Connectome-constrained modeling identifies neurons and synapses that sustain spontaneous activity in Drosophila

Connectome analysis of a cerebellum-like circuit for sensory prediction.

Many forms of learning, for example, learning a model of the environment or a motor skill, rely on synaptic plasticity that is widely distributed across cell types and network stages. Understanding how this distributed plasticity functions is a central challenge in neuroscience1-5. Here we use connectomics to map the cell types and synaptic connections underlying a form of multi-layer continual learning that cancels predictable sensory responses in a cerebellum-like structure in electric fish6,7. Our analysis shows inhibitory and disinhibitory sensory input pathways that fulfil theoretical requirements for instructing synaptic plasticity8,9, structured synaptic connectivity between network stages that solves a credit assignment problem and structured recurrent connectivity that accelerates sensory prediction and cancellation. A computational model constrained by electrophysiological recordings shows how this synaptic connectivity ensures that multiple sites of plasticity cooperate to overcome their individual limitations, resulting in cancellation that is fast, accurate and robust to noise. Overall, these findings highlight the potential of connectomics, in combination with cell-type-specific physiological recordings and computational modelling, for deciphering learning in neural circuits.

Transcriptome-Inspired Spiking Simulations Uncover Human-Specific Prefrontal Dynamics and Provide a Mechanistic Platform for Species-Appropriate Disease Modeling

Whole-brain transcriptomic atlases are now widely available, yet computational neural models are almost exclusively parameterized from rodent data and used to infer human brain function, an extrapolation whose cost remains unquantified. To address this, we constructed a biophysically detailed, conductance-based Hodgkin–Huxley spiking microcircuit of a five-population prefrontal network, where every ion-channel, receptor, and gap-junction conductance was scaled by cell-type-specific gene expression. We parameterized the identical circuit using single-nucleus RNA-seq from mouse mPFC and human DLPFC, alongside a literature-derived baseline, and compared their high-frequency-oscillation (HFO) outputs across seven physiological and pathological states. While population firing rates differed only modestly between the two refinements (∼20% for pyramidal and PV cells), the oscillatory dynamics diverged dramatically. The human-refined circuit generated strongly synchronized PV activity and robust ripple- and fast-ripple-band power (e.g., healthy-wake ripple power, in arbitrary units: 322 vs. 24 and 22), whereas the mouse-refined and literature arms remained asynchronous (interneuron synchrony: 0.21 vs. 0.02). This human ≫mouse ≈ original hierarchy was statistically consistent across all seven states (significant arm differences in 75/77 comparisons). Mechanistically, the human transcriptome drove markedly stronger PV–PV electrical coupling (gap-junction scale: 1.78 vs. 0.96) paired with stronger recurrent pyramidal excitation, which collectively synchronized the fast-spiking PV population into a coherent rhythm that perisomatic inhibition then imposed on the local field potential. Critically, these results are model-dependent; the gene-to-conductance mapping is phenomenological, and mRNA expression does not linearly translate to functional conductance. Nonetheless, under this mapping the divergence localizes PV-mediated coupling and excitation–inhibition balance as the parameters most in need of human-specific recalibration. More broadly, this work establishes transcriptome-informed spiking simulation as a powerful strategy for uncovering species-specific computational principles and for building mechanistically grounded, human-relevant models of prefrontal circuit dysfunction, an approach that moves beyond generic rodent defaults to enable targeted, species-appropriate modeling of neurological and psychiatric disorders.

A canonical excitatory–inhibitory motif embedded in the connectome underlies rhythmic locomotion in Drosophila larvae

Rhythmic locomotion is generated by central pattern generators, yet how defined neural circuits generate rhythmic motor waves remains unclear. Theoretical models, including Wilson–Cowan-type excitatory–inhibitory networks, have long proposed that reciprocal interactions between excitatory and inhibitory neuronal populations can generate oscillatory activity and travelling waves, but whether such motifs are physically embedded within biological connectomes is unknown. Here we show that Drosophila larval forward locomotion is generated by a segmentally repeated excitatory–inhibitory network centred on the excitatory A18f and inhibitory A02j neurons. Connectomic analysis revealed that A18f and A02j form reciprocally connected loops coupled across segments, receive input from forward command systems and project to premotor modules controlling muscle contraction and relaxation. Connectome-constrained firing- rate simulations, in which synaptic weights were determined by reconstructed synapse numbers, reproduced rhythmic forward-propagating motor waves with minimal parameter tuning. Physiological recordings revealed activity dynamics and phase relationships consistent with model predictions. Acute and chronic perturbations showed that A18f is required for forward locomotion, whereas optogenetic activation of posterior A18f neurons initiated forward motor waves. Perturbation of inhibitory input to A18f further supported a role for A02j-mediated inhibition. These findings identify a canonical excitatory–inhibitory motif embedded within the connectome as a biological implementation of a rhythm-generating network.

Functionally identified neuronal assemblies robustly encode stimuli and are structurally delineated by inhibition.

In 1949, Donald Hebb proposed that neuronal assemblies with temporally specific patterns of activity form the building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging due to a lack of large-scale structure-activity maps. Here, we combine in vivo optical physiology with postmortem electron microscopy (EM) in the same tissue volume. Using higher-order correlations in fluorescence traces, we extract neuronal assemblies. Physiologically, we show that these assemblies respond more reliably to repeated natural movies than size-matched control ensembles and decode such stimuli more accurately. Structurally, we find that over a quarter of the pyramidal neurons do not participate in any assembly and are significantly less integrated into the connectome than those that do. We do not observe a marked increase in the strength of monosynaptic excitatory connections between neurons sharing assembly assignment, but instead find significantly stronger indirect inhibitory connections targeting cells in other assemblies. These results show that assemblies can serve as functional units of perception and suggest they may be structurally delineated by mutual inhibition.

Sensory experience drives glutamate spillover, presynaptic LTP and functional connectome remodeling in the barrel cortex

NeuroGraphBench: Interacting with Drosophila Connectomes at Scale for Exploring the Functional Logic of Neural Circuits