84 results found
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., Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex, eNeuro, ISSN: 2373-2822
Soor NS, Quicke P, Howe CL, 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
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
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.
Lin Y, Mazo MM, Skaalure SC, et al., 2019, Activatable cell-biomaterial interfacing with photo-caged peptides, CHEMICAL SCIENCE, Vol: 10, Pages: 1158-1167, ISSN: 2041-6520
Lubba CH, 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
Lubba C, Fulcher B, Schultz S, et al., 2018, Efficient peripheral nerve firing characterisation through massive feature extraction
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.
Garasto S, Nicola W, Bharath A, et al., 2018, Neural Sampling Strategies for Visual Stimulus Reconstruction from Two-photon Imaging of Mouse Primary Visual Cortex
Deciphering the neural code involves interpreting the responses of sensory neurons from the perspective of a downstream population. Performing such a read-out is an important step towards understanding how the brain processes sensory information and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to identify a stimulus in a predefined set of categories, few studies have approached a full-stimulus reconstruction task, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of mouse primary visual cortex. We decoded the activity of 103 neurons from layer 2/3 using an optimal linear estimator and 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. We find that, on this dataset, reconstruction performance can increase by more than 50%, provided that 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 S, 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
Muzzu T, Mitolo S, Gava GP, et al., 2018, Encoding of locomotion kinematics in the mouse cerebellum, PLOS ONE, Vol: 13, ISSN: 1932-6203
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, ISSN: 2156-7085
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: 1741-2560
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
Annecchino LA, Schultz SR, 2018, Progress in automating patch clamp cellular physiology, Brain and Neuroscience Advances, Vol: 2, Pages: 239821281877656-239821281877656, ISSN: 2398-2128
Schuck R, Go MA, Garasto S, et al., 2017, Multiphoton minimal inertia scanning for fast acquisition of neural activity signals
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 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 Cramer-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estimate of the spike timing occurrence. The results show that TSS and SSA achieve comparable accuracy in spike t
Lubba C, Le Guen Y, Jarvis S, et al., 2017, Multiscale simulation of peripheral neural signaling
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 model we developed can easily be adapted to different nerve
Reynolds S, 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
Caze 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
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-+, ISSN: 0896-6273
Schultz SR, Copeland CS, Foust AJ, et al., 2017, Advances in Two-Photon Scanning and Scanless Microscopy Technologies for Functional Neural Circuit Imaging, PROCEEDINGS OF THE IEEE, Vol: 105, Pages: 139-157, ISSN: 0018-9219
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
Jarvis S, Nikolic K, Schultz S, 2016, Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study
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 functions, such as gain
Muzzu T, Mitolo S, Gava G, et al., 2016, Encoding of locomotion kinematics in the mouse cerebellum
The cerebellum has a well-established role in locomotion control, but how the cerebellar network regulates locomotion behaviour is still not well understood. We therefore characterized the activity of cerebellar neurons in awake mice engaged in a locomotion task, using high-density silicon electrode arrays. We characterized the activity of over 300 neurons in response to locomotion, finding tuning to speed of locomotion, turning, and phase of the step cycle. We found that the cerebellar neurons we recorded mainly encoded information about future locomotor activity. We were able to decode the speed of locomotion with a simple linear algorithm, needing relatively few well-chosen cells to provide an accurate estimate of locomotion speed. Our observation that cerebellar neuronal activity predicts locomotion in the near future, and encodes the required kinematic variables, points to this activity underlying the efference copy signal for vertebrate locomotion.
Yousif N, Fu RZ, Bourquin BA-E-E, et al., 2016, Dopamine Activation Preserves Visual Motion Perception Despite Noise Interference of Human V5/MT, JOURNAL OF NEUROSCIENCE, Vol: 36, Pages: 9303-9312, ISSN: 0270-6474
Delgado Ruz I, Schultz S, 2016, Fractured tonotopy of functional neural clusters in mouse auditory cortex
The degree of order versus randomness in mammalian cortical circuits has been the subject of much discussion. Previous reports showed that at a large scale there is smooth tonotopy in mouse auditory cortex, while at the single neuron level the representation is the traditional "salt and pepper" configuration attributed to rodent cortex. Here we show that at the micro columnar scale we find a large variety of response profiles, but neurons tend to share similar preference in terms of frequency, bandwidth and latency. However, this smooth representation was fractured and large differences were possible between neighbouring neurons. Despite the tendency of most groups of neurons to operate redundantly, high information gains were achieved between cells that had a high signal correlation. Connectivity between neurons was highly non-random, in agreement with a previous in-vitro report from layer five. Our results suggest the existence of functional clusters, connecting neighbouring mini-columns. This supports the idea of a "salt and pepper" configuration at the level of functional clusters of neurons rather than single units.
Phoka E, Berditchevskaia A, Barahona M, et al., 2016, Long-term, layer-specific reverberant activity in the mouse somatosensory cortex following sensory stimulation
Neocortical circuits exhibit spontaneous neuronal activity whose functional relevance remains enigmatic. Several proposed functions assume that sensory experience can influence subsequent spontaneous activity. However, long-term alterations in spontaneous firing rates following sensory stimulation have not been reported until now. Here we show that multi-whisker, spatiotemporally rich stimulation of mouse vibrissae induces a laminar-specific, long-term increase of spontaneous activity in the somatosensory cortex. Such stimulation additionally produces stereotypical neural ensemble firing patterns from simultaneously recorded single neurons, which are maintained during spontaneous activity following stimulus offset. The increased neural activity and concomitant ensemble firing patterns are sustained for at least 25 minutes after stimulation, and specific to layers IV and Vb. In contrast, the same stimulation protocol applied to a single whisker fails to elicit this effect. Since layer Vb has the largest receptive fields and, together with layer IV, receives direct thalamic and lateral drive, the increase in firing activity could be the result of mechanisms involving the integration of spatiotemporal patterns across multiple whiskers. Our results provide direct evidence of modification of spontaneous cortical activity by sensory stimulation and could offer insight into the role of spatiotemporal integration in memory storage mechanisms for complex stimuli.
Berditchevskaia A, Caze RD, Schultz SR, 2016, Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour, SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
Berditchevskaia A, Caze R, Schultz S, 2016, Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour
In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.
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