Cognition and behaviour arise from computations in neural circuits, which can differ in their readiness for recruitment or in the computations and behavioural outputs that they generate. Mitochondria contribute to both circuit properties and their variability by shaping the cellular processes on which circuit function depends. Across neurons and glia, mitochondria provide bioenergetic support, regulate Ca2+ dynamics and reactive oxygen species levels, influence neurotransmitter synthesis and turnover, and sustain quality control programmes that preserve cellular integrity. The capacity of mitochondria to provide this support and their plasticity have been linked to circuit architecture, engagement and adaptation, with implications for learning and memory, reward and reinforcement, state-trait anxiety and motivation. Here we describe two complementary modes of mitochondrial support: a baseline mode, in which mitochondria sustain circuit architecture and physiological properties over long timescales, and an activity-evoked mode, in which local mitochondrial outputs support synaptic transmission and plasticity. Distinguishing these two modes helps to explain how behavioural modulators, including stress hormones, immune activity and metabolic signals, can shape behaviour by altering either baseline mitochondrial control of circuit readiness or activity-evoked mitochondrial support during circuit engagement.

Motor neuron diseases (MNDs) are caused by the progressive loss of motor neurons and eventually lead to paralysis and death. Once viewed as primarily neurocentric, MNDs are now recognized to be driven by intertwined cell-autonomous and non-cell-autonomous mechanisms. Dissecting these interactions is essential for developing effective therapies. Here, we describe induced pluripotent stem cell-derived 3D models that can be used to capture distinct aspects of MND pathology. We show that spinal cord organoids can be used to investigate cell-autonomous mechanisms and motor neuron-glia interactions (with axially elongated spinal cord organoids being particularly useful to study developmental vulnerability) as well as in 3D muscle and combined neuromuscular models to dissect muscle pathology and neuromuscular junction dismantling. In parallel, we discuss advances in bioengineering, machine learning and human trunk-like models, which together can begin to reproduce the coordinated co-development and spatial organization of the multiple tissues affected in MNDs. We discuss how these systems have advanced our understanding of disease mechanisms and highlight opportunities for drug repurposing. Finally, we propose a mechanism-informed and phenotype-informed framework to guide 3D model selection for future research and to prioritize promising avenues for therapeutic development.

Acetylcholine release in the hippocampus has been associated with diverse neural functions in learning and memory, including novelty, uncertainty detection, error correction, arousal and hidden state inference, while also modulating theta oscillations. Confronted with this plurality of roles, a unifying framework for interpreting cholinergic function is lacking. Recently, predictive models have emerged as a normative lens for understanding neural function, viewing the brain as learning to predict environmental dynamics. Within this framework, we propose that hippocampal acetylcholine serves as a fundamental learning signal, encoding state transition prediction errors - the magnitude of mismatches between predicted and actual transitions in environmental states. We suggest that, analogously to how dopamine signals reward prediction errors to guide value learning, acetylcholine guides structural learning, signalling state transition prediction errors. By condensing behaviour onto timescales amenable to spike-timing-dependent plasticity, theta sequences provide a potential substrate for calculating state transition prediction errors and driving the synaptic updates that revise the internal world model. Within the septo-hippocampal circuit, we propose that theta sequences relay predictions to the septum for comparison against observed state transitions, with the resulting cholinergic feedback gating plasticity in proportion to the mismatch. Plausibly, this mechanistic account unifies the diverse roles of acetylcholine, from novelty detection to hidden state inference, as aspects of a single computational principle - learning predictive internal world models from precisely-timed neural sequences.

Astrocytes are morphologically complex and functionally diverse glial cells that play central roles in neural circuits and disease. Although transcriptomic and physiological analyses have advanced understanding of astrocyte function, the intricacies of the relationship between gene expression and protein levels remains poorly understood. Proteins, rather than transcripts, execute the molecular processes that define astrocyte function, often within specialized subcellular compartments whose molecular composition has remained largely unresolved. Recent advances with genetically encoded proximity-dependent biotinylation have enabled mapping of astrocyte proteomes and subproteomes, revealing the spatial organization of protein networks that underpin astrocyte identity and function. In this Review we summarize strategies for defining astrocyte proteomes and subproteomes, emphasizing methodological innovations that permit cell-type-specific and spatially resolved proteomic profiling within intact neural tissue. The use of these approaches is shaping understanding of astrocyte biology by uncovering the molecular nature of their distributed physiology and the molecular basis of their interactions with neurons and other cells. Viewing astrocytes through a proteomic lens is beginning to help elucidate the molecular determinants of their morphology, signalling, and roles in homeostasis and disease. Such insights should enable detailed mechanistic understanding of astrocyte function and identify new avenues for targeting astrocytic contributions to brain disorders.

Epitranscriptomic regulation of cellular RNAs is a major mechanism of gene expression control in the brain. N[6]-Methyladenosine (m[6]A) is installed on thousands of mRNAs and non-coding RNAs, where it functions as a context-dependent regulator of RNA-protein interactions to control the amplitude and kinetics of gene expression. In the nervous system, m[6]A is critical for neurodevelopment, synaptic plasticity and adaptive responses to physiological stimuli, and its dysregulation has been linked to various brain disorders. In this Review, we present a comprehensive synthesis of how m[6]A is deposited, interpreted and dynamically regulated, and integrate recent advances to present a unified framework for its function in neural cells. We discuss how m[6]A coordinates RNA stability, translation, localization and chromatin-associated processes across developmental and adult contexts and how disruption of these pathways contributes to neurological disease. Finally, we explore challenges and future directions for the field.

AMPA receptors (AMPARs) mediate the majority of fast excitatory neurotransmission in the mammalian brain. Recent structural, functional and proteomic advances have reshaped our understanding of how these receptors assemble, gate and diversify within distinct synaptic environments. Notably, AMPAR function is governed by an interlocking set of regulatory layers that include alternative splicing and RNA editing within regions of the receptor subunits responsible for gating and permeation, and direct allosteric coupling with different families of auxiliary proteins. The assembly of AMPARs through a dedicated endoplasmic reticulum biogenesis pathway ensures accurate tetramer formation and provides a regulatory checkpoint for synaptic receptor abundance. Brain development introduces additional layers of control as editing, splicing and auxiliary-subunit expression shift from embryonic to adult states, whereas interactions with extracellular proteins contribute to the organization and diversification of synaptic architecture across circuits. Thus, AMPARs are dynamic macromolecular assemblies whose diversity underlies the breadth of glutamatergic signalling in health and disease. This Review synthesizes these emerging principles, highlighting how AMPARs transition from their molecular 'birth' to their deployment at functionally specialized synapses.