brain_graph_1

A terminal screenshot with garbled/overlapping text rendering (two overlapping renders of what appears to be the same markdown table, likely a scroll/redraw glitch), showing a parameter-count breakdown for a "sharded macrocolumn cortex" neural architecture — matches the brain_graph_1 project's known architecture (RWKV nodes, macrocolumns, basal ganglia/hippocampus/grid components). Text is partially garbled by rendering overlap, not genuine content loss.

Continuation of the same ICM (Intrinsic Curiosity Module) thread — explains the intuition that the inverse dynamics model (predicting the action taken from consecutive states) forces the feature encoder to discard action-irrelevant noise (like windblown leaves), so the forward model's prediction error in that cleaned feature space becomes a principled curiosity/novelty signal. Same relevance as the previous screenshot: architecture pattern applicable to brain_graph_1's intrinsic-motivation/curiosity design.

A tweet highlighting the classic Intrinsic Curiosity Module (ICM) paper (Pathak et al., "Curiosity-driven Exploration by Self-supervised Prediction") as an early example of a JEPA-like (joint embedding predictive architecture) approach trained end-to-end with RL — predicting in learned feature space rather than raw pixels to generate intrinsic/curiosity reward. Directly relevant to Nathan's brain_graph_1 project, which uses PVLV dopamine and curiosity-style intrinsic motivation signals; ICM's forward/inverse-model feature-space prediction is a foundational architecture pattern for that kind of RL exploration design.

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.

A technical thread on Multi-head Latent Attention (MLA) architecture experiments — scaling attention heads without diminishing ICL returns, and using truncated SVD on full-rank weights (analogous to quantization-aware training) to recover in-context-learning capability with less memorization. Directly relevant to the brain_graph_1 "KDA-MLA attention at the ends only" architecture plan noted in project memory.

Albert Gu (Mamba/SSM researcher) comments on a scaling-laws paper/thread about looping transformer blocks with an SSM-style gate applied along the residual stream, treating it as a dynamical-systems framing for how much recurrence to use at a given FLOP budget. Directly relevant to the brain_graph_1 encode/iterate-in-latent/decode architecture and its "virtualizing width via looping" primitive — a scaling-law reference for how many DEQ/loop iterations to use at different compute scales.

A research-methodology tip from ML researcher Jakob Foerster about debugging strategy: when stuck in an undiagnosable failure state, simplify or revert to a known-working baseline rather than continuing to iterate blind. Practically relevant advice for Nathan's own debugging-heavy brain_graph_1 project (currently in a mid-debug phase per project memory, with a training plateau and multiple degraded signal paths under investigation).

Neuroscience explainer tweet about the rosehip neuron, a human-specific inhibitory interneuron type in cortical Layer 1 that regulates Layer 3 pyramidal neurons. Potentially relevant background reading for Nathan's brain_graph_1 project (connectome-based RL agent with cortical/subcortical structure), as species-specific cell types like this bear on questions of what's essential vs. incidental in modeling human-like cognition.

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).

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.

A critique/technical exchange about the "Hierarchical Reasoning Model" (HRM), a brain-inspired architecture (high-level slow / low-level fast loops, cross-frequency coupling analogy) that beats chain-of-thought LLM baselines on ARC-AGI, Sudoku, and maze benchmarks with small-sample direct prediction. Directly relevant to Nathan's brain_graph_1 project, which similarly uses brain-inspired hierarchical/recurrent architecture; the readout-halting critique (representational collapse in the q_head) is a concrete design pitfall to be aware of.