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theoretical computer science compressed into 10 brilliant minutes...https://www.youtube.com/watch?v=YX40hbAHx3s …
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New paper by Tovar et al. from Alexander Maier's lab decoding grating orientation and stimulated eye as a function of V1 layer of origin, time past stimulus onset, and repetition--constraints for understanding the computations in the cortical microcircuit. https://www.biorxiv.org/content/10.1101/753368v1.full.pdf …pic.twitter.com/wI5dYnbhql
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Great great-debate teams...
#NeurIPS2019pic.twitter.com/mG7MS94MzV
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Open review: "Can parameter-free associative lateral connectivity boost generalization performance of CNNs?" about a new paper by Montobbio et al. https://nikokriegeskorte.org/2019/12/04/can-parameter-free-associative-lateral-connectivity-boost-generalization-performance-of-cnns/ …pic.twitter.com/z2SbQy24Yc
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do share. would be good to get a recent take on all this from someone who has studied it properly. i found this year's ICML tutorial on population-based search inspiring...https://www.youtube.com/watch?v=g6HiuEnbwJE …
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New paper: Recurrence is required to capture the representational dynamics of the human visual system by
@TimKietzmann et al. https://www.pnas.org/content/early/2019/10/04/1905544116 …pic.twitter.com/dbrAIsL3no
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Can we incentivise early (and continual) sharing of data, stimuli, tasks, analyses, and models by giving credit through authorship? For example, sharers could define parameters (eg a time period) within which they expect to be offered coauthorship.pic.twitter.com/buS4qxzXRn
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Debate with Rosalyn Moran, Karl Friston, and Jeff Beck
#ccn19pic.twitter.com/aA3wnNfVJJ
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Jeff Beck: the Free Energy Principle is a framework, not a (falsifiable) theory, providing limited constraints on model. (
@gershbrain and@haefnerlab cited on slide)#ccn19@CogCompNeuropic.twitter.com/2bgtXxb2TJ
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Jeff Beck in his opening statement at
#ccn19: FEP is not a theorypic.twitter.com/kPnxIsblB8
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Karl Friston delivering his opening statement...
#ccn19pic.twitter.com/DkainNkEhz
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Rosalyn Moran opening the
#ccn19 Challenges and Controversies session on the Free Energy Principlepic.twitter.com/Br1uH46tao
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Beyond model-free RPE, dopamine in VTA and NAc reflects value derived from a model of task structure.
#ccn19@CogCompNeuro https://ccneuro.org/2019/proceedings/0000013.pdf …pic.twitter.com/AcPf2PKfI1
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A human EEG signature of urgency in perceptual decisions as reflected in collapsing decision bounds
#ccn19@CogCompNeuro https://ccneuro.org/2019/proceedings/0000009.pdf …pic.twitter.com/xj8GRgj2TV
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Roshan Cools in her keynote argues that neuromodulators adjust cognitive tradeoffs to situational requirements, in particular frontal and striatal dopamine levels may control the tradeoff between cognitive stability and flexibility.
#ccn19@CogCompNeuropic.twitter.com/pszs2R03dS
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Optimizing a neural net model for odor classification can help us understand why flies and mice evolved the same particular architecture and even quantitative aspects of connectivity.
#ccn19@CogCompNeuro https://ccneuro.org/2019/proceedings/0000005.pdf …pic.twitter.com/hxRD5R8FgB
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Learning divisive normalization weights in a convolutional model of V1 neural responses to natural images suggests normalization among similar features. The model provides a nice compromise between interpretability and predictive power between classical LN and DNN models.
#ccn19pic.twitter.com/bC8QaTYzxQ
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Great keynote by
@behrenstimb on abstract representations of state transition graphs including those underlying 2d environments at#ccn19@CogCompNeuropic.twitter.com/aN9KMyi77N
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Six ancient innate abstract systems of core knowledge according to Liz Spelke at
#ccneuro2019#ccn19pic.twitter.com/Hs0vQPxwXy
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