Biology subjects

Spruston, N.

Publications and source records attributed to Spruston, N..

14 recordsLinked to original sources

bioRxiv2024

Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profiles

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single-cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new method, which we call morphoFISH, that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.

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bioRxiv2023

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

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bioRxiv2023

Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis

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.

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bioRxiv2022

Brain-wide measurement of protein turnover with high spatial and temporal resolution

Synaptic plasticity underlies learning and memory by altering neuronal connections in response to experiences1. However, the loci of learning-induced synaptic plasticity, and the degree to which plasticity is localized or distributed, remain largely unknown2. We developed a method (DELTA) for mapping brain-wide changes in synaptic protein turnover with single-synapse resolution, based on Janelia Fluor dyes and HaloTag knock-in mice. During associative learning, the turnover of the ionotropic glutamate receptor GluA2, an indicator of synaptic plasticity, was enhanced in several brain regions, most markedly in the hippocampal area CA1. More broadly distributed increases in turnover of synaptic proteins were observed in response to environmental enrichment. In CA1, GluA2 stability was regulated in an input specific manner, with more turnover in layers containing input from CA3 compared to entorhinal cortex. DELTA will facilitate exploration of the molecular and circuit basis of learning and memory and other forms of adaptive and maladaptive plasticity at scales ranging from single synapses to the entire brain.

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bioRxiv2021

Organizing memories for generalization in complementary learning systems

Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize a safe route to a water source and generalize to features that allow them to find new water sources, without expecting new paths to exactly resemble previous ones. Memory aids generalization by allowing the brain to extract general patterns from specific instances that were spread across time, such as when humans progressively build semantic knowledge from episodic memories. This cognitive process depends on the neural mechanisms of systems consolidation, whereby hippocampal-neocortical interactions gradually construct neocortical memory traces by consolidating hippocampal precursors. However, recent data suggest that systems consolidation only applies to a subset of hippocampal memories; why certain memories consolidate more than others remains unclear. Here we introduce a novel neural network formalization of systems consolidation that highlights an overlooked tension between neocortical memory transfer and generalization, and we resolve this tension by postulating that memories only consolidate when it aids generalization. We specifically show that unregulated memory transfer can be detrimental to generalization in unpredictable environments, whereas optimizing systems consolidation for generalization generates a high-fidelity, dual-system network supporting both memory and generalization. This theory of generalization-optimized systems consolidation produces a neural network that transfers some memory components to the neocortex and leaves others dependent on the hippocampus. It thus provides a normative principle for reconceptualizing numerous puzzling observations in the field and provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization.

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bioRxiv2021

Rapid synaptic plasticity contributes to a learned conjunctive code of position and choice-related information in the hippocampus

To successfully perform goal-directed navigation, animals must know where they are and what they are doing--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.

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bioRxiv2021

Hippocampal and thalamic afferents form distinct synaptic microcircuits in the mouse frontal cortex

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.

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bioRxiv2019

A sparse, spatially biased subtype of mature granule cell is preferentially recruited in hippocampal-associated behaviors

Animals can store information about experiences by activating specific neuronal populations, and subsequent reactivation of these neural ensembles can lead to recall of salient experiences. In the hippocampus, granule cells of the dentate gyrus participate in such memory engrams; however, whether there is an underlying logic to granule cell participation has not been examined. Here, we found that a broad range of novel experiences preferentially activates granule cells of the suprapyramidal blade relative to the infrapyramidal blade. Motivated by this, we identified a suprapyramidal-blade-enriched population of granule cells with distinct spatial, morphological, physiological, and developmental properties. Via transcriptomics, we mapped these traits onto a sparse and discrete granule cell subtype that was recruited at a ten-fold greater frequency than expected by subtype prevalence, constituting the majority of all recruited granule cells. Thus, a rare and spatially localized granule cell subtype is intrinsically predisposed to activation during hippocampal memory formation.

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bioRxiv2019

Synaptic mechanisms of context-dependent sensory responses in the hippocampus

As animals navigate, they must identify features in context. The hippocampus-a structure critical for navigation-exhibits a different code for each environment, uniquely representing locations and objects within each environment in the firing rates of its neurons. It is unknown how or where these context-specific codes emerge. For example, it is unknown whether pyramidal neurons in the CA1 subregion produce these codes (by combining separate inputs from local sensory cues and the environmental context) or inherit conjunctive codes from upstream brain areas. We performed electrical recordings in the hippocampus as mice navigated in two distinct virtual environments. In CA1, subthreshold synaptic responses to a visual cue in one environment were absent when the same cue occurred in the second environment. In CA3, cue-driven spiking also strongly depended on environmental context. These results indicate that context-dependent sensory coding in CA1 is inherited from its CA3 input.

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bioRxiv2019

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

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bioRxiv2018

Functional clustering of dendritic activity during decision-making

The active properties of dendrites support local nonlinear operations, but previous imaging and electrophysiological measurements have produced conflicting views regarding the prevalence of local nonlinearities in vivo. We imaged calcium signals in pyramidal cell dendrites in the motor cortex of mice performing a tactile decision task. A custom microscope allowed us to image the soma and up to 300 m of contiguous dendrite at 15 Hz, while resolving individual spines. New analysis methods were used to estimate the frequency and spatial scales of activity in dendritic branches and spines. The majority of dendritic calcium transients were coincident with global events. However, task-associated calcium signals in dendrites and spines were compartmentalized by dendritic branching and clustered within branches over approximately 10 m. Diverse behavior-related signals were intermingled and distributed throughout the dendritic arbor, potentially supporting a large computational repertoire and learning capacity in individual neurons.

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bioRxiv2018

Astrocytes integrate and drive neural activity

Many brain functions depend on the ability of neural networks to temporally integrate transient inputs to produce sustained discharges. This can occur through cell-autonomous mechanisms in individual neurons or through reverberating activity in recurrently connected neural networks. We report a third mechanism involving temporal integration of neural activity by a network of astrocytes. Previously, we showed that some types of interneurons can generate long-lasting trains of action potentials for tens of seconds (\"barrage firing\") following repeated depolarizing stimuli. Here, we show that calcium signaling in an astrocytic network correlates with barrage firing; that active depolarization of astrocyte networks by chemical or optogenetic stimulation enhances barrage firing; and that chelating internal calcium, inhibiting release from internal stores, or inhibiting GABA transporters or metabotropic glutamate receptors inhibited barrage firing. Thus, through complex molecular processes, networks of interconnected astrocytes influence the spatiotemporal dynamics of neural networks by directly integrating neural activity and driving long-lasting barrages of action potentials in some populations of inhibitory interneurons.

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bioRxiv2017

ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology

Neurons perform computations by integrating inputs from thousands of synapses - mostly in the dendritic tree - to drive action potential firing in the axon. One fruitful approach to understanding this process is to record from neurons using patch-clamp electrodes, fill the recorded neuron with a substance that allows subsequent staining, reconstruct the three-dimensional architecture of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic process, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.\n\nSignificance StatementWe developed a software package - ShuTu - that integrates automated reconstruction of stained neurons with manual error correction. This package facilitates rapid reconstruction of the three-dimensional geometry of neuronal dendritic trees, often needed for computational simulations of the functional properties of these structures.

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bioRxiv2017

The Transcriptional Logic of Mammalian Neuronal Diversity

The mammalian nervous system is constructed of many cell types, but the principles underlying this diversity are poorly understood. To assess brain-wide transcriptional diversity, we sequenced the transcriptomes of the largest collection of genetically and anatomically identified neuronal classes. Using improved expression metrics that distinguish information content from signal-to-noise-ratio, we found that homeobox transcription factors contain the highest information about cell types and have the lowest noise. Genes that contribute the most to neuronal diversity tend to be long and enriched in factors specifically involved in neuronal function. Genome accessibility measurements reveal that long genes have more candidate regulatory elements arrayed in more distinct patterns. These elements frequently overlap interspersed repeats (mobile elements) and the pattern of repeats is predictive of gene expression. New regulatory sites resulting from elongation of neuronal genes by mobile elements may be an evolutionary force enhancing nervous system complexity.

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