Michael P. Brenner

Coupling differential adhesion to cell signaling avoids kinetic traps to yield robust multicellular self-organization

This study shows that combining differential adhesion with cell signaling avoids kinetic traps, enabling robust multicellular self-organization in synthetic systems.

Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins

This paper introduces a computational framework for designing intrinsically disordered proteins by inverting molecular simulations to create sequence-ensemble-function relationships.

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

The paper uses a deep speech-to-text model to predict neural responses during spontaneous conversations, revealing a distributed cortical hierarchy for speech and language processing.

Correspondence between the layered structure of deep language models and temporal structure of natural language processing in the human brain

This study finds a correspondence between the layered structure of deep language models and the temporal structure of neural activity during language processing in the human brain.

Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language in the human brain

The paper shows that brain embeddings from the IFG share geometric structure with DLM contextual embeddings, enabling zero-shot encoding and decoding of unseen words.

Self-assembly based post-translational protein oscillators

This paper designs two post-translational protein oscillators inspired by the KaiABC system, using reversible binding and phosphorylation/dephosphorylation reactions.

Thinking ahead: prediction in context as a keystone of language in humans and machines

In nine epilepsy patients, cortical activity carries next-word information up to 800 ms before onset, tracks GPT-2 surprise after onset, and fits GPT-2 contextual embeddings better than static ones.

Programming cell growth into different cluster shapes using diffusible signals

This paper develops a mathematical model to study how cells can use diffusible signals to grow into clusters with different shapes, identifying rules for generating rod-like structures and multiple protrusions.