92 results found
Tolkiehn M, Schultz SR, 2019, Neural ensemble activity depends on stimulus type in mouse primary visual cortex
<jats:title>ABSTRACT</jats:title><jats:p>Early cortical processing of visual information has long been investigated by describing the response properties such as receptive fields or orientation selectivity of individual neurons to moving gratings. However, thanks to recent technological advances, it has been become easier to record from larger neuronal populations which allow us to analyse the population responses to probe visual information processing at the population level. In the end, it is unlikely that sensory processing is a single-neuron effort but that of an entire population. Here we show how different stimulus types evoke distinct binary activity patterns (words) of simultaneous events on different sites in the anaesthetised mouse. Spontaneous activity and natural scenes indicated lower word distribution divergences than each to drifting gratings. Accounting for firing rate differences, spontaneous activity was linked to more unique patterns than stimulus-driven responses. Multidimensional scaling conveyed that pattern probability distributions clustered for spatial frequencies but not for directions. Further, drifting gratings modulated the Shannon entropy estimated on spatial patterns in a similar fashion as classical directional and spatial frequency tuning functions of neurons. This was supported by a distinct sublinear relationship between Shannon entropy and mean population firing rate.</jats:p>
Tolkiehn M, Schultz SR, 2019, Temporo-nasally biased moving grating selectivity in mouse primary visual cortex
<jats:title>Abstract</jats:title><jats:p>Orientation tuning in mouse primary visual cortex (V1) has long been reported to have a random or “salt-and-pepper” organisation, lacking the structure found in cats and primates. Laminar in-vivo multi-electrode array recordings here reveal previously elusive structure in the representation of visual patterns in the mouse visual cortex, with temporo-nasally drifting gratings eliciting consistently highest neuronal responses across cortical layers and columns, whilst upward moving gratings reliably evoked the lowest activities. We suggest this bias in direction selectivity to be behaviourally relevant as objects moving into the visual field from the side or behind may pose a predatory threat to the mouse whereas upward moving objects do not. We found furthermore that direction preference and selectivity was affected by stimulus spatial frequency, and that spatial and directional tuning curves showed high signal correlations decreasing with distance between recording sites. In addition, we show that despite this bias in direction selectivity, it is possible to decode stimulus identity and that spatiotemporal features achieve higher accuracy in the decoding task whereas spike count or population counts are sufficient to decode spatial frequencies implying different encoding strategies.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>We show that temporo-nasally drifting gratings (i.e. opposite the normal visual flow during forward movement) reliably elicit the highest neural activity in mouse primary visual cortex, whereas upward moving gratings reliably evoke the lowest responses. This encoding may be highly behaviourally relevant, as objects approaching from the periphery may pose a threat (e.g. predators), whereas upward moving objects do not. This is a result at odds with the belief that mouse primary visual cortex is randomly organised. Fur
Lubba CH, Sethi SS, Knaute P, et al., catch22: CAnonical Time-series CHaracteristics, Data Mining and Knowledge Discovery, ISSN: 1384-5810
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification fortime-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can beachieved through systematic comparison across a comprehensive time-seriesfeature library, such as those in the hctsa toolbox. However, this approach iscomputationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time seriesfor real-world applications. In this work, we introduce a method to infer smallsets of time-series features that (i) exhibit strong classification performanceacross a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series) and using a filtered version of the hctsafeature library (4791 features), we introduce a set of 22 CAnonical Timeseries CHaracteristics, catch22, tailored to the dynamics typically encounteredin time-series data-mining tasks. This dimensionality reduction, from 4791 to22, is associated with an approximately 1000-fold reduction in computationtime and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse andinterpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributionsand outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, thatfacilitates feature-based time-series analysis for scientific, industrial, financialand medical applications using a common language of interpretable time-seriesproperties.
Márton CD, Schultz SR, Averbeck BB, 2019, Learning to select actions shapes recurrent dynamics in the corticostriatal system
<jats:title>Abstract</jats:title><jats:p>Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, showing how dynamics in the corticostriatal system
Lubba CH, Fulcher BD, Schultz SR, et al., 2019, Efficient peripheral nerve firing characterisation through massive feature extraction, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 179-182, ISSN: 1948-3546
Peripheral nerve decoding algorithms form an important component of closed-loop bioelectronic medicines devices. For any decoding method, meaningful properties need to be extracted from the peripheral nerve signal as the first step. Simple measures such as signal amplitude and features of the Fourier power spectrum are most typically used, leaving open whether important information is encoded in more subtle properties of the dynamics. We here propose a feature-based analysis method that identifies changes in firing characteristics across recording sections by unsupervised dimensionality reduction in a high-dimensional feature-space and selects single efficiently implementable estimators for each characteristic to be used as the basis for a better decoding in future bioelectronic medicines devices.
Schultz S, Gava G, Neural codes – necessary but not sufficient for understanding brain function, Behavioral and Brain Sciences, ISSN: 0140-525X
Brains are information processing systems, whose operational principles ultimately cannot be understood without resource to information theory. We suggest that understanding how external signals are represented in the brain is a necessary step towards employing further engineering tools (such as control theory) to understand the information processing performed by brain circuits during behaviour.
Quicke P, Song C, McKimm EJ, et al., 2019, Corrigendum: Single-neuron level one-photon voltage imaging with sparsely targeted genetically encoded voltage indicators, Frontiers in Cellular Neuroscience, Vol: 13, ISSN: 1662-5102
Voltage imaging of many neurons simultaneously at single-cell resolution is hampered bythe difficulty of detecting small voltage signals from overlapping neuronal processes inneural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaginghave shown single-cell resolution optical voltage recordings in intact tissue throughimaging naturally sparse cell classes, sparse viral expression, soma restricted expression,advanced optical systems, or a combination of these. Widespread sparse and strongtransgenic GEVI expression would enable straightforward optical access to a denselyoccurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recentlydescribed sparse transgenic expression strategy can enable single-cell resolution voltageimaging of cortical pyramidal cells in intact brain tissue without restricting expression tothe soma. We also quantify the functional crosstalk in brain tissue and discuss optimalimaging rates to inform future GEVI experimental design.
Lubba CH, Le Guen Y, Jarvis S, et al., 2019, Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python., Neuroinformatics
The original version of this article unfortunately contained a mistake. The following text: "This project has received funding from European Research Council (ERC) Synergy Grant no. 319818." is missing in the Acknowledgments.
Marton C, Fukushima M, Camalier C, et al., 2019, Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex, eNeuro, Vol: 6, Pages: 1-14, ISSN: 2373-2822
Predictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom-up interactions in sensory processing. The theory suggests there are differences in message passing up versus down the cortical hierarchy. These differences result from the linear feedforward of prediction errors, and the nonlinear feedback of predictions. This implies that cross-frequency interactions should predominate top-down. But it remains unknown whether these differences are expressed in cross-frequency interactions in the brain. Here we examined bidirectional cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up interactions made prominent use of low frequencies: low-to-low-frequency (theta, alpha, beta) and low-frequency-to-high- gamma couplings were predominant top-down, while low-frequency-to-low-gamma couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting that they are a general feature of information processing in auditory cortex. Our findings suggest the modulatory effect of low frequencies on gamma-rhythms in distant regions is important for bidirectional information transfer. The finding of low-frequency-to-low-gamma interactions in the bottom-up direction suggest that nonlinearities may also play a role in feedforward message passing. Altogether, the patterns of cross-frequency interaction we observed across the auditory hierarchy are largely consistent with the predictive coding framework.
Quicke P, Song C, McKimm EJ, et al., 2019, Single-neuron level one-photon voltage imaging with sparsely targeted genetically encoded voltage indicators, Frontiers in Cellular Neuroscience, Vol: 13, ISSN: 1662-5102
Voltage imaging of many neurons simultaneously at single-cell resolution is hampered by the difficulty of detecting small voltage signals from overlapping neuronal processes in neural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaging have shown single-cell resolution optical voltage recordings in intact tissue through imaging naturally sparse cell classes, sparse viral expression, soma restricted expression, advanced optical systems, or a combination of these. Widespread sparse and strong transgenic GEVI expression would enable straightforward optical access to a densely occurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recently described sparse transgenic expression strategy can enable single-cell resolution voltage imaging of cortical pyramidal cells in intact brain tissue without restricting expression to the soma. We also quantify the functional crosstalk in brain tissue and discuss optimal imaging rates to inform future GEVI experimental design.
<jats:p>Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147\,000 time series, including biomedical datasets) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.</jats:p>
Lin Y, Mazo M, Skaalure S, et al., 2019, Activatable cell-biomaterial interfacing with photo-caged peptides, Chemical Science, Vol: 10, Pages: 1158-1167, ISSN: 2041-6520
Spatio-temporally tailoring cell–material interactions is essential for developing smart delivery systems and intelligent biointerfaces. Here we report new photo-activatable cell–material interfacing systems that trigger cellular uptake of various cargoes and cell adhesion towards surfaces. To achieve this, we designed a novel photo-caged peptide which undergoes a structural transition from an antifouling ligand to a cell-penetrating peptide upon photo-irradiation. When the peptide is conjugated to ligands of interest, we demonstrate the photo-activated cellular uptake of a wide range of cargoes, including small fluorophores, proteins, inorganic (e.g., quantum dots and gold nanostars) and organic nanomaterials (e.g., polymeric particles), and liposomes. Using this system, we can remotely regulate drug administration into cancer cells by functionalizing camptothecin-loaded polymeric nanoparticles with our synthetic peptide ligands. Furthermore, we show light-controlled cell adhesion on a peptide-modified surface and 3D spatiotemporal control over cellular uptake of nanoparticles using two-photon excitation. We anticipate that the innovative approach proposed in this work will help to establish new stimuli-responsive delivery systems and biomaterials.
Soor N, Quicke P, Howe C, et al., 2019, All-optical crosstalk-free manipulation and readout of Chronos-expressing Neurons, Journal of Physics D: Applied Physics, Vol: 52, ISSN: 0022-3727
All optical neurophysiology allows manipulation and readout of neural network activity with single-cell spatial resolution and millisecond temporal resolution. Neurons can be made to express proteins that actuate transmembrane currents upon light absorption, enabling optical control of membrane potential and action potential signalling. In addition, neurons can be genetically or synthetically labelled with fluorescent reporters of changes in intracellular calcium concentration or membrane potential. Thus, to optically manipulate and readout neural activity in parallel, two spectra are involved: the action spectrum of the actuator, and the absorption spectrum of the fluorescent reporter. Due to overlap in these spectra, previous all-optical neurophysiology paradigms have been hindered by spurious activation of neuronal activity caused by the readout light. Here, we pair the blue-green absorbing optogenetic actuator, Chronos, with a deep red-emitting fluorescent calcium reporter CaSiR-1. We show that cultured Chinese hamster ovary cells transfected with Chronos do not exhibit transmembrane currents when illuminated with wavelengths and intensities suitable for exciting one-photon CaSiR-1 fluorescence. We then demonstrate crosstalk-free, high signal-to-noise ratio CaSiR-1 red fluorescence imaging at 100 frames s−1 of Chronos-mediated calcium transients evoked in neurons with blue light pulses at rates up to 20 Hz. These results indicate that the spectral separation between red light excited fluorophores, excited efficiently at or above 640 nm, with blue-green absorbing opsins such as Chronos, is sufficient to avoid spurious opsin actuation by the imaging wavelengths and therefore enable crosstalk-free all-optical neuronal manipulation and readout.
Lubba CH, Fulcher BD, Schultz SR, et al., 2019, Efficient peripheral nerve firing characterisation through massive feature extraction, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 179-182, ISSN: 1948-3546
Lubba CT, Le Guen Y, Jarvis S, et al., 2019, PyPNS: multiscale simulation of a peripheral nerve in Python, Neuroinformatics, Vol: 17, Pages: 63-81, ISSN: 1539-2791
Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help.We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modeled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modeled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin- Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
Garasto S, Nicola W, Bharath A, et al., Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex, 2019 9th International IEEE/EMBS Conference on Neural Engineering(NER), Publisher: IEEE
Interpreting the neural code involves decoding the firing pattern of sensory neurons from the perspective of a downstream population. Performing such a read-out is an essential step for the understanding of sensory information processing in the brain and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to categorize stimuli using a predefined set of labels, less attention has been given to full-stimulus reconstruction, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of 103 neurons in layer 2/3 of mouse primary visual cortex. Using an optimal linear estimator, we investigated which factors drive the reconstruction performance at the pixel level. We find the density of receptive fields to be the most influential feature. Finally, we use the receptive field data and simulations from a linear-nonlinear Poisson model to extrapolate decoding accuracy as a function of network size. Based on our analysis on a public dataset, reconstruction performance using two-photon protocols might be considerably improved if the receptive fields are sampled more uniformly in the full visual field. These results provide practical experimental guidelines to boost the accuracy of full-stimulus reconstruction.
Reynolds SC, abrahamsson T, sjostrom PJ, et al., 2018, CosMIC: a consistent metric for spike inference from calcium imaging, Neural Computation, Vol: 30, Pages: 2726-2756, ISSN: 0899-7667
In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.
Muzzu T, Mitolo S, Gava GP, et al., 2018, Encoding of locomotion kinematics in the mouse cerebellum, PLoS ONE, Vol: 13, ISSN: 1932-6203
The cerebellum is involved in coordinating motor behaviour, but how the cerebellar network regulates locomotion is still not well understood. We characterised the activity of putative cerebellar Purkinje cells, Golgi cells and mossy fibres in awake mice engaged in an active locomotion task, using high-density silicon electrode arrays. Analysis of the activity of over 300 neurons in response to locomotion revealed that the majority of cells (53%) were significantly modulated by phase of the stepping cycle. However, in contrast to studies involving passive locomotion on a treadmill, we found that a high proportion of cells (45%) were tuned to the speed of locomotion, and 19% were tuned to yaw movements. The activity of neurons in the cerebellar vermis provided more information about future speed of locomotion than about past or present speed, suggesting a motor, rather than purely sensory, role. We were able to accurately decode the speed of locomotion with a simple linear algorithm, with only a relatively small number of well-chosen cells needed, irrespective of cell class. Our observations suggest that behavioural state modulates cerebellar sensorimotor integration, and advocate a role for the cerebellar vermis in control of high-level locomotor kinematic parameters such as speed and yaw.
Quicke P, Reynolds S, Neil M, et al., 2018, High speed functional imaging with source localized multifocal two-photon microscopy, Biomedical Optics Express, Vol: 9, Pages: 3678-3693, ISSN: 2156-7085
Multifocal two-photon microscopy (MTPM) increases imaging speed over single-focus scanning by parallelizing fluorescence excitation. The imaged fluorescence’s susceptibility to crosstalk, however, severely degrades contrast in scattering tissue. Here we present a source-localized MTPM scheme optimized for high speed functional fluorescence imaging in scattering mammalian brain tissue. A rastered line array of beamlets excites fluorescence imaged with a complementary metal-oxide-semiconductor (CMOS) camera. We mitigate scattering-induced crosstalk by temporally oversampling the rastered image, generating grouped images with structured illumination, and applying Richardson-Lucy deconvolution to reassign scattered photons. Single images are then retrieved with a maximum intensity projection through the deconvolved image groups. This method increased image contrast at depths up to 112 μm in scattering brain tissue and reduced functional crosstalk between pixels during neuronal calcium imaging. Source-localization did not affect signal-to-noise ratio (SNR) in densely labeled tissue under our experimental conditions. SNR decreased at low frame rates in sparsely labeled tissue, with no effect at frame rates above 50 Hz. Our non-descanned source-localized MTPM system enables high SNR, 100 Hz capture of fluorescence transients in scattering brain, increasing the scope of MTPM to faster and smaller functional signals.
Annecchino L, Schultz SR, 2018, Progress in automating patch clamp cellular physiology, Brain and Neuroscience Advances, Vol: 2, Pages: 1-16, ISSN: 2398-2128
Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since theirinception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address bothvoltage and ligand gated channels, and showing the potential to play a pivotal role in drug discovery and biomedicalresearch. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologicallyrelevant cellular environments, or capture higher-order aspects of synaptic physiology and network dynamics. In vivopatch clamp electrophysiology has the potential to yield more biologically complex information and be especially usefulin reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, whilecapturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it isa difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, Invivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we documentthe development of automated patch clamp technology, from early systems based on multi-well plates through toautomated planar array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cellelectrophysiological recordings in vivo.
Jarvis S, Nikolic K, Schultz SR, 2018, Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron’s gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functio
Schuck R, Go MA, Garasto S, et al., 2018, Multiphoton minimal inertia scanning for fast acquisition of neural activity signals, Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552
Objective: Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on e
Reynolds SC, Abrahamsson T, Schuck R, et al., 2017, ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data, Eneuro, Vol: 4, ISSN: 2373-2822
We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally ‘similar’ time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.
Schuck R, Go MA, Garasto S, et al., 2017, Multiphoton minimal inertia scanning for fast acquisition of neural activity signals, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only regions of interest in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for <jats:italic>in vitro</jats:italic> hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to ”park” the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Cramér-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estima
Lubba CH, Guen YL, Jarvis S, et al., 2017, Multiscale simulation of peripheral neural signaling, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms to evaluate spontaneous activity patterns, stimulation efficiency, and organ responses. To reduce experimentation load and allow for a faster, more detailed analysis of both recording from and stimulation of peripheral nerves, adaptable computational models incorporating insights won in experiments will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealized extracellular space models in one environment. Two different scales of abstraction were merged. On the one hand we modeled the extracellular space in a finite element solver as a three dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed for different media (homogeneous, nerve in saline, nerve in cuff). Axons, on the other hand, were modeled at a higher level of abstraction as one dimensional chains of compartments; each consisting of lumped linear elements and, for some, channels with non-linear dynamics. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibers, we instead adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibers along the nerve with variable tortuosity, with tortuosity values fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity leads to differentiation in recorded signal shapes, with unmyelinated axons being the most affected. Tortuosity was further shown to increase the stimulation threshold. The
Quicke P, Neil M, Knopfel T, et al., 2017, Source-Localized Multifocal Two-Photon Microscopy for High-Speed Functional Imaging, 71st Annual Meeting of the Society-of-General-Physiologists (SGP) on Optical Revolution in Physiology - From Membrane to Brain, Publisher: ROCKEFELLER UNIV PRESS, Pages: 13A-14A, ISSN: 0022-1295
Annecchino LA, Morris AR, Copeland CS, et al., 2017, Robotic automation of in vivo two photon targeted whole-cell patch clamp electrophysiology, Neuron, Vol: 95, Pages: 1048-1055.e3, ISSN: 0896-6273
Whole-cell patch-clamp electrophysiological recording is a powerful technique for studying cellular function. While in vivo patch-clamp recording has recently benefited from automation, it is normally performed “blind,” meaning that throughput for sampling some genetically or morphologically defined cell types is unacceptably low. One solution to this problem is to use two-photon microscopy to target fluorescently labeled neurons. Combining this with robotic automation is difficult, however, as micropipette penetration induces tissue deformation, moving target cells from their initial location. Here we describe a platform for automated two-photon targeted patch-clamp recording, which solves this problem by making use of a closed loop visual servo algorithm. Our system keeps the target cell in focus while iteratively adjusting the pipette approach trajectory to compensate for tissue motion. We demonstrate platform validation with patch-clamp recordings from a variety of cells in the mouse neocortex and cerebellum.
Cazé RD, Jarvis S, Foust AJ, et al., 2017, Dendrites enable a robust mechanism for neuronal stimulus selectivity, Neural Computation, Vol: 29, Pages: 2511-2527, ISSN: 0899-7667
Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.
Lubba C, Mitrani E, Hokanson J, et al., 2017, Real-time decoding of bladder pressure from pelvic nerve activity, 8th International IEEE/EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 617-620, ISSN: 1948-3546
There has been substantial recent interest in the development of bioelectronic medicines (also known as “electroceuticals”) . Bioelectronic medicines consist of implantable devices capable of treating diseases by modulation of the nervous system. While substantial progress has been made in the treatment of some central nervous system disorders such as Parkinson's Disease by deep brain stimulation , recently attention is focused on development of neuromodulation strategies for the peripheral nervous system . The development of strategies for interfacing with and modulating the activity of peripheral nerves to the viscera may offer the prospect of extending bioelectronic medicine beyond diseases of the central nervous system, to the much larger class of non-neurological diseases that can be affected by electrical signalling in the peripheral nervous system, ranging from hypertension  to sleep apnea , rheumatoid arthritis  and sepsis .
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.