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Kernel Methods on Spike Train Space for Neuroscience: A Tutorial

Il Memming Park, Sohan Seth, Antonio R.C. Paiva, Lin Li, Jose C. Principe
2013 IEEE Signal Processing Magazine  
This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed.  ...  Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space.  ...  On the other hand, a better method for reading out the stimulus from the spike trains (neural decoding) can have major impact on a number of clinical and biomedical applications.  ... 
doi:10.1109/msp.2013.2251072 fatcat:lxwgwxt7nzhazg5hroo254bdve

A Tutorial for Information Theory in Neuroscience

Nicholas M. Timme, Christopher Lapish
2018 eNeuro  
Our primary audience for this tutorial is researchers new to information theory.  ...  In this tutorial, we provide a thorough introduction to information theory and how it can be applied to data gathered from the brain.  ...  The probability for a neuron to spike was modulated in space using a two-dimensional Gaussian function centered on the place field for that neuron and with a standard deviation of 0.15 spatial units.  ... 
doi:10.1523/eneuro.0052-18.2018 pmid:30211307 pmcid:PMC6131830 fatcat:bo3snmwevrdczn3f24v6fastsi

A Tutorial on Sparse Gaussian Processes and Variational Inference [article]

Felix Leibfried, Vincent Dutordoir, ST John, Nicolas Durrande
2021 arXiv   pre-print
The purpose of this tutorial is to provide access to the basic matter for readers without prior knowledge in both GPs and VI.  ...  Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems.  ...  Acknowledgments Many thanks to Vincent Adam for providing feedback to an early version of this manuscript.  ... 
arXiv:2012.13962v11 fatcat:q5ecvxiucjgpve4rarvgzlbofa

25th Annual Computational Neuroscience Meeting: CNS-2016

Tatyana O. Sharpee, Alain Destexhe, Mitsuo Kawato, Vladislav Sekulić, Frances K. Skinner, Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári, Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett (+597 others)
2016 BMC Neuroscience  
These results outline a framework for categorizing neuronal types based on their functional properties.  ...  BMC Neuroscience 2016, 17(Suppl 1):A1 Neural circuits are notorious for the complexity of their organization.  ...  Allen and Jody Allen, for their vision, encouragement and support.  ... 
doi:10.1186/s12868-016-0283-6 pmid:27534393 pmcid:PMC5001212 fatcat:bt45etzj2bbolfcxlxo7hlv6ju

Removing independent noise in systems neuroscience data using DeepInterpolation [article]

Jerome Lecoq, Michael Oliver, Joshua H Siegle, Natalia Orlova, Christof Koch
2020 bioRxiv   pre-print
Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a nonlinear interpolation model using only noisy samples from the original raw data.  ...  In extracellular electrophysiology recordings, DeepInterpolation recovered 25% more high-quality spiking units compared to a standard data analysis pipeline.  ...  Second, we trained our models 281 on large databases, demonstrating the impact of richer denoising models on existing neuroscience scientific 282 data and data workflows like cell segmentations or unit  ... 
doi:10.1101/2020.10.15.341602 fatcat:wxba4gtjujhrfa5gn5zvundbqi

KInNeSS: A Modular Framework for Computational Neuroscience

Massimiliano Versace, Heather Ames, Jasmin Léveillé, Bret Fortenberry, Anatoli Gorchetchnikov
2008 Neuroinformatics  
Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologicallyrealistic computational models of animal behavior is often a difficult task.  ...  Mike Hasselmo, Dash Sai Gaddam, and Jesse Palma for numerous valuable discussions and suggestions for this paper.  ...  Acknowledgements This work was supported by the Center of Excellence for Learning in Education, Science and Technology (NSF SBE-0354378).  ... 
doi:10.1007/s12021-008-9021-2 pmid:18695948 fatcat:coreidauxnawxplu3tjecsxi6i

NEST Desktop - An educational application for neuroscience

Sebastian Spreizer, Johanna Senk, Stefan Rotter, Markus Diesmann, Benjamin Weyers
2021 eNeuro  
We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.Significance StatementThe graphical user  ...  NEST Desktop effectively supports teaching the concepts and methods of computational neuroscience.  ...  We present a web-based software tool, which has been specifically developed to support education and training of basic computational neuroscience for individual learners and classroom teaching.  ... 
doi:10.1523/eneuro.0274-21.2021 pmid:34764188 pmcid:PMC8638679 fatcat:ajzdvl2wevbz5mqpwhtykiyz4u

NEST Desktop - An educational application for neuroscience [article]

Sebastian Spreizer, Johanna Senk, Stefan Rotter, Markus Diesmann, Benjamin Weyers
2021 bioRxiv   pre-print
We view the availability of the tool on public resources like the European ICT infrastructure for neuroscience EBRAINS as a contribution to equal opportunities.  ...  Simulation software for spiking neuronal network models matured in the past decades regarding performance and flexibility.  ...  Acknowledgments We thank Jens Buchertseifer for the collaboration in code development and review of NEST Desktop, and Jochen Martin Eppler for discussion and development of NEST Server.  ... 
doi:10.1101/2021.06.15.444791 fatcat:ty73vkhwjzf3jblf34dhgqmhji

29th Annual Computational Neuroscience Meeting: CNS*2020

2020 BMC Neuroscience  
I'll provide a high-level introduction to deep RL, discuss some recent neuroscience-oriented investigations from my group at DeepMind, and survey some wider implications for research on brain and behavior  ...  Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification.  ...  Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California Nano-Systems Institute (Challenge grants to SJ), the Research Corporation for  ... 
doi:10.1186/s12868-020-00593-1 pmid:33342424 fatcat:edosycf35zfifm552a2aogis7a

A Harmonic Analysis View on Neuroscience Imaging [chapter]

Paul Hernandez—Herrera, David Jiménez, Ioannis A. Kakadiaris, Andreas Koutsogiannis, Demetrio Labate, Fernanda Laezza, Manos Papadakis
2012 Excursions in Harmonic Analysis, Volume 2  
We also present an algorithm for the construction of synthetic data (computational phantoms) for the validation of algorithms for the morphological reconstruction of neurons.  ...  and sink into the heart of the soul, as Homer says in a parable, meaning to indicate the likeness of the soul to wax (κηρóς); these, I say, being pure and clear, and having a sufficient depth of wax,  ...  Their performance varies and depends on the level of training per data set, the noise that affects the data and on the level of manual intervention required.  ... 
doi:10.1007/978-0-8176-8379-5_21 fatcat:uu7qxmlj6zfz3gwfhlybl5xype

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

2018 BMC Neuroscience  
Acknowledgements We thank Ramón Huerta for his useful discussions on this work. This research was supported by the Spanish Government projects TIN2014-54580-R and TIN2017-84452-R.  ...  Acknowledgements This research was supported by NIH grant NS086082 and a GSU Brains and Behavior Seed Grant (DNC), N.H. is a Brains and Behavior and Honeycutt Fellow; A.A.P. is a 2CI Neurogenomics and  ...  Visualization using 3D Kohonen maps: Spike-trains were convoluted with an exponentially decaying kernel [3] yielding activity vectors by sampling the signals at a frequency of 1 kHz.  ... 
doi:10.1186/s12868-018-0452-x pmid:30373544 pmcid:PMC6205781 fatcat:xv7pgbp76zbdfksl545xof2vzy

Neurosciences and 6G: Lessons from and Needs of Communicative Brains [article]

Renan C. Moioli, Pedro H. J. Nardelli, Michael Taynnan Barros, Walid Saad, Amin Hekmatmanesh, Pedro Gória, Arthur S. de Sena, Merim Dzaferagic, Harun Siljak, Werner van Leekwijck, Dick Carrillo, Steven Latré
2020 arXiv   pre-print
This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains: Neurosciences and wireless communications, motivated by the ongoing  ...  (Neurosciences for Wireless), and the other focused on how wireless communication theory and 6G systems can provide new ways to study the brain (Wireless for Neurosciences).  ...  The sequence of spikes over time from a single-neuron is known as spike train, which is the data structure used as input to spike-based BMIs [46] .  ... 
arXiv:2004.01834v1 fatcat:3ocgtuiy55em3fxn3c5btxjl7u

An Overview of Bayesian Methods for Neural Spike Train Analysis

Zhe Chen
2013 Computational Intelligence and Neuroscience  
Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels.  ...  Some research challenges and opportunities for neural spike train analysis are discussed.  ...  This work was also supported by the NSF-IIS CRCNS (Collaborative Research in Computational Neuroscience) Grant (no. 1307645) from the National Science Foundation.  ... 
doi:10.1155/2013/251905 pmid:24348527 pmcid:PMC3855941 fatcat:nkst6mt3sfcqheuxheda3wq4wq

Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design

Maryam Parsa, John P. Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, Kaushik Roy
2020 Frontiers in Neuroscience  
We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52-71% for Pole-Balance), while having minimum effect on another (i.e., 50-53% for RoboNav  ...  In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.  ...  There are different methods to estimate this kernel function based on the smoothness, noise level and periodicity of the ground truth.  ... 
doi:10.3389/fnins.2020.00667 pmid:32848531 pmcid:PMC7396641 fatcat:jud4jgv3ejawjjtfcxeqtzza5a

Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)

Jacques Kaiser, Hesham Mostafa, Emre Neftci
2020 Frontiers in Neuroscience  
Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing gradients.  ...  A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks.  ...  Its linear scalability enables the training of hundreds of thousands of spiking neurons on a single GPU, and continual learning on very fine time scales.  ... 
doi:10.3389/fnins.2020.00424 pmid:32477050 pmcid:PMC7235446 fatcat:o3exad7ydnc7ffvbbf2dcx2soq
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