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Emer Jones proslijedio/la je Tweet
New preprint from the lab: "Individual differences among deep neural network models." https://www.biorxiv.org/content/10.1101/2020.01.08.898288v1 … Work with
@KriegeskorteLab,@HannesMehrer, and Courtney Spoerer.#tweeprint below. 1/7Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emer Jones proslijedio/la je Tweet
Now out in PNAS! We combine MEG source reconstruction, RSA, and end-to-end deep neural network training to show that "Recurrence is required to capture the representational dynamics of the human visual system". https://www.pnas.org/content/early/2019/10/04/1905544116 …
#deeplearning#neuroscience#neuroimaginghttps://twitter.com/KriegeskorteLab/status/1181394819148591105 …
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Randi Starrfelt spoke about the Back of the Brain project: a large and rich dataset (64 patients, 32 tests). PCA stroke patients were recruited based on their lesions rather than their symptoms. https://www.researchgate.net/project/The-Back-of-the-Brain-BoB-project …pic.twitter.com/2V3CG2uu5d
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Finally, an explanation for why there are cases of pure alexics and prosopagnosics if the processes are bilateral with dominant and not exclusive areas: people lie on a spectrum of lateralisation and those with extreme lateralisation are those in the dissociation case studies
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She proposed an explanation for why word- selective areas are more in the left hemisphere and face- selective regions are in the right hemisphere: 1. Local connections between word recognition and language areas (left); 2. face areas separate from word (become right-lateralised)pic.twitter.com/GmtxChf2hj
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Marlene discussed three common principles underlying face and word recognition: face and word selective areas are differentially, not exclusively tuned.pic.twitter.com/b5z73dAhEM
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Convolutional neural nets are a promising line of research regarding a more mechanistic account of the ventral stream, but don't yet speak to the specialisation, segregation and topography of the brain that we see empiricallypic.twitter.com/EhkXbDKNgc
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Marlene Behrmann opened the workshop with a great talk, discussing that the large "area" or "where" focus of central visual system research falls short of understanding HOW the brain accomplishes word and face recognition.
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Great programme for today's workshop on the visual ventral stream at the
@mrccbu. Many thanks to@GraceRice44 and Randi Starrfelt for organising!pic.twitter.com/booh75JfBw
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Talk by
@matthiaskue on Deep Gaze 2's success with fixation prediction. Interesting that 4 model neurons account for most of DG2's accuracy (approx corresponding to object 'pop-out', text, faces, and 'geometry' respectively)#BernsteinConferencepic.twitter.com/W0RtYf7TqY
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Which network architecture is capable of capturing the dynamics of MEG data from different ventral stream regions? Recurrent architecture with bottom-up, top-down and lateral connections is necessary; ramping feedforward network is insufficient
@TimKietzmann#BernsteinConferencepic.twitter.com/lM9iiK5YHT
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Talk by
@Marie_E_Bellet: Using UNet to segment timeseries data (saccade detection). Implemeted in PyTorch at http://github.com/berenslab/uneye#BernsteinConferencepic.twitter.com/BPWYI6SsiX
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New fMRI dataset for end-to-end modelling presented by
@kateiyas. One subject, 30 episodes of Dr Who#BernsteinConferencepic.twitter.com/Vyt6X2mDKs
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Great line-up of talks at the 'Deep learning in computational neuroscience' workshop at the
#BernsteinConference!pic.twitter.com/ey80s1Otvp
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From
@gershbrain 's tutorial on approximate inference on the brain, looking at whether we can explain human probability errors & biases in terms of different approximations of intractable Bayesian inference#CCN2019pic.twitter.com/M1sUvUxNyP
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Fun fact: when asked for the last digit of their social security number, and then asked what year Gandhi was born in, the SSN digit influenced peoples' birth-year estimate (lower SSN digits -> earlier year). Crazy that a digit anchors a totally unrelated estimation.
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Emer Jones proslijedio/la je Tweet
Help me out twitterverse, which team chat/lab organisational tool do you use/recommend and why? Slack? Basecamp? Mattermost? Telegram? (please RT for visibility)
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Last post from
#dliss19! Was a great week.@alfcnz ended the summer school with a very brief but super important reminder!pic.twitter.com/kC979fmt9a
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Tutorial material (PyTorch) athttps://github.com/alxndrkalinin/angiodysplasia-segmentation …
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Fab overview of deep learning-based image segmentation by
@alxndrkalinin incl U-Net, a top-performing model. Tips: pre-training is always beneficial, even if not domain-specific (ImageNet!) or the pretrained model doesn't initially match dimensions (eg greyscale/RGB)#dliss19pic.twitter.com/NtPBjzgYaU
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