76 results found
Schuck R, Quicke P, Copeland C, et al., 2015, Rapid three dimensional two photon neural population scanning
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.
Schuck R, Quicke P, Copeland C, et al., 2015, Rapid three dimensional two photon neural population scanning, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 5867-5870, ISSN: 1557-170X
Tolkiehn M, Schultz S, 2015, Multi-Unit Activity contains information about spatial stimulus structure in mouse primary visual cortex
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%.
Tolkiehn M, Schultz SR, 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 (EMBC), Publisher: IEEE, Pages: 3771-3774, 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
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
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
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
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
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
Jarvis S, Nikolic K, Grossman N, et al., 2013, Controlling the neuronal balancing act: optical coactivation of excitation and inhibition in neuronal subdomains., Pages: P348-P348, ISSN: 1471-2202
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, 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
Onativia J, Schultz SR, 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, ISSN: 1741-2560
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 BM, Guzman-Lopez J, Arshad Q, et al., 2013, Vestibular Activation Differentially Modulates Human Early Visual Cortex and V5/MT Excitability and Response Entropy, CEREBRAL CORTEX, Vol: 23, Pages: 12-+, ISSN: 1047-3211
Caballero J, Urigueen JA, Schultz SR, et al., 2012, SPIKE SORTING AT SUB-NYQUIST RATES, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 585-588, ISSN: 1520-6149
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, JOURNAL OF NEUROSCIENCE, Vol: 32, Pages: 1634-1642, ISSN: 0270-6474
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.
Schaub MT, Schultz SR, 2012, The Ising decoder: reading out the activity of large neural ensembles, JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Vol: 32, Pages: 101-118, ISSN: 0929-5313
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
Cook D, Gillies D, Schultz S, 2010, Using GLMs to recover sparse connectivity in complex networks., BMC neuroscience, Vol: 11, Pages: P53-P53, ISSN: 1471-2202
Phoka E, Wildie M, Petersen RS, et al., 2010, How is a sensory stimulus represented in ongoing dynamics in the barrel cortex?, Pages: P35-P35, ISSN: 1471-2202
Saleem AB, Chadderton P, Apergis-Schoute J, et al., 2010, Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials, JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Vol: 29, Pages: 49-62, ISSN: 0929-5313
Cheung K, Schultz SR, Leong PHW, 2009, A parallel spiking neural network simulator, Sydney, Australia, Pages: 247-254
Schultz SR, Kitamura K, Post-Uiterweer A, et al., 2009, Spatial Pattern Coding of Sensory Information by Climbing Fiber-Evoked Calcium Signals in Networks of Neighboring Cerebellar Purkinje Cells, JOURNAL OF NEUROSCIENCE, Vol: 29, Pages: 8005-8015, ISSN: 0270-6474
Wildie M, Luk W, Schultz SR, et al., 2009, Reconfigurable acceleration of neural models with gap junctions, Pages: 439-442
We describe the design and implementation of an FPGA-based architecture for real-time simulation of spiking neural networks that include gap junctions, a type of synapse not often used in neural models due to their high computational cost. Recent research suggests that electrical synapses or gap junctions play a role in synchronizing the activity of larger groups of neurons in the brain, and are potentially important in high level functions such as cognition and memory. We suggest the simulation cost of gap junctions can be reduced by clustering them within the model, which is consistent with evidence of the structure of gap junction networks and allows each cluster to be updated in parallel. Our implementation on a Xilinx Virtex-5 FPGA demonstrates a 24.3 times speedup over a software implementation running on a cluster of four 3.6GHz Intel Xeon processors. This is part of a larger effort to construct tools capable of real-time simulation and exploration of realistic brain networks of comparable size to biological networks. © 2009 IEEE.
Saleem AB, Krapp HG, Schultz SR, 2008, Receptive field characterization by spike-triggered independent component analysis, JOURNAL OF VISION, Vol: 8, ISSN: 1534-7362
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