86 results found
Phoka E, Berditchevskaia A, Barahona M, et al., 2016, Long-term, layer-specific reverberant activity in the mouse somatosensory cortex following sensory stimulation, Publisher: Cold Spring Harbor Laboratory
<jats:p>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.</jats:p>
Berditchevskaia A, Caze R, 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
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 R, Schultz SR, 2016, Performance in a GO/NOGO perceptual task reflects a balance between impulsive and instrumental components of behaviour, Publisher: Cold Spring Harbor Laboratory
<jats:p>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.</jats:p>
Evans B, Jarvis S, Schultz S, et al., 2016, PyRhO: A Multiscale Optogenetics Simulation Platform, Frontiers in Neuroinformatics, Vol: 10, ISSN: 1662-5196
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.
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: 1662-4548
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
Tang J, Ardila Jimenez S, Chakraborty S, et al., 2016, Visual receptive field properties of neurons in the mouse lateral geniculate nucleus, PLOS One, Vol: 11, ISSN: 1932-6203
The lateral geniculate nucleus (LGN) is increasingly regarded as a “smart-gating” operator for processing visual information. Therefore, characterizing the response properties of LGN neurons will enable us to better understand how neurons encode and transfer visual signals. Efforts have been devoted to study its anatomical and functional features, and recent advances have highlighted the existence in rodents of complex features such as direction/orientation selectivity. However, unlike well-researched higher-order mammals such as primates, the full array of response characteristics vis-à-vis its morphological features have remained relatively unexplored in the mouse LGN. To address the issue, we recorded from mouse LGN neurons using multisite-electrode-arrays (MEAs) and analysed their discharge patterns in relation to their location under a series of visual stimulation paradigms. Several response properties paralleled results from earlier studies in the field and these include centre-surround organization, size of receptive field, spontaneous firing rate and linearity of spatial summation. However, our results also revealed “high-pass” and “low-pass” features in the temporal frequency tuning of some cells, and greater average contrast gain than reported by earlier studies. In addition, a small proportion of cells had direction/orientation selectivity. Both “high-pass” and “low-pass” cells, as well as direction and orientation selective cells, were found only in small numbers, supporting the notion that these properties emerge in the cortex. ON- and OFF-cells showed distinct contrast sensitivity and temporal frequency tuning properties, suggesting parallel projections from the retina. Incorporating a novel histological technique, we created a 3-D LGN volume model explicitly capturing the morphological features of mouse LGN and localising individual cells into anterior/middle/posterior LGN. Based on th
Jarvis S, Schultz SR, Prospects for optogenetic augmentation of brain function, Frontiers in Systems Neuroscience, Vol: 9, ISSN: 1663-3563
The ability to optically control neural activity opens up possibilities for the restoration of normal function following neurological disorders. The temporal precision, spatial resolution and neuronal specificity that optogenetics offers is unequalled by other available methods, so will it be suitable for not only restoring but also extending brain function? As the first demonstrations of optically ``implanted'' novel memories emerge, we examine the suitability of optogenetics as a technique for extending neural function. While optogenetics is an effective tool for altering neural activity, the largest impediment for optogenetics in neural augmentation is our systems level understanding of brain function. Furthermore, a number of clinical limitations currently remain as substantial hurdles for the applications proposed. While neurotechnologies for treating brain disorders and interfacing with prosthetics have advanced rapidly in the past few years, partially addressing some of these critical problems, optogenetics is not yet suitable for use in humans. Instead we conclude that for the immediate future, optogenetics is the neurological equivalent of the 3D printer: its flexibility providing an ideal tool for testing and prototyping solutions for treating brain disorders and augmenting brain function.
Reynolds S, Copeland CS, Schultz SR, et al., 2015, AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION
<jats:p>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.</jats:p>
Caze RD, Foust AJ, Clopath C, et al., 2015, On the distribution and function of synaptic clusters
<jats:p>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.</jats:p>
Tolkiehn M, Schultz S, 2015, Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex
<jats:p>This study investigates the spatial and directional tuning of Multi-Unit Activity (MUA) in mouse primary visual cortex and how MUA can reflect spatiotemporal structures contained in moving gratings. Analysis of multi-shank laminar electrophysiological recordings from mouse primary visual cortex indicates a directional preference for moving gratings around 180◦, while preferred spatial frequency peaks around 0.02 cycles per degree, which is similar as reported in single-unit studies. Using only features from MUA, we further achieved a significant performance in decoding spatial frequency or direc- tion of moving gratings, with average decoding performances of up to 58.54% for 8 directions, and 44% correctly identified spatial frequencies against chance level of 16.7%.</jats:p>
Schuck R, Quicke P, Copeland C, et al., 2015, Rapid three dimensional two photon neural population scanning
<jats:p>Recording the activity of neural populations at high sampling rates is a fundamental requirement for understanding computation in neural circuits. Two photon microscopy provides one promising approach towards this. However, neural circuits are three dimensional, and functional imaging in two dimensions fails to capture the 3D nature of neural dynamics. Electrically tunable lenses (ETLs) provide a simple and cheap method to extend laser scanning microscopy into the relatively unexploited third dimension. We have therefore incorporated them into our Adaptive Spiral Scanning (SSA) algorithm, which calculates kinematically efficient scanning strategies using radially modulated spiral paths. We characterised the response of the ETL, incorporated its dynamics using MATLAB models of the SSA algorithm and tested the models on populations of Izhikevich neurons of varying size and density. From this, we show that our algorithms can theoretically at least achieve sampling rates of 36.2 Hz compared to 21.6 Hz previously reported for 3D scanning techniques.</jats:p>
Schuck R, Quicke P, Hwang JK, et al., 2015, Rapid three dimensional two photon neural population scanning, 37th Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 5867-5870, ISSN: 1557-170X
Recording the activity of neural populationsat high sampling rates is a fundamental requirement forunderstanding computation in neural circuits. Two photonmicroscopy provides one promising approach towards this.However, neural circuits are three dimensional, and functionalimaging in two dimensions fails to capture the 3D natureof neural dynamics. Electrically tunable lenses (ETLs) providea simple and cheap method to extend laser scanningmicroscopy into the relatively unexploited third dimension.We have therefore incorporated them into our Adaptive SpiralScanning (SSA) algorithm, which calculates kinematicallyefficient scanning strategies using radially modulated spiralpaths. We characterised the response of the ETL, incorporatedits dynamics using MATLAB models of the SSA algorithmand tested the models on populations of Izhikevich neuronsof varying size and density. From this, we show that ouralgorithms can theoretically at least achieve sampling rates of36.2Hz compared to 21.6Hz previously reported for 3D scanningtechniques.
Tolkiehn M, Schultz, 2015, Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 3771-3774, ISSN: 1557-170X
This study investigates the spatial and directionaltuning of Multi-Unit Activity (MUA) in mouse primary visualcortex and how MUA can reflect spatiotemporal structurescontained in moving gratings. Analysis of multi-shank laminarelectrophysiological recordings from mouse primary visualcortex indicates a directional preference for moving gratingsaround 180 , while preferred spatial frequency peaks around0.02 cycles per degree, which is similar as reported in single-unitstudies. Using only features from MUA, we further achieved asignificant performance in decoding spatial frequency or directionof moving gratings, with average decoding performancesof up to 58.54% for 8 directions, and 44% correctly identifiedspatial frequencies against chance level of 16.7%.
Hallett E, Woodward R, Schultz SR, et al., 2015, Rapid bicycle gear switching based on physiological cues, IEEE CASE 2015, Publisher: IEEE, Pages: 377-382
This paper discusses the merits of Mechanomyography (MMG) sensors in capturing and isolating muscle activity in high interference environs, with application to `hands free' gear shifting on a bicycle for users with limited extremity movement. MMG (acoustic) muscle sensing provides a simple and rugged alternative to physiological sensing for machine interface in the field, but suffers from interfering artifacts (in particular motion) which has limited its mainstream use. We introduce a system fusing MMG with a filter based on Inertial Measurement (IMU) to isolate muscle activity in the presence of interfering motion and vibrations. The system identifies user-initiated muscle trigger profiles during laboratory testing, allowing parameterization of MMG and IMU signals to identify purposeful muscle contractions (triggers) and to omit false triggers resulting from cycle/road vibration or rider movement. During laboratory testing the success rate of trigger identification was 88.5% while cycling with an average of 0.87 false triggers /min. During road testing the success rate was 72.5% and false triggers were more frequent at 3.7 /min. These results hold strong promise for alternative triggering mechanisms to the standard bar-end shifters used in current off-the-shelf cycling group sets, enabling amputees or people of reduced arm or hand dexterity to change gears while riding. Further testing will explore the use of signal filters on MMG data and further use of IMU data as feedback to increase false triggers rejection. Wider applications include a broad range of machine-interaction research.
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
Ruz ID, Schultz SR, 2014, Localising and classifying neurons from high density MEA recordings, JOURNAL OF NEUROSCIENCE METHODS, Vol: 233, Pages: 115-128, ISSN: 0165-0270
Longden KD, Muzzu T, Cook DJ, et al., 2014, Nutritional State Modulates the Neural Processing of Visual Motion, CURRENT BIOLOGY, Vol: 24, Pages: 890-895, ISSN: 0960-9822
Schuck R, Annecchino LA, Schultz SR, 2014, Scaling Up Multiphoton Neural Scanning: the SSA algorithm, Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, Pages: 2837-2840, ISSN: 1557-170X
Jarvis SJ, Nikolic K, Schultz SR, 2014, Optical coactivation in cortical cells: reprogramming the excitation-inhibition balancing act to control neuronal gain in abstract and detailed models, BMC Neuroscience, Vol: 15, Pages: F1-F1, ISSN: 1471-2202
Oñativia J, Schultz S, Dragotti PL, 2013, A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging, Journal of Neural Engineering, Vol: 10
Montani F, Phoka E, Portesi M, et al., 2013, Statistical modelling of higher-order correlations in pools of neural activity, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, Vol: 392, Pages: 3066-3086, ISSN: 0378-4371
Nikolic K, Jarvis S, Schultz S, et al., 2013, Controlling the neuronal balancing act: optical coactivation of excitation and inhibition in neuronal subdomains, Publisher: BioMed Central, ISSN: 1471-2202
Grossman N, Simiaki V, Martinet C, et al., 2013, The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials, JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Vol: 34, Pages: 477-488, ISSN: 0929-5313
Caze RD, Humphries MD, Gutkin B, et al., 2013, A difficult classification for neurons without dendrites, 6th International IEEE EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 215-218, ISSN: 1948-3546
Nikolic K, Jarvis S, Grossman N, et al., 2013, Computational Models of Optogenetic Tools for Controlling Neural Circuits with Light, 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5934-5937, ISSN: 1557-170X
Oshiorenoya AE, Marchand P, Mutlu M, et al., 2013, Calcium Imaging In Temporal Focus, 6th International IEEE EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 1525-1528, ISSN: 1948-3546
Seemungal B, Guzman-Lopez J, Arshad Q, et al., 2012, VESTIBULAR ACTIVATION DIFFERENTIALLY MODULATES HUMAN EARLY VISUAL CORTEX AND V5/MT EXCITABILITY AND RESPONSE ENTROPY, Annual Meeting of the Association-of-British-Neurologists, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Cheung K, Schultz SR, Luk W, 2012, A large-scale spiking neural network accelerator for FPGA systems, Pages: 113-120, ISSN: 0302-9743
Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-the-shelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing with run time proportional to the firing rate of the network. Using only one FPGA, this accelerator is estimated to support simulation of 64K neurons 2.5 times real-time, and achieves a spike delivery rate which is at least 1.4 times faster than a recent GPU accelerator with a benchmark toroidal network. © 2012 Springer-Verlag.
Phoka E, Wildie M, Schultz SR, et al., 2012, Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model, JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Vol: 33, Pages: 323-339, ISSN: 0929-5313
Saleem AB, Longden KD, Schwyn DA, et al., 2012, Bimodal optomotor response to plaids in blowflies: mechanisms of component selectivity and evidence for pattern selectivity., J Neurosci, Vol: 32, Pages: 1634-1642
Many animals estimate their self-motion and the movement of external objects by exploiting panoramic patterns of visual motion. To probe how visual systems process compound motion patterns, superimposed visual gratings moving in different directions, plaid stimuli, have been successfully used in vertebrates. Surprisingly, nothing is known about how visually guided insects process plaids. Here, we explored in the blowfly how the well characterized yaw optomotor reflex and the activity of identified visual interneurons depend on plaid stimuli. We show that contrary to previous expectations, the yaw optomotor reflex shows a bimodal directional tuning for certain plaid stimuli. To understand the neural correlates of this behavior, we recorded the responses of a visual interneuron supporting the reflex, the H1 cell, which was also bimodally tuned to the plaid direction. Using a computational model, we identified the essential neural processing steps required to capture the observed response properties. These processing steps have functional parallels with mechanisms found in the primate visual system, despite different biophysical implementations. By characterizing other visual neurons supporting visually guided behaviors, we found responses that ranged from being bimodally tuned to the stimulus direction (component-selective), to responses that appear to be tuned to the direction of the global pattern (pattern-selective). Our results extend the current understanding of neural mechanisms of motion processing in insects, and indicate that the fly employs a wider range of behavioral responses to multiple motion cues than previously reported.
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