The ability to use generalized prior experience to guide behavior in novel situations is a fundamental cognitive function. While recent evidence suggests that the hippocampus supports generalization how this is accomplished is poorly understood. Here we combined longitudinal optical imaging in head-fixed mice with computational modeling to examine generalization in hippocampal area CA1. We found that prior training accelerated behavioral adaptation to a novel environment and that this was accompanied by highly stable hippocampal representations. We identified putative memory traces from prior experience that enabled this generalization at multiple levels. At the population level, novel-context network dynamics rapidly aligned with low-dimensional neural subspaces established during prior experience. At the cellular level, spatially-informative weak "residual" activity reflecting generalizable information about the task structure appeared to bias which neurons form place fields (PFs) and where via behavioral timescale synaptic plasticity (BTSP). Finally, this was an active process as many PFs changed their reference frame in the novel environment to reflect the consistent task structure. In sum, the influence of memory traces on new PF formation may allow past experience to guide new learning such that representations are based on generalizable features, thus enabling rapid adaptive behavior in new contexts.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including the hippocampus’ map-like representations of familiar environments. However, whether the representations in such “cognitive maps” can be volitionally and selectively accessed is unknown. We developed a brain-machine interface to test if rats could control their hippocampal activity in a flexible, goal-directed, model-based manner. We show that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This should provide insight into the mechanisms underlying episodic memory recall, mental simulation/planning, and imagination, and open up possibilities for high-level neural prosthetics utilizing hippocampal representations.

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that optimize behavioral performance and reward prediction, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioral policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically-calibrated manipulations of mesolimbic dopamine produced multiple effects inconsistent with value learning but predicted by a neural network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioral policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioral policies, expanding the explanatory power of reinforcement learning models for animal learning.

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction; however, so far there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioural policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically calibrated manipulations of mesolimbic dopamine produced several effects inconsistent with value learning but predicted by a neural-network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioural policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioural policies, expanding the explanatory power of reinforcement learning models for animal learning.

Pioneer transcription factors (PTFs) possess the unique capability to access closed chromatin regions and initiate cell fate changes, yet the underlying mechanisms remain elusive. Here, we characterized the single-molecule dynamics of PTFs targeting chromatin in living cells, revealing a notable 'confined target search' mechanism. PTFs such as FOXA1, FOXA2, SOX2, OCT4 and KLF4 sampled chromatin more frequently than non-PTF MYC, alternating between fast free diffusion in the nucleus and slower confined diffusion within mesoscale zones. Super-resolved microscopy showed closed chromatin organized as mesoscale nucleosome-dense domains, confining FOXA2 diffusion locally and enriching its binding. We pinpointed specific histone-interacting disordered regions, distinct from DNA-binding domains, crucial for confined target search kinetics and pioneer activity within closed chromatin. Fusion to other factors enhanced pioneer activity. Kinetic simulations suggested that transient confinement could increase target association rate by shortening search time and binding repeatedly. Our findings illuminate how PTFs recognize and exploit closed chromatin organization to access targets, revealing a pivotal aspect of gene regulation.

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes are physically limited by objective-to-specimen distance, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed a photochemical strategy for spatially precise sectioning of specimens. By combining serial photochemical sectioning with lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelin sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory bulb–wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative brain, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach. High-resolution microscopes have a short working distance, making it difficult to see deep within large biological samples such as an intact brain. Slicing the tissue with a blade can reach deeper, but this often distorts or destroys the fine structures that scientists want to study. By embedding a sample in a light-sensitive hydrogel, Wang et al. demonstrated a gentler approach using a precise ray or sheet of light to dissolve or cut away tissue layer by layer. After each layer is removed, the newly exposed surface is imaged, allowing for a complete, high-resolution, three-dimensional reconstruction without damaging physical contact. 

bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2024.08.01.605857v1

Seconds-scale network states, affecting many neurons within a network, modulate neural activity by complementing fast integration of neuron-specific inputs that arrive in the milliseconds before spiking. Non-rhythmic subthreshold dynamics at intermediate timescales, however, are less well-characterized. We found, using automated whole cell patch clamping in vivo, that spikes recorded in CA1 and barrel cortex in awake mice are often preceded not only by monotonic voltage rises lasting milliseconds, but also by more gradual (lasting 10s-100s of ms) depolarizations. The latter exert a gating function on spiking, in a fashion that depends on the gradual rise duration: the probability of spiking was higher for longer gradual rises, even controlling for the amplitude of the gradual rises. Barrel cortex double-autopatch recordings show that gradual rises are shared across some but not all neurons. The gradual rises may represent a new kind of state, intermediate both in timescale and in proportion of neurons participating, which gates a neuron's ability to respond to subsequent inputs.

Animals can learn general task structures and use them to solve new problems with novel sensory specifics. This capacity of ‘learning to learn’, 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.

Mitochondria-derived reactive oxygen species (mROS) are required for the survival, proliferation, and metastasis of cancer cells. The mechanism by which mitochondrial metabolism regulates mROS levels to support cancer cells is not fully understood. To address this, we conducted a metabolism-focused CRISPR-Cas9 genetic screen and uncovered that loss of genes encoding subunits of mitochondrial complex I was deleterious in the presence of the mitochondria-targeted antioxidant mito-vitamin E (MVE). Genetic or pharmacologic inhibition of mitochondrial complex I in combination with the mitochondria-targeted antioxidants, MVE or MitoTEMPO, induced a robust integrated stress response (ISR) and markedly diminished cell survival and proliferation in vitro. This was not observed following inhibition of mitochondrial complex III. Administration of MitoTEMPO in combination with the mitochondrial complex I inhibitor phenformin decreased the leukemic burden in a mouse model of T cell acute lymphoblastic leukemia. Thus, mitochondrial complex I is a dominant metabolic determinant of mROS-dependent cellular fitness.