51 results found
Cazé RD, Jarvis S, Foust AJ, et al., 2017, Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity., Neural Comput, Pages: 1-17
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.
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 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
Evans BD, Jarvis S, Schultz SR, et al., 2016, PyRhO: A Multiscale Optogenetics Simulation Platform, FRONTIERS IN NEUROINFORMATICS, Vol: 10, ISSN: 1662-5196
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
Cheung K, Schultz SR, Luk W, 2015, NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors., Front Neurosci, 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.
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., Front Syst Neurosci, Vol: 9, ISSN: 1662-5137
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, 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
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 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
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
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
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
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.
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