Questions tagged [neuroscience]

A field of science that studies nervous systems. Use [neuroimaging] tag for questions about fMRI data analysis.

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Drift-diffusion model: Can the accumulated evidence be expressed as probability?

For a reaction-time model, I am considering whether I can compare 1) a probabilistic classifier or survival model and 2) a drift-diffusion model (DDM). I am interested in predicting reaction ...
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Multiple Sclerosis: study design to beta test a software

I am a beta-tester of a software that is intended to help the radiologist to interpret MRI reading of Multiple Sclerosis (MS). MS is a disease that, over time, could lead to new lesions, expanding ...
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Training a Neural Network on Two Interrelated Tasks

I'm working on a project where I'm interested in comparing the performance of a Neural Network to human performance on a multisensory perceptual task. The task is quite easy, but I'm more interested ...
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What if weights of model is output of neurons?

If instead of, giving axon's weights some number value, why not give it output value from other neuron. I think, taking output from neuron in previous layer and setting it as weight in current layer ...
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Doing ICA analysis on MEG data with 248 channels

Deal all, I am trying to localize sources of brain activity using MEG data. I first want to compute ICA and then localize independent components of interest. However, I sometimes have trouble making a ...
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Is it possible to run Spiking Neural Network (SNN) on the current von neumann architecture?

I am a new to Spiking Neural network (SNN). I read a couple of papers about it. Some of them highlighted that SNN is a kind of a hardware-dependent model that can efficiently work on neuromorphic ...
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What is 'Soft' normalization ? (not softmax)

While reading the neuroscience paper "Neural population dynamics during reaching" by Churchland et al. 2012, Nature, the authors mention using 'soft' normalization of their (biological) ...
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What is a "filter" and what does "filtering" mean in statistics/engineering/computer science?

I see the term "filter" in many neuroscience papers including those with heavy statistical content ("spatial filter", "temporal filter", etc.), as well as those with ...
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Square root transformation of Poisson process. How $\small Var[\sqrt{P(\lambda)}] \approx \frac{1}{4}$

I am working on Kaggle Neural data challenge. I am trying to understand the transformation applied on the neural spiking data. A number of spikes given a stimulus are Poisson distributed as $$Y_i \...
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Generalised linear mixed effect model(gamer)

I have total of 230 subjects where each subject has 82 volume data of 82 brain region. Not all but many subjects have gone time point 1, time point 2 and time point 3 scans. So, this leads to total of ...
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Fluctuations in the HH Model

The Hodgkin Huxley Model describes the voltage that flows through a membrane of a nerve cell - this voltage is subject to arbitrary fluctuations that result from the stochastic closing and opening of ...
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Regression on unevenly distributed high dimensional dataset

I have a very high dimensional (20K+ hand engineered features) biological dataset to predict a single continuous output variable (such as a mental state exam scores for a dementia patient). The output ...
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How to perform machine learning on data with many trials on individual subjects

I have an EEG dataset. The data are epoched around an event of interest and are of the form channels x timepoints x trials. There are many of these types of datasets for several subjects. Because ...
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Updating Prediction Errors in Gradient Ascent (Friston's Free-energy)

Background In Rafal Bogacz's tutorial on the free-energy framework for modelling perception and learning, section 2.3 we have: $$\dot{\phi} = \frac{\partial F}{\partial\phi} = \varepsilon_u g'(\phi) ...
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Feedback Neural Network

I'm looking for a Neural Network has feedback weights. Feedback weights are connections between neurons from a layer to a previous layer. For instance: from the output layer to the input layer. I did ...
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How to find difference between data recorded for 1431 variables for X subjects, across 3 time points?

I am doing research using magnetoencephalography (MEG). I'm performing coherence analysis which, briefly, is a measure of neuronal synchronicity and functional connectivity. For each subject scanned ...
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How to test whether event A affects the frequency at which event B occurs

I conducted an experiment in which I measured the occurrence of some event A over time. I then intervened in the system, periodically introducing event B. I hypothesise that event B affects the ...
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12 answers
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Why do neural networks need so many training examples to perform?

A human child at age 2 needs around 5 instances of a car to be able to identify it with reasonable accuracy regardless of color, make, etc. When my son was 2, he was able to identify trams and trains, ...
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biological basis for excluding values outside 3 standard deviations from the mean? [duplicate]

Is there any biological basis for excluding outliers in a dataset of blood cytokine levels (e.g. values outside 3 standard deviations from the mean for each cytokine) as measured by multiplex ...
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For EEG analysis, why is it more efficient to use the raw data than images of the data?

Data Science publications for EEG analysis like EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces use the raw EEG data (see: github vlawhern/arl-eegmodels ) rather than ...
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Bonferroni correction and discussion of the results

In a study in which I analyze several biomarkers through logistic and linear regressions, should I discuss in the discussion of the paper the results with p <0.05 (they only have a nominal ...
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Statistic analysis on dependent and simultaneously independent data

Let's assume that I have a datasheet looking like that: ...
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Does correlation = 0.2 mean that there is an association "in only 1 in 5 people"?

In The Idiot Brain: A Neuroscientist Explains What Your Head is Really Up To, Dean Burnett wrote The correlation between height and intelligence is usually cited as being about $0.2$, meaning ...
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Couldn't ANN's be modeled on any part of the body (not just the brain)?

It's commonly said that ANN's are modeled after the brain, but as we know, neurons are also found elsewhere in the human body. So if someone was being provocative and said that ANN's could equally ...
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How to reverse factor analysis (FA) and reconstruct original variables?

I saw this interesting topic: How to reverse PCA and reconstruct original variables from several principal components? and a nice answer with a very useful example of Iris data in Matlab. I would like ...
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How to determine needed amount of random samples of whole population?

I wrote an algorithm that categorizes a large set of electrophysiological data, way more than I could go through per hand (think over 10.000 cells). I want to take randomly drawn sample cells out of ...
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2 votes
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Finding time intervals where two groups of time series are significantly different

I have two sets of time series, let's call them $C$ and $D$. Assume that $C$ ($D$) has $n$ ($m$) samples (each sample is a time-series of length $T$). I want to assess if there are periods of time ...
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Cross-fertilization of neural network/machine learning theory and R&D in physiology

Clearly, the development of neural networks has been based, at least initially, on those occurring in actual animals, human or otherwise. Conversely, how have neural-network or machine-learning models/...
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1 answer
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Comparing results of unsupervised clustering to a known classification

Disclaimer: I'm looking for a bit of help as I'm only a simple neuroscientist and even working out what to google in this area is a tricky prospect. Here goes: I have a set of data (3d positions in ...
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8 votes
1 answer
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Meaning of low power in neuroscience after combining results of many meta-analyses (Button et al 2013)

In a 2013 review article in Nature Neuroscience, Button et al. Power failure: why small sample size undermines the reliability of neuroscience, it was stated that: the average statistical power of ...
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May outliers represent significant values?

Each element of a 56x1 vector represents the functional association between two brain networks. I want to assess which of these 56 values are significant. One way to deal with this is to use an ...
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To what extent are convolutional neural networks inspired by biology?

I have read in several places that convolutional neural networks were biologically inspired. In what ways do CNNs mirror biology, and in what ways don't they? Is there a more biologically plausible ...
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How predict time series B given A; predict time series transform?

Assume a unidirectional circuit that transforms information. If one records the activity from the middle and end of the circuit they will end up with two times series. My goal is to predict the ...
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1 answer
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Machine Learning application to neuroscientific data

I have the following question, and it is (for me) an open research question. I really don't have a feeling if it is a difficult question or a trivial one, getting a better feeling for approaches I ...
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What is a d-prime experiment

I am reading about Roc curves and have encountered the term ""D-prime experiment" does anyone know what it is?
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5 votes
1 answer
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Linear model: only the whole model significant?

This is the result of a linear model (MatLab, LinearModel.fit): ...
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3 votes
1 answer
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Correlation with repeated measures

For each of my 20 subjects, I have: physiological parameter: brain activity (y1) at time T1; physiological parameter: brain activity (y2) at time T2; behavioral parameter: reaction time (X); I want ...
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8 votes
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Bootstrap to test differences between correlation coefficients

I have two correlation coefficients ($r_1$ and $r_2$), obtained within the same sample (20 subjects). My aim is to test it they are significantly different. $r_1$ is the correlation between a ...
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3 votes
1 answer
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Approximating Bernoulli distributions as Poisson distributions in analysis of neural data

I'm reading Analysis of Neural Data, by Kass et al (2014), where Kass argues that spike trains can be viewed as point processes (in chapter 19). Furthermore, he goes on to convert spike trains (that ...
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1 answer
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Tests for comparing correlation values. The winner is?

The question is relatively simple, but trivial. I have data divided by one fixed effect (Emotion, with 2 conditions: A and B), and I have a covariate, for each of my 20 subjects. My aim is to 1) ...
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3 votes
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Is Fisher's z transformation necessary when comparing the variances of correlation coefficients?

I am working on functional connectivity between brain regions, where functional connectivity is represented by the Pearson correlation coefficient between the time series (fMRI) of brain regions. I am ...
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Classifying spikes as excitatory or inhibitory based on their shape

I have two classes of spike, namely, excitatory spikes and inhibitory spikes. In the figure you can see their shapes. I want to classify my spikes based on these two shapes. The shape in the figure ...
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2 votes
1 answer
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Reliability of single case reports vs group inference

In his paper "Ten ironic rules for non-statistical reviewers", Karl Friston includes the following tongue-in-cheek response of a fictional author to a reviewer who complains about the sample size ...
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Neuroimaging in SPM & GIFT - Regressor Convolution and fitting a GLM to ICA data

I have two kind of disparate questions. I am attempting to replicate the following paper: http://www.sciencedirect.com/science/article/pii/S1053811916300428 I am using SPM to perform preprocessing ...
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66 votes
1 answer
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40,000 neuroscience papers might be wrong

I saw this article in the Economist about a seemingly devastating paper [1] casting doubt on "something like 40,000 published [fMRI] studies." The error, they say, is because of "erroneous statistical ...
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2 votes
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Studying how parameters affect standard deviation of skewed data

I'm running a bunch of simulations that are modeling the first time a neuron fires when it receives stochastic input and has intrinsic noise. The program I wrote creates a dataset of a bunch of time ...
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Comparing neuron "jitter" values across trials

I'm asking this question on the statistics forum because I'm wondering a bit about drawing appropriate conclusions supported by statistics from skewed data (if it belongs on another area of stack ...
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2 answers
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How to compute the firing rate of a neuron with different duration of trials?

I have recorded data from a neuron from different trials. Each trial we show a stimulus to a rat and then we measure the neural activity. I want to measure the PSTH which is peristimulus time ...
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4 votes
1 answer
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What does the number of independent components produced by ICA depend on?

I'm a student working on my bachelor thesis performing independent component analysis (ICA) on some fMRI data using MELODIC FSL. I would like to ask some questions regarding the results of ICA. ...
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2 votes
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Experimental design: Same randomly selected stimuli versus different randomly selected stimuli in every subject

In an experiment, we’re going to present randomly generated stimuli to multiple subjects one trial at a time and measure neural responses to them. The stimuli are randomly generated to satisfy the ...
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