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predictive coding

3 captures, most recent first.

@spisaktamas

Predictive Neuroscience Lab @spisaktamas The brain's "default mode" and "action mode" networks are two sides of the same attractor. Encoding a macro-scale Bayesian prior that biases processing toward internal or external drive. [Figure: fMRI signals → score matching → FEP-ANN attractor network → energy landscape diagram with numbered attractor basins (1-6) mapped to brain renderings showing Default Mode/Action Mode network regions (aPFC, pMFG, TPJ, IFG, a-mIns, pmCing, PCC/Prec, SMA, dACC, mPFC, Mid Thal) colored green/magenta; six small paired brain images labeled μ1–μ6 correspond to the six attractor basins on the energy landscape. Citation: Englert et al., 2024, Spisak & Friston 2...] 1:44 PM · Apr 28, 2026 · 3,357 Views
Note from Claude Sonnet 5

A neuroscience research tweet on free-energy-principle (FEP) modeling of the brain's default-mode/action-mode networks as attractor states in an energy landscape, derived via score-matching on fMRI data and an ANN. Relevant to Nathan's brain_graph_1 project (biologically-inspired RL agent using connectome priors) — this kind of attractor/energy-landscape framing of large-scale brain network dynamics could inform future architecture choices.

neurosciencefree energy principledefault mode networkpredictive codingfmriattractor networksbrain_graph_1

Surya Ganguli @SuryaGanguli

Surya Ganguli @SuryaGanguli · Nov 13 Our new paper on large scale holographic read-write experiments observing and controlling thousands of neurons in mouse visual cortex reveals a new functional cell-type that detects even moderate levels of excess activity (just 50 extra neurons firing) then inhibits top down cortical inputs. This cell type is a subclass of somatostatin neurons, whose dysfunction is implicated in schizophrenia. This suggests an intriguing hypothesis for the origins hallucinations in schizophrenia: the breakdown of this highly sensitive cortical gate allows top down inputs to enter visual cortex that should not, creating hallucinations. This work was expertly lead by @ADrinnenberg w/ @allanraventos and @Alex_Attinger on data analysis and theory. Another fun collab w/ @KarlDeisseroth! For more see: biorxiv.org/content/10.110... And also this excellent thread: x.com/ADrinnenberg/s...
Note from Claude Sonnet 5

A tweet by Stanford neuroscientist Surya Ganguli summarizing a new paper (with Karl Deisseroth's lab) on holographic read-write experiments in mouse visual cortex, identifying a somatostatin-neuron subtype that gates top-down cortical input and proposing a mechanistic hypothesis for schizophrenia hallucinations. Relevant to Nathan's brain_graph_1 project (top-down/bottom-up cortical gating, predictive-coding-adjacent architecture).

neurosciencevisual cortexsomatostatin neuronsschizophreniakarl deisserothsurya gangulipredictive codingbrain_graph_1

davinci @leothecurious

— quoting Tahereh Toosi (@taherehtoosi)

davinci @leothecurious · Oct 25 predictive coding doesn't merely serve to update parameters via local credit assignment but doubles as am algorithm for inference-to-best-explanation based on observed features (bottom-up signal) and learned priors (top-down signal). vision models are bound to evolve into bidirectional networks with feedfoward and feedback computational graphs. not to mention the self-attention-like role of lateral connectivity as well. the implications will be manifold. > QUOTED: Tahereh Toosi @taherehtoosi · Oct 24 > Replying to @taherehtoosi > Theory: feedback errors, under certain conditions, approximate the steepest ascent toward naturalistic patterns (the score function from generative models). These errors act like a... > [Diagram: two-panel figure comparing "Pattern recognition / Adversarially robust classifiers" (gradient of loss w.r.t. input, ∇L_x(x,y)) against "Pattern generation / Score-based generative models" (gradient of log-density, ∇log p_θ(x)), plus a 3D loss-landscape surface with a red dashed arrow labeled ∇log p(x) climbing toward a peak]
Note from Claude Sonnet 5

A neuroscience/ML Twitter thread on predictive coding as a unifying theory linking cortical feedback connectivity to bidirectional (feedforward+feedback) computational graphs and self-attention-like lateral connectivity, with a connection to score-based generative models. Relevant to Nathan's brain_graph_1 project, which uses predictive-coding-adjacent architectures and biological connectome priors.

predictive codingneurosciencemachine learningvision modelsgenerative modelsself-attentionbrain_graph_1