Local and global predictors of synapse elimination during motor learning

Science Advances, Год журнала: 2024, Номер 10(11)

Опубликована: Март 15, 2024

During learning, synaptic connections between excitatory neurons in the brain display considerable dynamism, with new being added and old eliminated. Synapse elimination offers an opportunity to understand features of synapses that deems dispensable. However, limited observations activity plasticity vivo, subjected remain poorly understood. Here, we examined functional basis synapse apical dendrites L2/3 primary motor cortex throughout learning. We found no evidence is facilitated by a lack or other local forms plasticity. Instead, eliminated asynchronous nearby synapses, suggesting clustering critical component survival. In addition, show delayed timing respect postsynaptic output. Thus, inputs fail be co-active their neighboring are mistimed neuronal output targeted for elimination.

Язык: Английский

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Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 28, 2025

Apical and basal dendrites of pyramidal neurons receive anatomically functionally distinct inputs, implying compartment-level functional diversity during behavior. To test this, we imaged in vivo calcium signals from soma, apical dendrites, mouse hippocampal CA3 head-fixed navigation. capture compartment-specific population dynamics, developed computational tools to automatically segment extract accurate fluorescence traces densely labeled neurons. We validated the method on sparsely preparations synthetic data, predicting an optimal labeling density for high experimental throughput analytical accuracy. Our detected rapid, local dendritic activity. Dendrites showed robust spatial tuning, similar soma but with higher activity rates. Across days, remained more stable outperformed decoding animal's position. Thus, population-level differences may reflect input-output functions computations CA3. These will facilitate future studies mapping sub-cellular their relation The authors develop analysis package characterizing neural using optical imaging show that are representations than area

Язык: Английский

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Dendritic growth and synaptic organization from activity-independent cues and local activity-dependent plasticity

eLife, Год журнала: 2025, Номер 12

Опубликована: Фев. 3, 2025

Dendritic branching and synaptic organization shape single-neuron network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth the of synapses arise from interaction activity-independent cues potential partners local activity-dependent plasticity. Consistent with experiments, three phases dendritic – overshoot, pruning, stabilization naturally model. The generates stellate-like morphologies that capture several morphological features biological under normal perturbed learning rules, reflecting variability. Model-generated dendrites have approximately optimal wiring length consistent experimental measurements. In addition to establishing morphologies, plasticity rules organize spatial clusters according correlated activity experience. We demonstrate trade-off between -independent factors influences location throughout development, suggesting early developmental variability can affect mature morphology function. Therefore, single account for inputs development. Our work suggests concrete components underlying emergence formation removal function dysfunction, provides experimentally testable predictions role individual components.

Язык: Английский

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Somatic and Dendritic Encoding of Spatial Variables in Retrosplenial Cortex Differs during 2D Navigation

Neuron, Год журнала: 2019, Номер 105(2), С. 237 - 245.e4

Опубликована: Ноя. 20, 2019

Язык: Английский

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A synaptic learning rule for exploiting nonlinear dendritic computation

Neuron, Год журнала: 2021, Номер 109(24), С. 4001 - 4017.e10

Опубликована: Окт. 28, 2021

Information processing in the brain depends on integration of synaptic input distributed throughout neuronal dendrites. Dendritic is a hierarchical process, proposed to be equivalent by multilayer network, potentially endowing single neurons with substantial computational power. However, whether can learn harness dendritic properties realize this potential unknown. Here, we develop learning rule from cable theory and use it investigate capacity detailed pyramidal neuron model. We show that computations using spatial or temporal features patterns learned, even synergistically combined, solve canonical nonlinear feature-binding problem. The voltage dependence drives coactive synapses engage nonlinearities, whereas spike-timing shapes time course subthreshold potentials. input-output relationships therefore flexibly tuned through plasticity, allowing optimal implementation functions neurons.

Язык: Английский

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