Neuroscience Radar

2026-10-29

New bioRxiv neuroscience preprints, filtered for computational and systems work.

29papers read
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2shortlisted
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1must-read

52 s · 26,310 input tokens · ≈ $0.0011 · jev-1.13.0 via typesafe

Must-read ≥ 95%

97%new methodbiorxiv

Multi-region two-photon calcium imaging in freely moving animals

Zong, W., Ma, S., Arbaciauskaite, J. et al. · neuroscience

Methods 97%
Abstract

Complex behaviors emerge from the coordinated activity of neural circuits distributed across widely separated brain regions. Existing optical approaches for monitoring neural activity lack the combined capability to record from anatomically distributed regions with high spatiotemporal resolution in freely moving animals. Here we introduce MINI2P-II, a 1.1-g 2P miniscope that achieves large-scale calcium imaging with high spatial resolution, deep imaging depth and dual-color imaging capability. Together with a dual-scope imaging system and a novel implantable periscope lens assembly, MINI2P-II enables simultaneous 2P imaging in widely separated or anatomically adjacent brain regions. In mice, we demonstrate inter-hemisphere coordination in bilateral motor cortex during climbing, egocentric boundary coding in simultaneous retrosplenial and posterior parietal cortex recordings, and in zebra finches, we showed large-scale behavior-dependent activity in both juvenile and adult individuals. MINI2P-II opens the door to multi-region 2P imaging across species and developmental stages in freely moving animals.

biorxiv:10.64898/2026.10.27.760324Useful?👍👎

Maybe 90% to 95%

90%new methodbiorxiv

Reward changes the use and structure of predictive spatial maps

Oza, S., Cohen, A., Wager, G. et al. · neuroscience

Computational models 90%Neural coding 66%
Abstract

Reward-related changes in hippocampal place-cell activity may reflect either different use of an existing spatial map or plasticity that reshapes the map. We separated these contributions in a two-pathway model: a recurrent network learned to predict sensory input and developed a spatial representation, while a reinforcement-learning controller combined this representation with an explicit goal signal to guide behavior. Replaying identical trajectories before and after learning isolated representational plasticity. Goal-directed behavior strongly concentrated place fields near reward, whereas changes to the representation were smaller and more variable. A reward-proximity teaching signal made these changes larger and more consistent, and the change was strongly attenuated when recurrent plasticity was restricted. After reward relocation, goal-related directional tuning adapted more fully than place-field organization, while withdrawing reward and proximity teaching reversed the learned field concentration. Thus, reward affects predictive spatial maps through separable mechanisms: goal information changes how an existing map is used, whereas reward-related teaching signals reshape the map itself. Our results provide a concrete framework for future experiments to disentangle how reward information, plasticity and behavior jointly shape hippocampal activity.

biorxiv:10.64898/2026.10.28.762439Useful?👍👎