@article {68773, title = {Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis}, journal = {bioRxiv}, year = {2023}, month = {2023 May 02}, abstract = {

Animals can learn general task structures and use them to solve new problems with novel sensory specifics. This capacity of {\textquoteleft}learning to learn{\textquoteright}, or meta-learning, is difficult to achieve in artificial systems, and the mechanisms by which it is achieved in animals are unknown. As a step toward enabling mechanistic studies, we developed a behavioral paradigm that demonstrates meta-learning in head-fixed mice. We trained mice to perform a two-alternative forced-choice task in virtual reality (VR), and successively changed the visual cues that signaled reward location. Mice showed increased learning speed in both cue generalization and serial reversal tasks. During reversal learning, behavior exhibited sharp transitions, with the transition occurring earlier in each successive reversal. Analysis of motor patterns revealed that animals utilized similar motor programs to execute the same actions in response to different cues but modified the motor programs during reversal learning. Our study demonstrates that mice can perform meta-learning tasks in VR, thus opening up opportunities for future mechanistic studies.

}, doi = {10.1101/2023.05.01.538936}, url = {https://doi.org/10.1101/2023.05.01.538936}, author = {Xinyu Zhao and Rachel Gattoni and Andrea Kozlosky and Angela Jacobs and Colin Morrow and Sarah Lindo and Nelson Spruston} } @article {67789, title = {Organizing memories for generalization in complementary learning systems.}, journal = {Nature Neuroscience}, volume = {26}, year = {2023}, month = {2023 Aug 01}, pages = {1438-1448}, abstract = {

Our ability to remember the past is essential for guiding our future behavior. Psychological and neurobiological features of declarative memories are known to transform over time in a process known as systems consolidation. While many theories have sought to explain the time-varying role of hippocampal and neocortical brain areas, the computational principles that govern these transformations remain unclear. Here we propose a theory of systems consolidation in which hippocampal-cortical interactions serve to optimize generalizations that guide future adaptive behavior. We use mathematical analysis of neural network models to characterize fundamental performance tradeoffs in systems consolidation, revealing that memory components should be organized according to their predictability. The theory shows that multiple interacting memory systems can outperform just one, normatively unifying diverse experimental observations and making novel experimental predictions. Our results suggest that the psychological taxonomy and neurobiological organization of declarative memories reflect a system optimized for behaving well in an uncertain future.

}, doi = {10.1038/s41593-023-01382-9}, url = {https://doi.org/10.1038/s41593-023-01382-9}, author = {Weinan Sun and Madhu Advani and Nelson Spruston and Andrew Saxe and James E. Fitzgerald} } @article {68503, title = {Brain-wide measurement of protein turnover with high spatial and temporal resolution}, journal = {bioRxiv}, year = {2022}, month = {2022 Nov 13}, abstract = {

Cells regulate function by synthesizing and degrading proteins. This turnover ranges from minutes to weeks, as it varies across proteins, cellular compartments, cell types, and tissues. Current methods for tracking protein turnover lack the spatial and temporal resolution needed to investigate these processes, especially in the intact brain, which presents unique challenges. We describe a pulse-chase method (DELTA) for measuring protein turnover with high spatial and temporal resolution throughout the body, including the brain. DELTA relies on rapid covalent capture by HaloTag of fluorophores that were optimized for bioavailability\ in vivo. The nuclear protein MeCP2 showed brain region- and cell type-specific turnover. The synaptic protein PSD95 was destabilized in specific brain regions by behavioral enrichment. A novel variant of expansion microscopy further facilitated turnover measurements at individual synapses. DELTA enables studies of adaptive and maladaptive plasticity in brain-wide neural circuits.

}, doi = {10.1101/2022.11.12.516226}, url = {https://doi.org/10.1101/2022.11.12.516226}, author = {Boaz Mohar and Jonathan B. Grimm and Ronak Patel and Timothy A. Brown and Paul Tillberg and Luke D. Lavis and Nelson Spruston and Karel Svoboda} } @article {67186, title = {Hippocampal and thalamic afferents form distinct synaptic microcircuits in the mouse frontal cortex.}, journal = {Cell Reports}, volume = {37}, year = {2021}, month = {2021 October 19}, pages = {109837}, abstract = {

Neural circuits within the frontal cortex support the flexible selection of goal-directed behaviors by integrating input from brain regions associated with sensory, emotional, episodic, and semantic memory functions. From a connectomics perspective, determining how these disparate afferent inputs target their synapses to specific cell types in the frontal cortex may prove crucial in understanding circuit-level information processing. Here, we used monosynaptic retrograde rabies mapping to examine the distribution of afferent neurons targeting four distinct classes of local inhibitory interneurons and four distinct classes of excitatory projection neurons in mouse infralimbic cortex. Interneurons expressing parvalbumin, somatostatin, or vasoactive intestinal peptide received a large proportion of inputs from hippocampal regions, while interneurons expressing neuron-derived neurotrophic factor received a large proportion of inputs from thalamic regions. A more moderate hippocampal-thalamic dichotomy was found among the inputs targeting excitatory neurons that project to the basolateral amygdala, lateral entorhinal cortex, nucleus reuniens of the thalamus, and the periaqueductal gray. Together, these results show a prominent bias among hippocampal and thalamic afferent systems in their targeting to genetically or anatomically defined sets of frontal cortical neurons. Moreover, they suggest the presence of two distinct local microcircuits that control how different inputs govern frontal cortical information processing.

}, author = {Kourtney Graham and Nelson Spruston and Erik Bloss} } @article {67767, title = {Rapid synaptic plasticity contributes to a learned conjunctive code of position and choice-related information in the hippocampus}, journal = {Neuron}, year = {2021}, month = {2021 Oct 14}, abstract = {

To successfully perform goal-directed navigation, animals must know where they are and what they are doing{\textemdash}e.g., looking for water, bringing food back to the nest, or escaping from a predator. Hippocampal neurons code for these critical variables conjunctively, but little is known about how this where/what code is formed or flexibly routed to other brain regions. To address these questions, we performed intracellular whole-cell recordings in mouse CA1 during a cued, two-choice virtual navigation task. We demonstrate that plateau potentials in CA1 pyramidal neurons rapidly strengthen synaptic inputs carrying conjunctive information about position and choice. Plasticity-induced response fields were modulated by cues only in animals previously trained to collect rewards based on these cues. Thus, we reveal that gradual learning is required for the formation of a conjunctive population code, upstream of CA1, while plateau-potential-induced synaptic plasticity in CA1 enables flexible routing of the code to downstream brain regions.

}, doi = {https://doi.org/10.1101/2021.06.30.450574}, author = {Xinyu Zhao and Ching-Lung Hsu and Nelson Spruston} }