from region- and modality specific tuning of STP properties. Together these findings point towards the STP-EI balance combination as being a versatile and highly efficient neural building block for a wide range of pattern-specific responses.

Using sensitivity analyses to understand bistable system behavior.

BACKGROUND: Bistable systems, i.e., systems that exhibit two stable steady states, are of particular interest in biology. They can implement binary cellular decision making, e.g., in pathways for cellular differentiation and cell cycle regulation. The onset of cancer, prion diseases, and neurodegenerative diseases are known to be associated with malfunctioning bistable systems. Exploring and characterizing parameter spaces in bistable systems, so that they retain or lose bistability, is part of a lot of therapeutic research such as cancer pharmacology. RESULTS: We use eigenvalue sensitivity analysis and stable state separation sensitivity analysis to understand bistable system behaviors, and to characterize the most sensitive parameters of a bistable system. While eigenvalue sensitivity analysis is an established technique in engineering disciplines, it has not been frequently used to study biological systems. We demonstrate the utility of these approaches on a published bistable system. We also illustrate scalability and generalizability of these methods to larger bistable systems. CONCLUSIONS: Eigenvalue sensitivity analysis and separation sensitivity analysis prove to be promising tools to define parameter design rules to make switching decisions between either stable steady state of a bistable system and a corresponding monostable state after bifurcation. These rules were applied to the smallest two-component bistable system and results were validated analytically. We showed that with multiple parameter settings of the same bistable system, we can design switching to a desirable state to retain or lose bistability when the most sensitive parameter is varied according to our parameter perturbation recommendations. We propose eigenvalue and stable state separation sensitivity analyses as a framework to evaluate large and complex bistable systems.

Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms.

Hippocampal CA1 cells take part in reliable, time-locked activity sequences in tasks that involve an association between temporally separated stimuli, in a manner that tiles the interval between the stimuli. Such cells have been termed time cells. Here, we adopt a first-principles approach to comparing diverse analysis and detection algorithms for identifying time cells. We generated synthetic activity datasets using calcium signals recorded in vivo from the mouse hippocampus using two-photon (2-P) imaging, as template response waveforms. We assigned known, ground truth values to perturbations applied to perfect activity signals, including noise, calcium event width, timing imprecision, hit trial ratio and background (untuned) activity. We tested a range of published and new algorithms and their variants on this dataset. We find that most algorithms correctly classify over 80% of cells, but have different balances between true and false positives, and different sensitivity to the five categories of perturbation. Reassuringly, most methods are reasonably robust to perturbations, including background activity, and show good concordance in classification of time cells. The same algorithms were also used to analyze and identify time cells in experimental physiology datasets recorded in vivo and most show good concordance.

HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.

Signaling networks mediate many aspects of cellular function. The conventional, mechanistically motivated approach to modeling such networks is through mass-action chemistry, which maps directly to biological entities and facilitates experimental tests and predictions. However such models are complex, need many parameters, and are computationally costly. Here we introduce the HillTau form for signaling models. HillTau retains the direct mapping to biological observables, but it uses far fewer parameters, and is 100 to over 1000 times faster than ODE-based methods. In the HillTau formalism, the steady-state concentration of signaling molecules is approximated by the Hill equation, and the dynamics by a time-course tau. We demonstrate its use in implementing several biochemical motifs, including association, inhibition, feedforward and feedback inhibition, bistability, oscillations, and a synaptic switch obeying the BCM rule. The major use-cases for HillTau are system abstraction, model reduction, scaffolds for data-driven optimization, and fast approximations to complex cellular signaling.

Computation, wiring, and plasticity in synaptic clusters.

Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano-chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.

An Early Cortical Progenitor-Specific Mechanism Regulates Thalamocortical Innervation.

The cortical subplate is critical in regulating the entry of thalamocortical sensory afferents into the cortex. These afferents reach the subplate at embryonic day (E)15.5 in the mouse, but "wait" for several days, entering the cortical plate postnatally. We report that when transcription factor LHX2 is lost in E11.5 cortical progenitors, which give rise to subplate neurons, thalamocortical afferents display premature, exuberant ingrowth into the E15.5 cortex. Embryonic mutant subplate neurons are correctly positioned below the cortical plate, but they display an altered transcriptome and immature electrophysiological properties during the waiting period. The sensory thalamus in these cortex-specific Lhx2 mutants displays atrophy and by postnatal day (P) 7, sensory innervation to the cortex is nearly eliminated leading to a loss of the somatosensory barrels. Strikingly, these phenotypes do not manifest if LHX2 is lost in postmitotic subplate neurons, and the transcriptomic dysregulation in the subplate resulting from postmitotic loss of LHX2 is vastly distinct from that seen when LHX2 is lost in progenitors. These results demonstrate a mechanism operating in subplate progenitors that has profound consequences on the growth of thalamocortical axons into the cortex.SIGNIFICANCE STATEMENT Thalamocortical nerves carry sensory information from the periphery to the cortex. When they first grow into the embryonic cortex, they "wait" at the subplate, a structure critical for the guidance and eventual connectivity of thalamic axons with their cortical targets. How the properties of subplate neurons are regulated is unclear. We report that transcription factor LHX2 is required in the progenitor "mother" cells of the cortical primordium when they are producing their "daughter" subplate neurons, in order for the thalamocortical pathway to wait at the subplate. Without LHX2 function in subplate progenitors, thalamocortical axons grow past the subplate, entering the cortical plate prematurely. This is followed by their eventual attrition and, consequently, a profound loss of sensory innervation of the mature cortex.

Physiology and Therapeutic Potential of SK, H, and M Medium AfterHyperPolarization Ion Channels.

SK, HCN, and M channels are medium afterhyperpolarization (mAHP)-mediating ion channels. The three channels co-express in various brain regions, and their collective action strongly influences cellular excitability. However, significant diversity exists in the expression of channel isoforms in distinct brain regions and various subcellular compartments, which contributes to an equally diverse set of specific neuronal functions. The current review emphasizes the collective behavior of the three classes of mAHP channels and discusses how these channels function together although they play specialized roles. We discuss the biophysical properties of these channels, signaling pathways that influence the activity of the three mAHP channels, various chemical modulators that alter channel activity and their therapeutic potential in treating various neurological anomalies. Additionally, we discuss the role of mAHP channels in the pathophysiology of various neurological diseases and how their modulation can alleviate some of the symptoms.

Patterned Optogenetic Stimulation Using a DMD Projector.

Optical manipulation is a powerful way to control neural activity in vitro and in vivo with millisecond precision. Patterning of light provides the remarkable ability to simultaneously target spatially segregated neurons from a population. Commercially available projectors provide one of the simplest and most economical ways of achieving spatial light modulation at millisecond timescales. Here, we describe the protocol for constructing a projector-based spatio-temporal light patterning system integrated with a microscope on a typical electrophysiology rig. The set-up is well suited for applications requiring rapid, distinct, and combinatorial inputs, akin to brain activity. This equipment involved is fairly economical (<$5000 including all optical and mechanical components), and the set-up is easy to assemble and program.

Impaired Reliability and Precision of Spiking in Adults But Not Juveniles in a Mouse Model of Fragile X Syndrome.

Fragile X syndrome (FXS) is the most common source of intellectual disability and autism. Extensive studies have been performed on the network and behavioral correlates of the syndrome, but our knowledge about intrinsic conductance changes is still limited. In this study, we show a differential effect of FMRP knockout in different subsections of hippocampus using whole-cell patch clamp in mouse hippocampal slices. We observed no significant change in spike numbers in the CA1 region of hippocampus, but a significant increase in CA3, in juvenile mice. However, in adult mice we see a reduction in spike number in the CA1 with no significant difference in CA3. In addition, we see increased variability in spike numbers in CA1 cells following a variety of steady and modulated current step protocols. This effect emerges in adult mice (8 weeks) but not juvenile mice (4 weeks). This increased spiking variability was correlated with reduced spike number and with elevated AHP. The increased AHP arose from elevated SK currents (small conductance calcium-activated potassium channels), but other currents involved in medium AHP, such as Ih and M, were not significantly different. We obtained a partial rescue of the cellular variability phenotype when we blocked SK current using the specific blocker apamin. Our observations provide a single-cell correlate of the network observations of response variability and loss of synchronization, and suggest that the elevation of SK currents in FXS may provide a partial mechanistic explanation for this difference.

Dendrites, deep learning, and sequences in the hippocampus.

The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.

FindSim: A Framework for Integrating Neuronal Data and Signaling Models.

Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources.

Discovery biology of neuropsychiatric syndromes (DBNS): a center for integrating clinical medicine and basic science.

BACKGROUND: There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer's dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository. METHODS: The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing. DISCUSSION: We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

Multirate method for co-simulation of electrical-chemical systems in multiscale modeling.

Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the establishment of a firm theoretical basis and to the development of an efficient computational framework for multiscale modeling and simulations.

Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations.

Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

Role of DARPP-32 and ARPP-21 in the Emergence of Temporal Constraints on Striatal Calcium and Dopamine Integration.

In reward learning, the integration of NMDA-dependent calcium and dopamine by striatal projection neurons leads to potentiation of corticostriatal synapses through CaMKII/PP1 signaling. In order to elicit the CaMKII/PP1-dependent response, the calcium and dopamine inputs should arrive in temporal proximity and must follow a specific (dopamine after calcium) order. However, little is known about the cellular mechanism which enforces these temporal constraints on the signal integration. In this computational study, we propose that these temporal requirements emerge as a result of the coordinated signaling via two striatal phosphoproteins, DARPP-32 and ARPP-21. Specifically, DARPP-32-mediated signaling could implement an input-interval dependent gating function, via transient PP1 inhibition, thus enforcing the requirement for temporal proximity. Furthermore, ARPP-21 signaling could impose the additional input-order requirement of calcium and dopamine, due to its Ca2+/calmodulin sequestering property when dopamine arrives first. This highlights the possible role of phosphoproteins in the temporal aspects of striatal signal transduction.

Minority odors get equal say.

The olfactory system becomes more sensitive when odor inputs are weak, and less sensitive when confronted with strong odors.

NSDF: Neuroscience Simulation Data Format.

Data interchange is emerging as an essential aspect of modern neuroscience. In the areas of computational neuroscience and systems biology there are multiple model definition formats, which have contributed strongly to the development of an ecosystem of simulation and analysis tools. Here we report the development of the Neuroscience Simulation Data Format (NSDF) which extends this ecosystem to the data generated in simulations. NSDF is designed to store simulator output across scales: from multiscale chemical and electrical signaling models, to detailed single-neuron and network models, to abstract neural nets. It is self-documenting, efficient, modular, and scalable, both in terms of novel data types and in terms of data volume. NSDF is simulator-independent, and can be used by a range of standalone analysis and visualization tools. It may also be used to store variety of experimental data. NSDF is based on the widely used HDF5 (Hierarchical Data Format 5) specification and is open, platform-independent, and portable.

Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding.

Stimulus encoding by primary sensory brain areas provides a data-rich context for understanding their circuit mechanisms. The vertebrate olfactory bulb is an input area having unusual two-layer dendro-dendritic connections whose roles in odor coding are unclear. To clarify these roles, we built a detailed compartmental model of the rat olfactory bulb that synthesizes a much wider range of experimental observations on bulbar physiology and response dynamics than has hitherto been modeled. We predict that superficial-layer inhibitory interneurons (periglomerular cells) linearize the input-output transformation of the principal neurons (mitral cells), unlike previous models of contrast enhancement. The linearization is required to replicate observed linear summation of mitral odor responses. Further, in our model, action-potentials back-propagate along lateral dendrites of mitral cells and activate deep-layer inhibitory interneurons (granule cells). Using this, we propose sparse, long-range inhibition between mitral cells, mediated by granule cells, to explain how the respiratory phases of odor responses of sister mitral cells can be sometimes decorrelated as observed, despite receiving similar receptor input. We also rule out some alternative mechanisms. In our mechanism, we predict that a few distant mitral cells receiving input from different receptors, inhibit sister mitral cells differentially, by activating disjoint subsets of granule cells. This differential inhibition is strong enough to decorrelate their firing rate phases, and not merely modulate their spike timing. Thus our well-constrained model suggests novel computational roles for the two most numerous classes of interneurons in the bulb.

Olfactory bulb coding of odors, mixtures and sniffs is a linear sum of odor time profiles.

The olfactory system receives intermittent and fluctuating inputs arising from dispersion of odor plumes and active sampling by the animal. Previous work has suggested that the olfactory transduction machinery and excitatory-inhibitory olfactory bulb circuitry generate nonlinear population trajectories of neuronal activity that differ across odorants. Here we show that individual mitral/tufted (M/T) cells sum inputs linearly across odors and time. By decoupling odor sampling from respiration in anesthetized rats, we show that M/T cell responses to arbitrary odor waveforms and mixtures are well described by odor-specific impulse responses convolved with the odorant's temporal profile. The same impulse responses convolved with the respiratory airflow predict the classical respiration-locked firing of olfactory bulb neurons and several other reported response properties of M/T cells. These results show that the olfactory bulb linearly processes fluctuating odor inputs, thereby simplifying downstream decoding of stimulus identity and temporal dynamics.

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.