From 4dc21b0bfe921d502adf2fc197e4183e823534ae Mon Sep 17 00:00:00 2001 From: mbalazs98 <49280623+mbalazs98@users.noreply.github.com> Date: Mon, 9 Dec 2024 09:42:22 +0000 Subject: [PATCH] Made the requested fixes in the DCLS section. (#153) * Delete paper/sections/delays/0-5.png * Delete paper/sections/delays/Confuse_dcls.png * Add files via upload * Update Delays.md --- paper/sections/delays/0-5.png | Bin 88528 -> 148811 bytes paper/sections/delays/Confuse_dcls.png | Bin 45340 -> 191621 bytes paper/sections/delays/Delays.md | 5 +++-- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/paper/sections/delays/0-5.png b/paper/sections/delays/0-5.png index e159555a227a6f7d770354bdc855adb5090ea9a7..ebfb50eef8bfa2b027d608df0eb143d0e42fc7ca 100644 GIT binary patch literal 148811 zcmd432UL^k);9bgBb*sj1{(?jGoa!~6G1veM~Vst5d@?w(wh)^cQ|UKjs+r;fXV>U zn-D^P;7E}cdI>EW2qCmc4L#resAuMUYn^|+@4wFZ=9sly6EZ2!bKm>k``XvO_I_{$ zski$(q3<9F+I{hY_EiYt%>qv&{>B4d`5luv3;y%%?F-jY5F{%B|KkeGK=^|f5Butv z`x<*W`Uc$aaex8>0%To0-B9*7Zac_&`8cJ_X$V2k570&Jb0&ebKhq}JUzaEhF#m~<9r%kMlZfw{OZ0Gqhup@*tRRxr7;rQkmmL{WA zfyfSR4@wKknp_U92A2yzo_k_+>)2DG;`-@xT%TWp23DVRe|h?wF_Qc9(_fLQyFWiW zt43G+^1qpX^&2ZqN4SgD9UUEMbr1Fl2*|6d9!^hB4;$goXg7yLSk?1O-G+@5Z7KH7 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[](../research/Solving_problem_with_delay_learning.ipynb) +- [](../research/Quick_Start_Delay_DCLS.ipynb) ``` This section introduces a simple method to solve the sound localization problem with only learnable delays. Additionally, it discusses a method that learns both weights and delays, introduced in {cite:p}`hammouamri2024learning`. @@ -117,12 +118,12 @@ Similar to the other cases, the DCLS architecture is trained for classification ```{figure} sections/delays/Confuse_dcls.png :label: DelaySpikeHistograms2 :width: 100% -The same performance metrics for DCLS +Distributions of true and estimated IPD values, along with their corresponding confusion matrices for the training and test sets, obtained using the DCLS algorithm. ``` Learning synaptic delays with weights enables the visualization of the 'receptive field' of postsynaptic neurons, as illustrated in [](#rf). Five randomly chosen neurons from the hidden layer are plotted, revealing clear spatiotemporal separation of excitation and inhibition. ```{figure} sections/delays/0-5.png :label: rf :width: 100% -Receptive fields of 5 randomly chosen postsynaptic neurons. The x-axis represents the presynaptic neuron index, while the y-axis displays the learned delay value. Colors indicate the sign of the weight (excitation or inhibition), with transparency denoting magnitude. +Receptive fields of 5 randomly chosen neurons in the hidden layer. The x-axis represents the presynaptic neuron index, while the y-axis displays the learned delay value. Colors indicate the sign of the weight (excitation or inhibition), with transparency denoting magnitude. Excitation and inhibition appear to be spatiotemporally separated. ```