Biology subjects

Sjulson, L.

Publications and source records attributed to Sjulson, L..

9 recordsLinked to original sources

bioRxiv2026

Distinct population dimensions separate sensory adaptation and expectation-related signals in visual cortex

Mismatch negativity is conventionally quantified as a scalar response-amplitude difference between rare deviant and repeated standard stimuli, leaving its organization at the neuronal population level unresolved. We recorded population activity in mouse primary visual cortex (V1) during visual oddball and control paradigms while testing contributions from anterior cingulate cortex (ACC) input using optogenetic inhibition. Mismatch modulated firing rates bidirectionally and these changes largely canceled out on average, yet produced robustly separable population activity patterns. On the stimulus-encoding manifold, repeated standard exposure produced standard-specific response attenuation and a shared rotation of standard and deviant representations, revealing a component not isolated by conventional stimulus-specific adaptation and deviance-detection contrasts. Off the manifold, standards and deviants occupied opposing positions along an ACC-modulated axis consistent with expectation confirmation and violation. Thus, visual mismatch responses engage separable transformations of stimulus representations and expectation-related population activity, with ACC input selectively contributing to the latter while not significantly affecting stimulus encoding or on-manifold adaptation.

neuroscience↗

bioRxiv2024

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

neuroscience↗

bioRxiv2024

Neocortical long-range inhibition promotes cortical synchrony and sleep

Sleep and wakefulness are associated with distinct cortical patterns of rhythmic activity. During low-arousal states such as slow wave sleep, synchronous low-frequency rhythms dominate activity across widespread cortical regions. Although inhibitory neurons are increasingly recognized as key players of cortical state, the in vivo circuit mechanisms coordinating synchronized activity across local and distant neocortical networks remain poorly understood. Here, we show that somatostatin and chondrolectin co-expressing cells (Sst-Chodl), a sparse and genetically distinct class of neocortical GABAergic inhibitory neurons, are selectively active during low-arousal states and largely silent during periods of high arousal. In contrast to most neocortical inhibitory neurons, Sst-Chodl cells, despite being extremely sparse, exert widespread influence across the neocortex via long-range axons that simultaneously target multiple regions. Selective activation of Sst-Chodl cells is sufficient to promote multi-region cortical synchronization characteristic of low-arousal states and to induce sleep. Together, these findings show that long-range Sst-Chodl inhibitory neurons not only track behavioral state but can actively promote sleep-like cortical activity and sleep behavior, highlighting an important contribution of cortical circuits to sleep regulation alongside established subcortical mechanisms.

neuroscience↗

bioRxiv2024

Developmental disruption of Mef2c in Medial Ganglionic Eminence-derived cortical inhibitory interneurons impairs cellular and circuit function

MEF2C is strongly linked to various neurodevelopmental disorders (NDDs) including autism, intellectual disability, schizophrenia, and attention-deficit/hyperactivity. Mice constitutively lacking one copy of Mef2c, or selectively lacking both copies of Mef2c in cortical excitatory neurons, display a variety of behavioral phenotypes associated with NDDs. The MEF2C protein is a transcription factor necessary for cellular development and synaptic modulation of excitatory neurons. MEF2C is also expressed in a subset of cortical GABAergic inhibitory neurons, but its function in those cell types remains largely unknown. Using conditional deletions of the Mef2c gene in mice, we investigated the role of MEF2C in Parvalbumin-expressing Interneurons (PV-INs), the largest subpopulation of cortical GABAergic cells, at two developmental timepoints. We performed slice electrophysiology, in vivo recordings, and behavior assays to test how embryonic and late postnatal loss of MEF2C from GABAergic interneurons impacts their survival and maturation, and alters brain function and behavior. We found that loss of MEF2C from PV-INs during embryonic, but not late postnatal, development resulted in reduced PV-IN number and failure of PV-INs to molecularly and synaptically mature. In association with these deficits, early loss of MEF2C in GABAergic interneurons lead to abnormal cortical network activity, hyperactive and stereotypic behavior, and impaired cognitive and social behavior. Our findings indicate that MEF2C expression is critical for the development of cortical GABAergic interneurons, particularly PV-INs. Embryonic loss of function of MEF2C mediates dysfunction of GABAergic interneurons, leading to altered in vivo patterns of cortical activity and behavioral phenotypes associated with neurodevelopmental disorders.

neuroscience↗

bioRxiv2024

Disruption of Cholinergic Retinal Waves Alters Visual Cortex Development and Function

Retinal waves represent an early form of patterned spontaneous neural activity in the visual system. These waves originate in the retina before eye-opening and propagate throughout the visual system, influencing the assembly and maturation of subcortical visual brain regions. However, because it is technically challenging to ablate retina-derived cortical waves without inducing compensatory activity, the role these waves play in the development of the visual cortex remains unclear. To address this question, we used targeted conditional genetics to disrupt cholinergic retinal waves and their propagation to select regions of primary visual cortex, which largely prevented compensatory patterned activity. We find that loss of cholinergic retinal waves without compensation impaired the molecular and synaptic maturation of excitatory neurons located in the input layers of visual cortex, as well as layer 1 interneurons. These perinatal molecular and synaptic deficits also relate to functional changes observed at later ages. We find that the loss of perinatal cholinergic retinal waves causes abnormal visual cortex retinotopy, mirroring changes in the retinotopic organization of gene expression, and additionally impairs the processing of visual information. We further show that retinal waves are necessary for higher order processing of sensory information by impacting the state-dependent activity of layer 1 interneurons, a neuronal type that shapes neocortical state-modulation, as well as for state-dependent gain modulation of visual responses of excitatory neurons. Together, these results demonstrate that a brief targeted perinatal disruption of patterned spontaneous activity alters early cortical gene expression as well as synaptic and physiological development, and compromises both fundamental and, notably, higher-order functions of visual cortex after eye-opening.

neuroscience↗

bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

neuroscience↗

bioRxiv2022

Off-manifold coding in visual cortex revealed by sleep

Low-dimensional neural manifolds are controversial in part because it is unclear how to reconcile them with high-dimensional representations observed in areas such as primary visual cortex (V1). We addressed this by recording neuronal activity in V1 during slow-wave sleep, enabling us to identify internally-generated low-dimensional manifold structure and evaluate its role during visual processing. We found that movements and visual stimuli were both encoded in the "on-manifold" subspace preserved during sleep. However, only stimuli were encoded in the "off-manifold" subspace, which contains activity patterns that are less likely than chance to occur spontaneously during sleep. This off-manifold activity comprises sparse firing in neurons with the strongest low-dimensional modulation by movement, which paradoxically prevents movement-evoked activity from interfering with stimulus representations. These results reveal an unexpected link between low-dimensional dynamics and sparse coding, which together create a protected off-manifold coding space keeping high-dimensional representations separable from movement-evoked activity.

neuroscience↗

bioRxiv2022

Hybrid offspring of C57BL/6J mice exhibit improved properties for neurobehavioral research

C57BL/6 is the most commonly used mouse strain in neurobehavioral research, serving as a background for multiple transgenic lines. However, C57BL/6 exhibit behavioral and sensorimotor disadvantages that worsen with age. We bred FVB/NJ females and C57BL/6J males to generate first-generation hybrid offspring, (FVB/NJ x C57BL/6J)F1. The hybrid mice exhibit reduced anxiety-like behavior, improved learning, and enhanced long-term spatial memory. In contrast to both progenitors, older hybrids maintain sensorimotor performance and exhibit improved long-term memory. The hybrids are larger than C57BL/6J, exhibiting enhanced running behavior on a linear track during freely-moving electrophysiological recordings. Hybrids exhibit typical rate and phase coding of space by CA1 pyramidal cells. Hybrids generated by crossing FVB/NJ females with transgenic males of a C57BL/6 background support optogenetic neuronal control in neocortex and hippocampus. The hybrid mice provide an improved model for neurobehavioral studies combining complex behavior, electrophysiology, and genetic tools readily available in C57BL/6 mice.

neuroscience↗

bioRxiv2017

Cocaine place conditioning strengthens location-specific hippocampal inputs to the nucleus accumbens

Conditioned place preference (CPP) is a widely used model of addiction-related behavior whose underlying mechanism is not understood. In this study, we used dual site silicon probe recordings in freely moving mice to examine interactions between the hippocampus and nucleus accumbens in cocaine CPP. We found that CPP was associated with recruitment of nucleus accumbens medium spiny neurons to fire in the cocaine-paired location, and this recruitment was driven predominantly by selective strengthening of hippocampal inputs arising from place cells that encode the cocaine-paired location. These findings provide in vivo evidence that the synaptic potentiation in the accumbens caused by repeated cocaine administration preferentially affects inputs that were active at the time of drug exposure. This provides a potential physiological mechanism by which drug use becomes associated with specific environmental contexts.

neuroscience↗