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Posts by Torben Ott

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The belief that artificial consciousness is coming crucially depends on the assumption that consciousness is a computation.

In this new paper, we'll try to convince you that consciousness cannot be a computation

w Albert Gidon and M.Larkum

www.frontiersin.org/journals/neu...

#neuroAI #consci

10 months ago 84 21 7 3
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Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex High-resolution neuroimaging reveals stimulus-specific predictions sent from hippocampus to the neocortex during perception.

Our study using layer fMRI to study the direction of communication between the hippocampus and cortex during perceptual predictions is finally out in Science Advances! Predicted-but-omitted shapes are represented in CA2/3 and correlate specifically with deep layers of PHC, suggesting feedback. 🧠🟦

10 months ago 166 53 4 1
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Compositional pretraining improves computational efficiency and matches animal behaviour on complex tasks - Nature Machine Intelligence Hocker et al. demonstrate a method for training recurrent neural networks, which they call ‘kindergarten curriculum learning’, involving pretraining on simple cognitive tasks to improve learning effic...

www.nature.com/articles/s42...

11 months ago 11 9 0 0
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Constructing future behavior in the hippocampal formation through composition and replay - Nature Neuroscience A model of compositional state spaces in the hippocampus shows latent learning and rapid generalization. Bakermans et al. show that this model predicts the emergence of place responses in replay.

Happy to share the latest version of our work on compositional maps in hippocampus with Jo Warren, @jcrwhittington.bsky.social, @behrenstimb.bsky.social. We propose hippocampus constructs maps from cortical building blocks in replay – now with empirical support! www.nature.com/articles/s41... 1/9

1 year ago 80 24 1 4
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Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model The appropriate form of the drift diffusion model depends on how one wishes to reason about their target with the model. If the goal is to parsimoniously explain the speed-accuracy tradeoff, the appr...

super excited that the first paper from my PhD is now out! we develop a "philosophical toolkit" for computational cognitive modeling & use it to conceptually re-analyze a long-standing debate about evidence accumulation models of decision making 🧠📈 onlinelibrary.wiley.com/doi/full/10....

11 months ago 95 35 2 3
A schematic of our method. On the left are shown Bayesian inference (visualized using Bayes’ rule and a portrait of the Reverend Bayes) and neural networks (visualized as a weight matrix). Then, an arrow labeled “meta-learning” combines Bayesian inference and neural networks into a “prior-trained neural network”, described as a neural network that has the priors of a Bayesian model – visualized as the same portrait of Reverend Bayes but made out of numbers. Finally, an arrow labeled “learning” goes from the prior-trained neural network to two examples of what it can learn: formal languages (visualized with a finite-state automaton) and aspects of English syntax (visualized with a parse tree for the sentence “colorless green ideas sleep furiously”).

A schematic of our method. On the left are shown Bayesian inference (visualized using Bayes’ rule and a portrait of the Reverend Bayes) and neural networks (visualized as a weight matrix). Then, an arrow labeled “meta-learning” combines Bayesian inference and neural networks into a “prior-trained neural network”, described as a neural network that has the priors of a Bayesian model – visualized as the same portrait of Reverend Bayes but made out of numbers. Finally, an arrow labeled “learning” goes from the prior-trained neural network to two examples of what it can learn: formal languages (visualized with a finite-state automaton) and aspects of English syntax (visualized with a parse tree for the sentence “colorless green ideas sleep furiously”).

🤖🧠 Paper out in Nature Communications! 🧠🤖

Bayesian models can learn rapidly. Neural networks can handle messy, naturalistic data. How can we combine these strengths?

Our answer: Use meta-learning to distill Bayesian priors into a neural network!

www.nature.com/articles/s41...

1/n

11 months ago 155 44 4 1
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Prefrontal encoding of an internal model for emotional inference Nature - Neurons in the rodent dorsomedial prefrontal cortex encode a flexible internal model of emotion by linking directly experienced and inferred associations with aversive experiences.

I’m excited to share a new paper from the lab. The study led by Xiaowei Gu reveals how the mPFC encodes complex emotional memories, using an internal model to infer emotional associations and memories via projections to the amygdala. a🧵(1/8)

rdcu.be/el19t

11 months ago 106 27 10 1
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Systems consolidation reorganizes hippocampal engram circuitry Nature - A study shows that loss of memory precision associated with systems consolidation can be explained by neurogenesis-dependent reorganization of engram circuitry within the hippocampus over...

Sharing a new paper from the lab. This paper, led by Sangyoon Ko, represents a merging of two longstanding research themes in the lab-- adult neurogenesis and systems consolidation.

rdcu.be/el18q

A short thread follows for those interested.

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11 months ago 220 97 15 6
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🐦 Deadline tomorrow!

Working in the decision-making field? Then you should definitely apply for the "Symposium on the Biology of Decision-Making" (SBDM25) in Lyon 🎥

Speakers include:
@anna-beyeler.bsky.social
@juliasliwa.bsky.social
@torbenott.bsky.social
@faurelab.bsky.social

sbdm2025.github.io

11 months ago 4 2 0 0
Barron Group | mrcbndu Our group investigates how cells and circuits in the brain work together to perform computations that support memory. Using a wide range of technical approaches, we investigate how these computations ...

*** Come join us !! We are recruiting !! ***
How does the brain generate beliefs that go beyond direct experience? How do these mechanisms go awry in psychosis?

The Barron lab tinyurl.com/bdf4drjf @MRCBNDU @NDCNOxford are recruiting a new postdoc tinyurl.com/ut4vbwt4 to work in mouse models

11 months ago 30 31 2 5
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A prospective code for value in the serotonin system - Nature Merging ideas from reinforcement learning theory with recent insights into the filtering properties of the dorsal raphe nucleus, a unifying perspective is found explaining why serotonin neurons are ac...

I'm excited to share that the last chapter of my PhD thesis is now published in Nature! 🍾

What drives serotonin neurons? We think it's the expectation of future reward and --- critically --- how fast this expectation is increasing. 📈

doi.org/10.1038/s415...

1/6

1 year ago 134 46 9 3
Illustration for an article titled "Mental health in academia is too often a forgotten footnote. That needs to change" featuring a person overwhelmed by a swirl of papers and books.

Illustration for an article titled "Mental health in academia is too often a forgotten footnote. That needs to change" featuring a person overwhelmed by a swirl of papers and books.

"Academics tend to be averse to discussing mental health openly, and higher education’s mental health safety net is patchy—a forgotten footnote that all too often fails its students." #MentalHealthAwarenessMonth scim.ag/4m61iaU

11 months ago 126 40 2 11
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Impaired motor-to-sensory transformation mediates auditory hallucinations Can the absence of inhibition lead to auditory hallucinations? This study shows that the impairment of motor-based sensory predictions causes erroneous monitoring of imprecise internal auditory repres...

Super interesting work with a computational model of how auditory hallucinations may arise:
journals.plos.org/plosbiology/...
#neuroscience #compneuro

11 months ago 57 20 0 0

I'd also be curious, obwohl ich Deutsch-Muttersprachler bin höre ich fast nur englischsprachige Podcasts. Ich höre nur ein paar Psychologie-Sachen von Stefanie Stahl und Lukas Klaschinski, zB "So bin ich eben!"

11 months ago 0 0 0 0
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🎓 Join us for the International Postdoc–Junior PI Retreat!
📍 Neupitz, near Berlin
🗓️ Sept 1–3, 2025

Connect, collaborate & recharge with peers in nature.
🆓 Travel, stay & meals funded.
🔗 Apply by May 31: torbenottlab.org/postdoc-youn...
With @jelenabrasanac.bsky.social

11 months ago 2 1 0 1
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Do you want to analyse multidimensional large scale foraging data?

I have a postdoc opening. Postdoc will be part of an HSFP grant ‘”Neurometabolic mechanisms underlying social foraging” with Robert Froemke (NYU) and Jee Hyun Choi (KIST).

Details: stellen.uni-konstanz.de/jobposting/8...

1 year ago 11 8 1 4

Re-posting is appreciated: We have a fully funded PhD position in CMC lab @cmc-lab.bsky.social (at @tudresden_de). You can use forms.gle/qiAv5NZ871kv... to send your application and find more information. Deadline is April 30. Find more about CMC lab: cmclab.org and email me if you have questions.

1 year ago 77 89 3 8
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romotional flyer for the "International Postdoc – Junior PI Retreat," taking place 1–3 September 2025 in Neupitz near Berlin. The flyer features a four-panel layout with images of sticky notes, a lakeside view, an outdoor dining setup, and a writing desk. Central text reads “Connect with peers. Build collaborations.” Below, the date, location, and a QR code for registration are shown. (Design: Canva | Images: Unsplash)

romotional flyer for the "International Postdoc – Junior PI Retreat," taking place 1–3 September 2025 in Neupitz near Berlin. The flyer features a four-panel layout with images of sticky notes, a lakeside view, an outdoor dining setup, and a writing desk. Central text reads “Connect with peers. Build collaborations.” Below, the date, location, and a QR code for registration are shown. (Design: Canva | Images: Unsplash)

🎓 Join us for the International Postdoc–Junior PI Retreat!
📍 Neupitz, near Berlin
🗓️ Sept 1–3, 2025

Connect, collaborate & recharge with peers in nature.
🆓 Travel, stay & meals funded.
🔗 Apply by May 31: torbenottlab.org/postdoc-youn...

11 months ago 3 1 0 0