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Andreas Tolias Lab @ Stanford University
@AToliasLab
to understand intelligence and develop technologies by combining neuroscience and AI
Palo Alto, CA
Joined May 2017
Posts
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    Does the concept of cortical columns extend to higher-level primate cortex? Using #DeepLearning & physiology, we found that V4 neurons cluster in columns & form functional groups biorxiv.org/content/10.110… Led by @KonstantinWille @kelli_restivo w/ @sinzlab @kfrankelab @alxecker 🧵
    GIF
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    Functional connectomics to mechanistically dissect cortical algorithms. We align 2P calcium with EM data using CNMF framework to recover cell body and dendritic activity in EM volume. Thanks @IARPAnews for funding biorxiv.org/content/10.110…
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    After 7 years, thrilled to finally share our #MICrONS functional connectomics results! We recorded activity from ~75K neurons in visual cortex in a single mouse, then mapped its wiring using electron microscopy. To systematically characterize neuron function, we built the first
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    Multimodal census-atlas of cell types of mouse and primate motor cortex including Patch-Seq data. Massive effort from biccn.org labs of @NIH BRAIN Initiative. Proud to be part of it with @CellTypist @FedericoScala7 @hippopedoid @sandberglab disq.us/t/3seoncm
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    Last day @bcm_neurosci after an incredible 15+ yrs. Immensely thankful to our brilliant students, postdocs, staff, collaborators, and colleagues. Next up: @Stanford! Thrilled for new adventures and collaborations in the Bay Area as we unlock the secrets of intelligence!
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    With @AllenInstitute and @SebastianSeung we release our MICrONS @IARPAnews phase 1 functional connectomics data microns-explorer.org. Movie shows 2P calcium and EM data aligned together using EASE with cell body and dendritic activity in EM volume. biorxiv.org/content/10.110….
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    Announcing the MICrONs Explorer: A virtual observatory of the cortex 👉 microns-explorer.org This data portal includes #openscience #electronmicroscopy based reconstructions of cortical circuitry from mouse visual cortex.
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    #InceptionLoops combine large scale recordings with #DeepLearning to reveal a neuron's feature selectivity, such as most exciting inputs. Optimal stimuli for mouse V1 are not Gabors. nature.com/articles/s4159… in #NatureNeuroscience Lead by @eywalker @sinzlab together with @ecobost
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    Think government ROI is low? In the ‘60s, #NIH-funded researchers Hubel & Wiesel studied how cats’ neurons process visual info—seemed trivial, right? Yet their work led to convolutional neural networks like Hinton’s AlexNet, sparking the AI revolution that’s reshaping the global
    The ROI for most government expenditures is very low.
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    1/4 Excited to share our latest work on feedback (FB). nature.com/articles/s4146…. There are abundant FB connections between cortical areas. To understand its function and its circuit level mechanisms we need to discover the FB cell type writing rules.
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    Check out our new #BCI shorturl.at/nKwnH. Subdural, flexible μECoG device features 65,536 channels, with bi-directional wireless communication and power enabling decoding the primate brain with high spatiotemporal resolution. Compare specs to other #BCIs
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    Canonical circuits are thought to makeup the #cortex. But what is canonical about them? Lead by @FedericoScala7 and Dmitry Kobak we show that not all cell types are present across areas and propose that specific circuit motifs are used as building blocks disq.us/t/3i8a85j
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    Proud to release our MICrONS multi-area data microns-explorer.org, the largest functional connectome to date, containing 200,000 cells, 75,000 neurons with functional imaging, and 523 million synapses. Excited to see the discoveries from this dataset.
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    1/2 As suggested by our huge Patch-seq effort lead by @FedericoScala7 and @hippopedoid published today in @nature the tree of cortical cell types seems more like a banana tree with a few large leaves, rather than an olive tree with many small ones disq.us/t/3sufdoi
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    Finding optimal stimuli for neurons has long been central for understanding information processing in the brain. This is hard because the search space is high-dimensional and sensory information processing is fundamentally nonlinear.Check out our preprint disq.us/t/39zixxq