76 results found
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
Lubba CH, Le Guen Y, Jarvis S, et al., 2018, PyPNS: Multiscale Simulation of a Peripheral Nerve in Python., Neuroinformatics
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 modelled 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 modelled 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.
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
Reynolds S, Abrahamsson T, Sjöström PJ, et al., 2018, CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging., Neural Comput, Vol: 30, Pages: 2726-2756
In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention, yet 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 maximized 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.
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
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
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
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
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
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
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
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
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
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.
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
Cheung K, Schultz SR, Luk W, 2016, NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors, FRONTIERS IN NEUROSCIENCE, Vol: 9, ISSN: 1662-453X
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.
Evans BD, Jarvis S, Schultz SR, et al., 2016, PyRhO: A Multiscale Optogenetics Simulation Platform, FRONTIERS IN NEUROINFORMATICS, Vol: 10, ISSN: 1662-5196
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.
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.
Reynolds S, Copeland CS, Schultz SR, et al., 2016, AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION, IEEE 13th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 676-679, ISSN: 1945-7928
Tang J, Jimenez SCA, Chakraborty S, et al., 2016, Visual Receptive Field Properties of Neurons in the Mouse Lateral Geniculate Nucleus, PLOS ONE, Vol: 11, ISSN: 1932-6203
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
Caze RD, Foust AJ, Clopath C, et al., 2015, On the distribution and function of synaptic clusters
Local non-linearities in dendrites render neuronal output dependent on the spatial distribution of synapses. A neuron will activate differently depending on whether active synapses are spatially clustered or dispersed. While this sensitivity can in principle expand neuronal computational capacity, it has thus far been employed in very few learning paradigms. To make use of this sensitivity, groups of correlated neurons need to make contact with distinct dendrites, and this requires a mechanism to ensure the correct distribution of synapses contacting from distinct ensembles. To address this problem, we introduce the requirement that on a short time scale, a pre-synaptic neuron makes a constant number of synapses with the same strength on a post-synaptic neuron. We find that this property enables clusters to distribute correctly and guarantees their functionality. Furthermore, we demonstrate that a change in the input statistics can reshape the spatial distribution of synapses. Finally, we show under which conditions clusters do not distribute correctly, e.g. when cross-talk between dendrites is too strong. As well as providing insight into potential biological mechanisms of learning, this work paves the way for new learning algorithms for artificial neural networks that exploit the spatial distribution of synapses.
Hallett E, Woodward R, Schultz S, et al., 2015, Rapid Bicycle Gear Switching Based on Physiological Cues, IEEE International Conference on Automation Science and Engineering (CASE), Publisher: IEEE, Pages: 377-382, ISSN: 2161-8070
Jarvis S, Schultz SR, 2015, Prospects for Optogenetic Augmentation of Brain Function, FRONTIERS IN SYSTEMS NEUROSCIENCE, Vol: 9, ISSN: 1662-5137
Reynolds S, Copeland C, Schultz S, et al., 2015, AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION
Two-photon calcium imaging of the brain allows the spatiotemporal activity of neuronal networks to be monitored at cellular resolution. In order to analyse this activity it must first be possible to detect, with high temporal resolution, spikes from the time series corresponding to single neurons. Previous work has shown that finite rate of innovation (FRI) theory can be used to reconstruct spike trains from noisy calcium imaging data. In this paper we extend the FRI framework for spike detection from calcium imaging data to encompass data generated by a larger class of calcium indicators, including the genetically encoded indicator GCaMP6s. Furthermore, we implement least squares model-order estimation and perform a noise reduction procedure ('pre-whitening') in order to increase the robustness of the algorithm. We demonstrate high spike detection performance on real data generated by GCaMP6s, detecting 90% of electrophysiologically-validated spikes.
Reynolds S, Onativia J, Copeland CS, et al., 2015, Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons, International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE, Pages: 533-537
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