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A general framework for investigating how far the decoding process in the brain can be simplified
2008
Neural Information Processing Systems
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We have developed a general framework for investigating how far the decoding process in the brain can be simplified. ...
How is information decoded in the brain?" is one of the most difficult and important questions in neuroscience. ...
Summary and Discussion We described a general framework for investigating how far the decoding process in the brain can be simplified. ...
dblp:conf/nips/OizumiIIHO08
fatcat:kqvk7kayozeizmzsbjpbeoq32y
An introduction to time-resolved decoding analysis for M/EEG
[article]
2019
arXiv
pre-print
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The human brain is constantly processing and integrating information in order to make decisions and interact with the world, for tasks from recognizing a familiar face to playing a game of tennis. ...
dynamics in the human brain. ...
Acknowledgments: We would like to thank the reviewers and Nick McNair, Denise Moerel, Selene Petit for their feedback and suggestions. ...
arXiv:1905.04820v1
fatcat:wg27j3nnpreslahx3brio424aq
Creating the brain and interacting with the brain: an integrated approach to understanding the brain
2015
Journal of the Royal Society Interface
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from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop. ...
Subject Areas: computational biology In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary ...
Brain activity was a cause; a behavioural change was the result in this DecNef example. In a more general framework, DecNef can be described as follows. ...
doi:10.1098/rsif.2014.1250
pmid:25589568
pmcid:PMC4345490
fatcat:aviuaqaisfdiloir6ahnhrhfem
Optimal Population Coding for Dynamic Input by Nonequilibrium Networks
2022
Entropy
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Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. ...
We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. ...
The work was performed during the alternative national service, which the author acknowledges for providing flexibility for independent research. ...
doi:10.3390/e24050598
pmid:35626482
pmcid:PMC9140425
fatcat:schvzei5lfb75lveuht7p3br4e
Pattern-information analysis: From stimulus decoding to computational-model testing
2011
NeuroImage
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helpful, but also limiting in terms of the questions that can be addressed. ...
These methods sample the stimulus (or mental-state) space more richly, estimate a separate response pattern for each stimulus, and can generalize from the stimulus sample to a stimulus population. ...
Acknowledgment I thank Kendrick Kay and Tom Mitchell for helpful comments on a draft of this manuscript. ...
doi:10.1016/j.neuroimage.2011.01.061
pmid:21281719
fatcat:pbloc55h3nfgva3c7ag6lr65ay
Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
[article]
2020
arXiv
pre-print
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In terms of vision, incoming information can be processed by the brain in millisecond interval. ...
Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling ...
We review some of these decoders with an emphasis on how they can be used for retinal neuroprosthesis to get a better performance for both static images and dynamical videos. ...
arXiv:2001.04064v1
fatcat:sqralx5q2zca3k6nvamur46wbe
Coherence: The Measurement and Application of Brain Connectivity
2017
NeuroRegulation
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We argue that EEG coherence presents a unique target for treatment of these and other populations, in that the ability to modulate connectivity via EEG neurofeedback has been shown to be of significant ...
(NCC) framework for understanding these disorders. ...
Within the NCC framework described here, the value of cognitive tests in assessing brain networks can be clarified. ...
doi:10.15540/nr.4.1.3
fatcat:5nfkexa6xrdnpekvuxjqgqbipi
Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
2020
Engineering
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Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ...
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version ...
We review some of these decoders with an emphasis on how they can be used to give retinal neuroprostheses a better performance for both static images and dynamic videos. ...
doi:10.1016/j.eng.2020.02.004
fatcat:33rqlxpyefdd7luo3pkm4wumqq
Bioartificial Brains and Mobile Robots
[chapter]
2011
Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training
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The proposed paradigm was not that far from having a simplified tool for studying "learning", seen as an "experience dependent" process (i.e. adaptive process) where the wiring process of the brain needs ...
Indeed, all that can be usefully tested and studied in such a simplified experimental framework then need to be translated to an in-vivo situation in which an external devices is connected to specific ...
doi:10.5772/26378
fatcat:odjrkd5b6reermrfvjel7loj3i
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
[article]
2021
bioRxiv
pre-print
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Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. ...
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state ...
generous gifts from the Alfred Sloan Foundation (RL, ELD) and the McKnight Foundation (RL, MA, CHL, ELD). ...
doi:10.1101/2021.07.21.453285
fatcat:ddlz3krurreabfvpmzizyaqabi
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
[article]
2023
arXiv
pre-print
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We employ an fMRI dataset of natural image vision and create a deep learning decoding pipeline inspired by the existence of both bottom-up and top-down processes in human vision. ...
These features are then categorized in the latent space using a nearest-neighbor strategy, and the results are used to condition a generative latent diffusion model to create novel images. ...
Brain and Deep Learning Models Our brain can be thought of as a prediction machine that utilizes past knowledge in the form of top-down processing of external inputs. ...
arXiv:2212.06726v2
fatcat:esc7a2p6xfg45es6m47itxg3bi
Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data
2018
PLoS Computational Biology
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The framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach. ...
In particular, the theory identifies exactly what valid inferences can be made about independent encoding of stimulus dimensions from the results of multivariate analyses of neuroimaging data and psychophysical ...
Author Contributions Conceptualization: Fabian A. Soto.
Data curation: Fabian A. Soto. Formal analysis: Fabian A. Soto. ...
doi:10.1371/journal.pcbi.1006470
pmid:30273337
pmcid:PMC6181430
fatcat:croyw4oyy5f4tgcpnuwjx6b35u
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning
[article]
2021
arXiv
pre-print
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Furthermore, we show that because the randomized weights do not depend on the data, several decoders can share a single randomized encoder, giving RWFNs a unique economy of spatial scale for simultaneous ...
The randomized projections between input neurons and higher-order processing centers in the input brain is mimicked in RWFN by a single-hidden-layer neural network that specially structures latent representations ...
Acknowledgments This work was supported in part by NSF SES-1735579. ...
arXiv:2109.06663v1
fatcat:xe5c5mb6jjfbbkpocrh3usqr7q
A zero-shot learning approach to the development of brain-computer interfaces for image retrieval
2019
PLoS ONE
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Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. ...
In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically ...
models can be a feasible approach to image decoding in a real-world BCI framework. ...
doi:10.1371/journal.pone.0214342
pmid:31525201
pmcid:PMC6746355
fatcat:brrt7s2gojc4tgcqlniuucceu4
Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2312.07213v2 fatcat:cuq2cmdofvaj5aqqbwlw6ht3fiOther Versions
Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based ...
In addition, the latest progress of deep learning in different tasks of human-computer interaction for brain-inspired computing is reviewed from six perspectives, such as data sets and different brain ...
In the future, more application scenarios can be broadened, BCI systems across language boundaries can be investigated, and paradigms for shifting neural network architectures can be transformed. ...
arXiv:2312.07213v3
fatcat:uepgf367kbbhvjsvqhp6hi5xm4
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