Mitchell B Slapik

According to dynamical systems, psychiatric disorders can be seen as energy landscapes with peaks and valleys. OCD has deep valleys where neural activity gets stuck, while schizophrenia has shallow valleys that let neural activity roam too freely, connecting unrelated ideas.

1/ New preprint with @dyamins + team! Ventral visual representations within areas evolve over the course of the response along the same hierarchical complexity axis that distinguishes the visual areas, potentially driven by local recurrence. biorxiv.org/content/10.648…

26 anos de In the Mood for Love.

What makes computation important to understanding life and minds is not the Turing machine, but the principle of causal insulation. Computers create their own, self contained world, fully governed by rules that are independent from the dynamics of the substrate.

A técnica pra inserir 'movimento' na pintura.

New paper!! On health justice, resource allocation, and age in suicide risk assessment. We argue that, in resource-limited healthcare systems, appropriately developed risk tools may support fairer allocation of clinical resources, especially for groups at highest risk.

Neural networks might speak English, but they think in shapes. Understanding their rich *neural geometry* is key to understanding how they work – and to debugging and controlling them with precision. Starting today, we’re releasing a series of posts on this research agenda. 🧵

🧵1/ Our new study on AI and physician reasoning just came out in @ScienceMagazine. As co-senior author, I'm excited about our findings, and I do think AI will reshape medicine. But after seeing some of the discussions, I'm also worried about how our findings may be misinterpreted.

My bet: in the near future, 80%⬆️ of CS research will be done by AI in collaboration with humans. However, today's research ecosystem is still built around the human, not the AI scientist. For example, the 8-page paper PDF is a lossy compression of months of branching exploration into a linear story, optimized for a human reviewer to skim in 30 minutes. It hides two structural taxes: 📖 Storytelling Tax — failures, rejected hypotheses, and dead ends get stripped. On RE-Bench (24,008 runs, 21 frontier models), failed runs = 90.2% of total compute cost, with a 113× median failed-to-success token ratio. Every lab independently rediscovers the same dead ends. 🔧 Engineering Tax — the gap between reviewer-sufficient prose and agent-sufficient spec. Across 8,921 PaperBench requirements (23 ICML'24 papers), only 45.4% are fully specified in the PDF. The rest is tacit lab knowledge. Tolerable when readers were human. Critical now that agents read, reproduce, and extend. We propose ARA: the Agent-Native Research Artifact — replace the narrative PDF with an agent-executable package, in 4 layers: 🧠 structured scientific logic ⚙️ executable code w/ full specs 🌳 exploration graph (every failure preserved) 📊 evidence grounding every claim

What if the model didn’t just use a computer, but actually was the computer? Meta AI introduces "Neural Computer", a model where computation, memory, and I/O are all inside one learned system. Their early prototype learns from screen recordings of terminals and desktops, and it can already imitate some basic computer behavior like rendering interfaces and responding to clicks or commands. But it still breaks on slightly harder tasks like reliable reasoning, stable memory, and reusable skills.

@paraschopra Very reasonable take on a polarizing topic

My new paper on "AI Consciousness". I argue that to talk about that we need to have *validated* theories of human consciousness (and we're very far from that) Accepted at AAAI Symposium 2026 on Machine Consciousness. Feedback and discussion welcome.

🚨 New Paper Can AI models be conscious? We argue that answering this question requires us to have a validated theory of human consciousness first and without that, the concept “ai consciousness” is not well grounded. Accepted at AAAI Symposium 2026 lossfunk.com/papers/ai-cons… 🧵

Across versions of ChatGPT, responses to psychotic prompts were frequently inappropriate or partially appropriate, raising safety concerns for users at risk for #psychosis. ja.ma/4mb41Ae

Psychiatry is at a crossroads. What if much of what we diagnose as disorder is actually a rational response to an unstable world shaped by climate change, conflict, and uncertainty? Our new editorial 👇 globalpsychiatryarchives.com/index.php/gpa/…

Postpartum depression is often missed, & not all cases look the same. A new study in @BMJMentalHealth shows AI can help: by analysing clinical notes, researchers identified 30% more PPD cases beyond diagnosis codes alone, revealing distinct subtypes with different needs. Earlier detection = better, more personalised care Link: mentalhealth.bmj.com/content/29/1/e… Authors: Prakash Adekkanattu, Veer Vekaria, Yiye Zhang, Braja Gopal Patra, Priscilla Liang, Marianne Sharko, Natalie Benda, Meghan Reading Turchioe, Andrea Temkin- Yu, Alison Hermann, Jyotishman Pathak @Columbia

Our latest in @molpsychiatry: In patients with anxiety+depression, targeting a novel “anxiosomatic” circuit in dmPFC outperforms standard dlPFC target for anxiety, and equally effective for depression. nature.com/articles/s4138…

Among individuals with severe, treatment-resistant #Schizophrenia, #dementia was common and showed a distinct clinical and genetic profile not explained by #Alzheimer disease, cardiovascular risk, or medication effects. ja.ma/4uWAsGG

Effectiveness of psychotherapy for 12 disorders. • 0.2 = small • 0.5 = medium • 0.8 = large [Link below.]

The Orange Cat Brain Atlas is here. 🧠🐈 Today, we published the first comprehensive cellular map of the orange cat brain. The new atlas reveals a single, specialized neuron responsible for behaviors like staring at walls, knocking objects off tables, and the 3am "zoomies."

The missing half of the neural network–brain comparison For a decade, the standard benchmark for artificial neural networks as models of the brain has been forward predictivity: learn a linear mapping from model activations to neural recordings and measure explained variance. Top models of the macaque inferior temporal (IT) cortex—central to object recognition—have plateaued near 50% regardless of architecture. Muzellec and Kar argue this plateau hides something important. Two models can score identically on forward predictivity while relying on fundamentally different internal strategies. One may have many units tightly coupled to IT responses; the other may reach the same score with a smaller aligned subset while carrying a large pool of biologically inaccessible dimensions. To expose this, they introduce reverse predictivity: instead of asking how well model features predict neurons, they ask how well IT neurons predict individual model units. A truly brain-like model should be bidirectionally predictable—just as two monkeys' IT populations predict each other symmetrically, which the authors confirm as their empirical baseline. Across 39 architectures—CNNs, transformers, self-supervised and robust models—reverse predictivity is consistently lower than forward predictivity and the two metrics are uncorrelated. Strikingly, higher ImageNet accuracy predicts lower reverse predictivity. Adversarial training helps; higher dimensionality hurts. The "common" units identified this way predict primate behavior more consistently across species and models than the "unique" ones inaccessible from neural activity. For AI in drug discovery, neurotechnology, or computational biology, this has a direct implication: forward accuracy alone does not guarantee that a model's internal representations are embedded in the biological system it claims to describe. When those representations guide mechanistic interpretations or experimental decisions, the mismatch can mislead. Paper: Muzellec et al., Nature Machine Intelligence (2026) | nature.com/articles/s4225…

"Before we name, we touch. We propose that the roots of language lie not in abstract, amodal symbols but in early bodily experience." Our Opinion paper is out in TICS. Congrats Luca Sergey Rinaldi !! sciencedirect.com/science/articl…