https://crcns.org These are the search results for the query, showing results 631 to 645. https://crcns.org/forum/using-datasets/440665814/840975711 Hi Nicholas, Thanks for your interest in our data. As you assumed, "response.trace" contains the raw data trace, which is the membrane potential of a rat primary auditory cortical neuron (in units of mV as in "response.unit") in response to sound (as in "stimulus" structure). Please see "ReadMe.txt" for details of the data structure, see "Methods.txt" for general descriptions of our experimental procedures, and see "Contents.m" for a brief description of the data in each directory.  For details, please refer to our papers:   Machens et al. (2004) J Neurosci 24:1089-1100.   Asari and Zador (2009) J Neurophysiol 102(5): 2638-56. Hiroki   No publisher asari 2012-07-03T04:08:28Z Comment https://crcns.org/forum/using-datasets/876922272/901523303 I wrote up a post on xcorr on using the V2-1, pvc-4, and MT datasets; includes info on upsampling spike trains, cropping stimuli, rejecting data, etc. It's all here: http://xcorr.net/2014/03/29/tips-on-using-crcns-datasets/ No publisher pmineault 2014-03-29T20:36:01Z Comment https://crcns.org/forum/using-datasets/527189381/634920535 Thanks very much for your reply! But I still have some questions. I hope you can help me, thanks again. 1. I'm still confused about the unit of the LFP waveform in the .eeg files. In the "crcns.org hc2 data description" pdf file, it is mentioned that the neurophysiological signals were amplified 1,000x, but you said  the voltage was amplified 20,000x, why? In addition, taking the data "ec013.527" for example, the voltage range is 20, the maximum value and minimum value is 13441 and -6975 respectively (which are achieved by using the matlab script "LoadBinary"), how can I convert the data point of the LFP waveform to the real neural signal, namely, how can I get the unit?  2. I've comprehended that the spike clusters are unrelated to the number of channels, but I want to know that how a specified cluster is assigned to the corresponding electrode. In the "LoadCluRes" script, it is commented that "Map is a matrix displaying the correspondance between new cluster numbers (first column) and inital shank # (second column) and cluster # (third column)". In the matrix "Map" of "ec013.527", for example, the 9th line is " 9 1 12", what the "12" means?  3. In the "crcns.org hc2 data description" pdf file, it is mentioned that the "4-shank or 8-shank silicon probe in layer CA1 of the right dorsal hippocampus". It means that all the electrode are located in the layer CA1, and I cannot obtain the signals in the EC. Is it the fact? If I am right, how can I know which neurons in the deeper parts of the layer and which neurons in the superficial parts of the layer (this is mentioned in the paper "Hippocampal CA1 pyramidal cells form functionally distinct sublayers")?    Best regards, Li Previously Gautam Agarwal wrote: Hi, 1. Channels 0-30 correspond to rows 1-31 in the matlab file. Since the data were recorded originally on 32 electrodes, 1 channel must be removed for this particular file. 1a. The answer to the units is more complicated. For all experiments, the voltage was amplified 20,000x, encoded as a 16-bit signal, usually spanning a range of 10 or 20 V. Thus a value of '1' would correspond to roughly 3.8 to 7.6 picovolts. 2. The spike clusters are unrelated to the number of channels, since the channels correspond to individual electrodes, and the spike clusters are defined by their unique patterns of depolarization measured across electrodes. Thus, one channel  may detect spikes from multiple neurons, which were separated as described in the data description and processing flowchart pdf. Best, Gautam   No publisher lizhaohui 2012-03-02T04:01:46Z Comment https://crcns.org/forum/using-datasets/527189381/634920534 Hi, 1. Channels 0-30 correspond to rows 1-31 in the matlab file. Since the data were recorded originally on 32 electrodes, 1 channel must be removed for this particular file. 1a. The answer to the units is more complicated. For all experiments, the voltage was amplified 20,000x, encoded as a 16-bit signal, usually spanning a range of 10 or 20 V. Thus a value of '1' would correspond to roughly 3.8 to 7.6 picovolts. 2. The spike clusters are unrelated to the number of channels, since the channels correspond to individual electrodes, and the spike clusters are defined by their unique patterns of depolarization measured across electrodes. Thus, one channel  may detect spikes from multiple neurons, which were separated as described in the data description and processing flowchart pdf. Best, Gautam No publisher synapse 2012-03-01T23:44:09Z Comment https://crcns.org/forum/using-datasets/250636693/142061104 Hi All,         I downloaded the eye-1 dataset and uncompressed all 4 archives. I'm not finding the /stimuli directory containing all the original clips. Can you help me out with this ? I can see the eye tracking data, saliency visualisation and also overlay of eye gaze on the stimuli though.   best, harish No publisher harish 2011-01-09T09:44:01Z Comment https://crcns.org/forum/using-datasets/295692235/175272522 Use scripts provided with the  hc-2 data set (file "crcns-hc2-scripts.zip" in the hc-2 downloads folder).  They should work with hc-1 because formats for hc-1, hc-2 and hc-3 are the same. No publisher jteeters 2014-03-26T18:15:25Z Comment https://crcns.org/forum/using-datasets/149748892/113920544 The documentation says that the MATLAB file EE.188_example.mat, in the EE0708 folder, contains the final data actually used for the main conclusions in the Nature 2008 paper it cites. But two versions of MATLAB say it cannot be loaded, file corrupt. (To be sure we downloaded and decompressed many times.) Two versions of octave say it cannot be loaded because zlib cannot decompress numerical value. To be honest, since it is only 3 megabytes, is there a way to get it as simple email attachment? That would be a lifesaver. Failing that, we could try to get the information by processing the clu and res files in the same EE188 folder which we get from decompressing the TAR. Those load fine in MATLAB, octave and even excel. But I note that clu seems to be missing for other trials we glanced at, like EE040 through EE048. More seriously, can we say we are using the same data that fujisawa and buzsaki used if we just merge all the spikes good which clu>1? Since I don't see a copy of that Anton m file mentioned in the documentation, I can't tell. I have a suspicion that we should be using the fet and spk file information as well, but not sure how: fet loads up nicely in excel at least.  Am puzzled why there are 13 or 14 clu files in each complete session but only 8 shanks. Also, are the neurons all cerebral cortex, not based on the 32 channels of original data from hippocampus? Or are the extra shanks hippocampus ???? Thanks for your patience. The MATLAB file is all we need urgently, but it would be really nice go know the rest. No publisher pwerbos 2016-05-15T21:25:38Z Comment https://crcns.org/forum/using-datasets/276820825/853042422 The sampling rate for gor01 is 32,552Hz not 20k Hz.  This is given at the top of document "crcns-hc3-processing-flowchart.pdf".  Thanks to Kamran Diba (responded by email). No publisher admin 2016-04-16T02:34:32Z Comment https://crcns.org/forum/using-datasets/601153055/531550029 Try the scripts supplied with the hc-2 data set.  They are in the file "crcns-hc2-scripts.zip" which is in the hc-2 downloads area.  The hc-1 and hc-2 data set have mostly (perhaps all) the same format.  Another option would be to get neuroscope running.  The data can be directly read and then viewed by neuroscope.     No publisher jteeters 2011-04-25T23:36:57Z Comment https://crcns.org/forum/using-datasets/901659658/535221765 Hello all, I have two questions regarding vim 2 dataset: What causes NaNs to be found in BOLD response voxels? I have to elaborate this question little bit more, so to follow my contemplation please take a look at this picture http://picpaste.com/NaN_values_ROI_voxels-tRU6masC.png (subject 1, first BOLD response), there you can see highlighted/masked ROI pixels in each 64x64 slice. White pixels correspond to mentioned NaNs, red ones are all ROI's indexes together. What bothers me most is that some ROI indexes point right to these NaNs. So, at first, I thought that there was some mismatch in pyTables library, therefore I tried to load the same data with h5py, Octave, even Matlab itself, but results were always the same. Am I doing something wrong or is this actually due to how fMRI data acquisition/processing works and everything is in fact ok? Well, there is offcourse no problem with getting rid of these NaNs using numpy.nan_to_num, but anyway I need to understand clearly to this discrepancy before any experiments. Maybe I am only uncertain and this is not a problem at all. Last thing, only a small remark that is not related to this question - indexing of "data" variable in example load code is swapped (vim 2 data description pdf, "how to get started" section: "v1lh_resp = data[:,v1lh_idx]" should be "v1lh_resp = data[v1lh_idx,:]").    My second question is simpler, is there any possibility of getting minimally preprocessed fMRI data for vim 2, like the gzipped NIFTI files attached to vim 1 data set? This could be really helpful for my experiments.   Please let me know what you think about it. Thank you in advance. Jan No publisher Zeno 2015-02-04T20:32:58Z Comment https://crcns.org/forum/using-datasets/6396666/836155699 Hello, I have two questions about the hc-2 data. Firstly, since the action potentials may be detected by different electrodes on one shank, how can I get the theta phase of a neuron? In other words, given a spike train of one neuron, the LFP signal of which channel should be employed to study the phase relation between the spikes and LFP? Secondly, the cells type can be separated by their autocorrelograms, waveforms and mean firing rates, but how can I get the spike waveform? By using the matlab script “Spk = LoadSpk('ec013.527.spk.1',8)”, for example, I got a 3d array Spk(Channel, Sample, Spike Number) array of 8*32*39659 elements. According to the “ec013.527.clu.1” file, the 966th spike is classified as cluster 12, but there are eight waveforms from eight electrodes, which waveform should be used? Whether the citation of largest amplitude can be employed to identify the correspondence? Additionally, in the description of the hc-2 data it states that the width of each spike is 32 samples with sample #15 being the trough of spike. But I found this does not always hold, why? In the text file of waveshape features for each spike (‘ec013.527.fet.1’ for example), there are 29 features, which is the spike width? One more thing, what is the appropriate epoch duration can be used to calculate the mean firing rate?   Thank you! No publisher caofei 2012-03-12T02:51:01Z Comment https://crcns.org/forum/using-datasets/6343405/425250364 I implement the Basis-restricted separable model in the paper "Identifying natural images from human brain activity" to convert Minimally preprocessed EPI data to estimated BOLD responses amplitude on the Sub1_Ses1-5 data. Actually, the deconvolution results have low correlation (low than 0.4) to the data in EstimatedResponse.mat for some voxel (about 40% in V1). This really hinder identification performance.  Would you provide the codes of Basis-restricted model?  This would be most helpful to my resent work.   Best, Dai.       No publisher todaizhang 2013-12-03T04:00:28Z Comment https://crcns.org/forum/using-datasets/62983964/440642068 Dear Diogo, Really sorry for the delayed reply. The problem has been taken care of and all the position data should be available now. Hope you'll have fun with the data Best, Adrien No publisher apeyrache 2016-03-10T04:44:12Z Comment https://crcns.org/forum/using-datasets/725145404/972007623 Probably that's lost tracking. In this case, you could detect when it's lost (like zeros or -1) and maybe use interpolation. No publisher RobScheffer 2015-04-24T23:06:30Z Comment https://crcns.org/forum/using-datasets/899614105/853711331 Hi Mary,  As I said in the other thread, it's up to you to come up with a set of categories into which to divide the stimuli. You could, for example, label all the images that have faces or people in them as one category, and all the images that don't have people as another category. You can label anything you like. And as you can perhaps see, that could be a lot of work. But it is beyond the scope of our data sharing to define all the possible analyses you might want to do with our data. Cheers, Mark No publisher mlescroart 2014-05-02T17:42:43Z Comment