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fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Shuo Huang, Wei Shao, Mei-Ling Wang, Dao-Qiang Zhang
2021 International Journal of Automation and Computing  
, brain activity pattern analysis, and visual stimuli reconstruction.  ...  the neural activation of human brains with satisfied spatiotemporal resolutions.  ...  /V3) and then built a sparse multi-scale multinomial logistic regression (SMLR) local decoder model for visual stimuli reconstruction.  ... 
doi:10.1007/s11633-020-1263-y fatcat:kwls2cvw4zgd5dti5d54uy6pgi

Ghosts in machine learning for cognitive neuroscience: Moving from data to theory

Thomas Carlson, Erin Goddard, David M. Kaplan, Colin Klein, J. Brendan Ritchie
2018 NeuroImage  
The third ghost emerges from our limited ability to distinguish information that is merely decodable from the brain from information that is represented and used by the brain.  ...  The first ghost arises from difficulties in determining what information machine learning classifiers use for decoding.  ...  Summary of dimension reduction by multi-dimensional scaling (MDS) of the classifier performance discriminating multi-unit responses to moving dot fields (84 unique stimuli, of 12 directions and 7 speeds  ... 
doi:10.1016/j.neuroimage.2017.08.019 pmid:28793239 fatcat:7nm43es45rcldjelaiqtpm3hma

A simple spiking retina model for exact video stimulus representation

Aurel A Lazar, Eftychios A Pnevmatikakis
2008 BMC Neuroscience  
The Encoding and decoding mechanisms for video stimuli: The stimulus is filtered by the receptive fields of the neurons and enters the soma Figure 1 Encoding and decoding mechanisms for video stimuli:  ...  In the decoding part each spike, represented by a delta pulse, is weighted by an appropriate coefficient and then filtered from the same receptive field for stimulus reconstruction.  ...  The Encoding and decoding mechanisms for video stimuli: The stimulus is filtered by the receptive fields of the neurons and enters the soma Figure 1 Encoding and decoding mechanisms for video stimuli  ... 
doi:10.1186/1471-2202-9-s1-p130 fatcat:ohi7hbrrevcb3jwt5djajzwmpq

Neural decoding dissociates perceptual grouping between proximity and similarity in visual perception [article]

Lin Hua, Fei Gao, Chantat Leong, Zhen Yuan
2021 bioRxiv   pre-print
Meanwhile, electrophysiological (EEG) response patterns were able to decode the specific pattern out of the six visual stimuli involving both principles in each trail by using time-resolved multivariate  ...  The results therefore provide direct evidence for a link between human perceptual space of grouping decision-making and neural space of these brain response patterns.  ...  Interestingly, the neural response patterns obtained from multi-class MVPA of EEG recordings corroborated results of global electric field.  ... 
doi:10.1101/2021.11.15.468580 fatcat:ds56gkrpevcineylc2je2hruyu

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Lin Li, Austin J. Brockmeier, John S. Choi, Joseph T. Francis, Justin C. Sanchez, José C. Príncipe
2014 Computational Intelligence and Neuroscience  
For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering.  ...  This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control.  ...  This enables kernel-based machine learning methodologies to leverage multi-scale neural activity to uncover the mapping from the neural system states and the corresponding stimuli.  ... 
doi:10.1155/2014/870160 pmid:24829569 pmcid:PMC4009155 fatcat:mpojdu3bgzc4dizd32l7vd7khq

Sparse Representations for the Cocktail Party Problem

H. Asari, B. A. Pearlmutter, A. M. Zador
2006 Journal of Neuroscience  
The experimentally testable predictions that arise from this model-including a novel method for estimating a neuron's optimal stimulus using data from a multi-neuron recording experiment-are generic, and  ...  the cues provided by the differential filtering imposed on a source by its path from its origin to the cochlea (the head-related transfer function, or HRTF).  ...  We have assumed that the neural decoding function-the transformation from the neural response to the stimulus-is linear.  ... 
doi:10.1523/jneurosci.1563-06.2006 pmid:16837596 fatcat:bu4lwodnwjcsboferrpbvdtvny

Transductive neural decoding for unsorted neuronal spikes of rat hippocampus

Zhe Chen, F. Kloosterman, S. Layton, M. A. Wilson
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Neural decoding is an important approach for extracting information from population codes.  ...  We previously proposed a novel transductive neural decoding paradigm and applied it to reconstruct the rat's position during navigation based on unsorted rat hippocampal ensemble spiking activity.  ...  Neural decoding is important not only for understanding neural codes (i.e., neural response features capable of representing all information that neurons carry about the stimuli of interest), but also  ... 
doi:10.1109/embc.2012.6346178 pmid:23366139 pmcid:PMC3972894 fatcat:gx6dx7owifcctpucqfwlhflroy

Inter-subject neural code converter for visual image representation

Kentaro Yamada, Yoichi Miyawaki, Yukiyasu Kamitani
2015 NeuroImage  
The neural code converter was designed to learn statistical relationships between fMRI activity patterns of paired subjects obtained while they saw an identical series of stimuli.  ...  We show that fMRI activity patterns for visual images not used for training the converter could be predicted from those of another subject where brain activity was recorded for the same stimuli.  ...  Pixel patterns calculated from selectively weighted voxels may represent more reliable responses to stimuli than non-weighted voxel patterns.  ... 
doi:10.1016/j.neuroimage.2015.03.059 pmid:25842289 fatcat:y3c735ivuvanvbikjj7b6hvvri

Linking V1 Activity to Behavior

Eyal Seidemann, Wilson S. Geisler
2018 Annual Review of Vision Science  
The ultimate goal in this framework is to find the actual decoder-the model that best predicts behavior from neural responses.  ...  We start by describing a conceptual approach-the decoder linking model (DLM) framework-in which candidate decoding models take neural responses as input and generate predicted behavior as output.  ...  Stimuli give rise to neural activity in V1, which is processed (decoded) by subsequent areas of the brain to produce a behavioral response.  ... 
doi:10.1146/annurev-vision-102016-061324 pmid:29975592 pmcid:PMC6141357 fatcat:cfsv5tanhne7nmee5uyehh7w2y

EEG-Based Decoding of Auditory Attention to a Target Instrument in Polyphonic Music

Giorgia Cantisani, Slim Essid, Gael Richard
2019 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)  
Auditory attention decoding aims at determining which sound source a subject is "focusing on".  ...  To our knowledge, this model was never applied to musical stimuli for decoding attention.  ...  Auditory attention decoding aims at determining, from the brain's activity, which sound source a subject is "focusing on" while listening to a complex auditory scene.  ... 
doi:10.1109/waspaa.2019.8937219 dblp:conf/waspaa/CantisaniER19 fatcat:s7t5oqqnyffcxpcdz74fqdwxia

Improved multi-unit decoding at the brain–machine interface using population temporal linear filtering

D J Herzfeld, S A Beardsley
2010 Journal of Neural Engineering  
Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct  ...  The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated  ...  One way to minimize the computational cost associated with decoding neural signals is to decode directly from multi-neuron recordings without spike sorting [2, 15, 16] .  ... 
doi:10.1088/1741-2560/7/4/046012 pmid:20644245 fatcat:rmolgyzl3vgkhmtwagn25uwa7y

Spatio-temporal Representations of Uncertainty in Spiking Neural Networks

Cristina Savin, Sophie Denève
2014 Neural Information Processing Systems  
Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures.  ...  The neural encoding of such distributions remains however highly controversial.  ...  The core idea is that the network activity evolves through recurrent dynamics such that samples from the posterior distribution can be linearly decoded from the (quasi-)instantaneous neural responses.  ... 
dblp:conf/nips/SavinD14 fatcat:vg6lnrhgezgnxnfjhxks2eaq2y

Similarity representation of pattern-information fMRI

ShaoWei Xue, XuChu Weng, Sheng He, DianWen Li
2013 Chinese Science Bulletin  
., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation.  ...  This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform  ...  The logic behind is: if prediction of the stimuli from the measured/observed neural response patterns is significantly above chance level, the patterns provides information about the stimuli [17] .  ... 
doi:10.1007/s11434-013-5743-0 fatcat:af6oc4nmnjftrapukernlxkv6u

The time-course of component processes of selective attention

Tanya Wen, John Duncan, Daniel J. Mitchell
2019 NeuroImage  
While many studies have investigated individual neural signatures of attention, here we used multivariate decoding of electrophysiological brain responses (MEG/EEG) to track and compare multiple component  ...  Combining single and multi-item displays with different types of distractors allowed multiple aspects of information content to be decoded, distinguishing distinct components of attention, as the selection  ...  TW was supported by the Taiwan Cambridge Scholarship from the Cambridge Commonwealth, European & International Trust and the Percy Lander studentship from Downing College.  ... 
doi:10.1016/j.neuroimage.2019.05.067 pmid:31150787 pmcid:PMC6693528 fatcat:me2ukbzqnbfhzguwt5mt2ipycu

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception

Diana C. Dima, Gavin Perry, Krish D. Singh
2018 NeuroImage  
Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm.  ...  Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential  ...  Decoding responses to unfiltered scene categories Sensor-space decoding To evaluate differences in neural responses between stimulus categories, we performed time-resolved decoding of responses to scenes  ... 
doi:10.1016/j.neuroimage.2018.06.033 pmid:29902586 pmcid:PMC6057270 fatcat:b7loogks5nefjdojo3qcz2jqyy
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