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Olsen, S. R.

Publications and source records attributed to Olsen, S. R..

At least 19 recordsLinked to original sources
bioRxiv2026

Task Engagement Gates Interareal Communication Geometry in the Mouse Thalamocortical-Midbrain Visual Circuit

Visual processing unfolds across hierarchically organized brain circuits. Existing theories largely explain changes in population geometry through local shifts in gain, firing-rate statistics, or recurrent dynamics, yet do not account for how interareal coordination interacts with local population geometry to constrain downstream population states. We used task engagement, compared to a passive condition, to probe this coordination in Neuropixels recordings spanning the mouse visual thalamocortical-midbrain circuit. Engagement reduced network activity, response participation, and dimensionality across the hierarchy. To account for this circuit-level organization, we developed a theoretical framework in which afferent population geometry interacts with local recurrent dynamics to constrain the accessible dynamics of downstream populations. Across the thalamocortical stages, population-wide afferent statistics predicted downstream activity and dimensionality. At the cortex--midbrain interface, engagement instead reorganized interareal communication geometry. Together, these results identify interareal input geometry as a key constraint on neural population dynamics and uncover a general principle by which behavioral engagement constrains neural state spaces across distributed visual circuits.

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bioRxiv2024

Stimulus history, not expectation, drives sensory prediction errors in mammalian cortex

Hierarchical predictive coding (HPC) models have recently flourished in neuroscience1-9. Feedforward and feedback processing are at the heart of HPC models. Previous experimental studies using fMRI, EEG/MEG, and LFP9-11 do not reliably resolve feedback modulation from local computations and feedforward outputs. Here, using open-science8, multi-species, multi-area, high-density12, laminar neurophysiology13, we empirically test whether hierarchical predictive coding is a key component shaping cortical processing of visual stimuli. To isolate visual information processing and eliminate motor/reward confounders9-11, we use a no-report task. Our task leveraged so-called global oddballs (GO) as unpredictable, deviant stimuli that circumvent low-level adaptation. We examined their responses relative to local oddballs (LO) that we habituated into highly predictable priors. Four surprising findings in this dataset challenge many existing hierarchical predictive coding models. First, GO responses were exclusive to higher-order, more cognitive areas rather than early-to-mid visual cortex. Second, inhibitory interneuron-targeted optogenetics in primates and mice and waveform shape analysis in primates revealed no evidence that predictive suppression was implemented via these interneurons. Third, highly predictable LO responses dominated in over 50% of all neurons, including in higher-order cortex, which should have anticipated them, indicating limited evidence for predictive suppression. Lastly, prediction error responses evoked by GOs did not evoke feedforward processing. These results reveal circuit dynamics that govern how prediction shapes visual processing, motivating more neurally constrained predictive processing models.

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bioRxiv2024

Deciphering neuronal variability across states reveals dynamic sensory encoding

Influenced by factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. Because these factors are non-stationary, they dynamically impact the fidelity of sensory processing. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model with latent states to partition visual cortical variability across three crucial categories of sources: internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to the rhythmic patterns of cortical local field potentials, we consistently identified three distinct oscillation states. Each state revealed a unique variability profile and a consistent descending trend of stimulus modulation across the visual hierarchy. Regression models within each state revealed a dynamic composition of factors contributing to the observed spiking variability, with the primary influencing factor switching within seconds. In the state dominated by high-frequency oscillations, sensory inputs and behavior exerted the most influence on population dynamics. Conversely, internal brain activity explained most of the variance in the state dominated by low-frequency oscillations. This heterogeneity across states underscores the importance of partitioning variability over time, particularly when considering the dynamic influence of non-stationary factors on sensory processing.

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bioRxiv2023

Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics

Recent studies have found dramatic cell-type specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at the cell-type specific level of granularity to understand brain function. Although initial work classified and characterized activity for each cell type, the specific alterations in cortical circuitry--particularly when multiple novelty effects interact--remain unclear. To address this gap, we employed a large-scale public dataset of electrophysiological recordings in the visual cortex of awake, behaving mice using Neuropixels probes and designed population network models to investigate the observed changes in neural dynamics in response to a combination of distinct forms of novelty. The model parameters were rigorously constrained by publicly available structural datasets, including multi-patch synaptic physiology and electron microscopy data. Our systematic optimization approach identified tens of thousands of model parameter sets that replicate the observed neural activity. Analysis of these solutions revealed generally weaker connections under novel stimuli, as well as a shift in the balance e between SST and VIP populations. Along with this, PV and SST populations experienced overall more excitatory influences compared to excitatory and VIP populations. Our results also highlight the role of VIP neurons in multiple aspects of visual stimulus processing and altering gain and saturation dynamics under novel conditions. In sum, our findings provide a systematic characterization of how the cortical circuit adapts to stimulus novelty by combining multiple rich public datasets.

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bioRxiv2022

Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice

Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording electroencephalographic (EEG) responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a bi-phasic sequence of 120 ms profound off period and a rebound excitation. A similar pattern, partially attributed to burst spiking, is seen in thalamic nuclei, and is associated with a pronounced late component in the evoked EEG. We infer that cortico-thalamo-cortical interactions drive the long-lasting evoked EEG signals elicited by deep cortical stimulation during the awake state. The cortical and thalamic off period and rebound excitation, and the late component in the EEG, are reduced during running and absent during anesthesia.

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bioRxiv2022

Uncovering circuit mechanisms of current sinksand sources with biophysical simulations ofprimary visual cortex

Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of numerous cortical neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: Firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. Our findings show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.

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bioRxiv2022

Influence of claustrum on cortex varies by area, layer, and cell type

The claustrum is a small subcortical structure with widespread connections with disparate regions of the cortex. These far-reaching projections have led to many hypotheses concerning its function. However, we know little about how claustrum input affects neural activity in cortex, particularly beyond frontal areas. Here, using optogenetics and multi-regional Neuropixels recordings from over 15,000 neurons in awake mice, we demonstrate that the effect of claustrum input differs depending on brain area, layer, and cell type. Brief claustrum stimulation produces approximately 1 spike per claustrum neuron, which affects many fast-spiking (FS; putative inhibitory) but very few regular-spiking (RS; putative excitatory) cortical neurons. Prolonged claustrum stimulation affects many more cortical FS and RS neurons. More inhibition occurs in frontal regions and deeper layers, while more excitation occurs in posterior regions and superficial layers. These differences imply that the function of claustrum input to cortex depends on the area, supporting the idea that claustro-cortical circuits are organized into functional modules.

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bioRxiv2022

A survey of neurophysiological differentiation across mouse visual brain areas and timescales

Neurophysiological differentiation (ND), a metric that quantifies the number of distinct activity states that the brain or its part visits over a period of time, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete populations of spiking neurons rather than the whole brain. Therefore, in this study, we use Neuropixels recordings from the mouse brain to characterize the ND metric within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas as well as the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy as well. For animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Analysis of spiking activity allows us to characterize the ND metric across a wide range of timescales from 10s of milliseconds to a few seconds. This analysis reveals that although ND of activity of single neurons is often maximized at an optimal timescale around 100 ms, the optimum shifts to under 5 ms for ND of neuronal ensembles. Finally, we find that the ND of activations in convolutional neural networks (CNNs) trained on an image classification task shows distinct trends relative to the mouse visual system: ND is often higher for less naturalistic stimuli and varies by orders of magnitude across the hierarchy, compared to modest variation in the mouse brain. Together, these results suggest that ND computed on cellular-level neural recordings can be a useful tool highlighting cell populations that may be involved in subjective perception. SummaryAdvances in our understanding on neural coding has revealed that information about visual stimuli is represented across several brain regions. However, availability of information does not imply that it is necessarily utilized by the brain, much less that it is subjectively perceived. Since percepts originate in neural activity, distinct percepts must be associated with distinct states of neural activity, at least within the brain region that supports the percepts. Thus, one approach developed in this direction is to quantify the number of distinct states that the activity of the brain goes through, called neurophysiological differentiation (ND). ND of the entire brain has been shown to reflect subjective reports of visual stimulus meaningfulness. But what specific subpopulations within the brain could be supporting conscious perception, and what is the correct timescale on which states should be quantified? In this study, we analyze ND of spiking neural activity in the mouse visual cortex recorded using Neuropixels probes, allowing us to characterize the ND metric across a wide range of timescales all the way down from 5 ms to a few seconds. It also allows us to understand the ND of neural activity of different ensembles of neurons, from individual thalamic or cortical ensembles to those spanning across multiple visual areas in the mouse brain.

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bioRxiv2021

Backward masking in mice requires visual cortex

Visual masking is used to infer the timescale of conscious perception in humans; yet the underlying circuit mechanisms are not understood. Here we describe a two-alternative choice backward masking task in mice and humans, in which the location of a briefly presented grating is effectively masked within a 50 ms window after stimulus onset. Importantly, human subjects reported reduced perceptual visibility during masking that closely corresponded with behavior deficits. In mice, optogenetic silencing of visual cortex reduces performance over a similar time course as masking. However, response rates and accuracy do not match masking, demonstrating that cortical silencing and masking are distinct phenomena. Spiking responses recorded in primary visual cortex (V1) are consistent with masked behavior when quantified over long, but not short, time windows, indicating masking involves further downstream processing. Behavioral performance can be quantitatively recapitulated by a dual accumulator model constrained by V1 activity. The model and the animals performance for the earliest decisions imply that the initial spike arriving from the periphery can trigger a correct response, but subsequent V1 spikes, evoked by the mask, degrade performance for longer decisions. To test the necessity of visual cortex for backward masking, we optogenetically silenced mask-evoked cortical activity which fully restored discrimination of target location. Together, these results demonstrate that mice, like humans, are susceptible to backward visual masking and that visual cortex has a crucial role in this process.

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bioRxiv2021

Anterior claustrum cells are responsive during behavior but not passive sensory stimulation

The claustrum is uniquely positioned to communicate with almost all higher-order cortical areas through widespread and reciprocal anatomical projections, yet the in vivo functional properties of claustrum neurons are not well understood. Here we use microendoscope imaging in mice to measure activity in populations of genetically-labelled Gnb4+ claustrum neurons. We find that only a small fraction of cells in the anterior claustrum are responsive to visual or auditory stimuli when delivered under passive yet wakeful conditions. In contrast, during a visual behavioral task, the majority of cells in the anterior claustrum are strongly modulated, with separate and spatially intermingled cell populations showing either increases or decreases in activity relative to spontaneous levels. Our results suggest that the Gnb4+ cells in the anterior claustrum do not represent passively presented sensory stimuli; rather, these cells are strongly engaged during behavior associated with sensory-motor transformations.

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bioRxiv2020

Multi-area functional modules mediate feedforward and recurrent processing in visual cortical hierarchy

The visual cortex is organized hierarchically, but the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module is positioned to transmit feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.

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bioRxiv2020

Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology

Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of neurons in the brain. While these two modalities have distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging or electrophysiology. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging. This work explores which data transformations are most useful for explaining these modality-specific discrepancies. We show that the higher selectivity in imaging can be partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could not reconcile differences in responsiveness without sub-selecting neurons based on event rate or level of signal contamination. This suggests that differences in responsiveness more likely reflect neuronal sampling bias or cluster-merging artifacts during spike sorting of electrophysiological recordings, rather than flaws in event detection from fluorescence time series. This work establishes the dominant impacts of the two modalities respective biases on a set of functional metrics that are fundamental for characterizing sensory-evoked responses.

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bioRxiv2020

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet also produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process. Author SummaryAnimals have to adapt to environments with rich dynamics and maintain multiple types of memories. In this study, we focus on a visual change detection task in mice which requires short-term memory. Learning which features need to be maintained in short-term memory can be realized in a recurrent neural network by changing connections in the network, resulting in memory maintenance through persistent activity. However, in biological networks, a large diversity of time-dependent intrinsic mechanisms are also available. As an alternative to persistent neural activity, we find that learning to make use of internal adapting dynamics better matches both the observed neural activity and behavior of animals in this simple task. The presence of a large diversity of temporal traces could be one of the reasons for the diversity of cells observed. We believe that both learning to keep representations of relevant stimuli in persistent activity and learning to make use of intrinsic time-dependent mechanisms exist, and their relative use will be dependent on the exact task.

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bioRxiv2020

Similar visual perception in GCaMP6 transgenic mice despite differences in learning and motivation

To study mechanisms of perception and cognition, neural measurements must be made during behavior. A goal of the Allen Brain Observatory is to map activity in distinct cortical cell classes during visual processing and behavior. Here we characterize learning and performance of five GCaMP6-expressing transgenic lines trained on a visual change detection task. We used automated training procedures to facilitate comparisons across mice. Training times varied, but most transgenic mice learned the task. Motivation levels also varied across mice. To compare mice in similar motivational states we subdivided sessions into over-, under-, and optimally motivated periods. When motivated, the pattern of perceptual decisions were highly correlated across transgenic lines, although overall d-prime was lower in one line labeling somatostatin inhibitory cells. These results provide important context for using these mice to map neural activity underlying perception and behavior.

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bioRxiv2019

Experience shapes activity dynamics and stimulus coding of VIP inhibitory and excitatory cells

Cortical circuits are flexible and can change with experience and learning. However, the effects of experience on specific cell types, including distinct inhibitory types, are not well understood. Here we investigated how excitatory and VIP inhibitory cells in layer 2/3 of mouse visual cortex were impacted by visual experience in the context of a behavioral task. Mice learned to perform an image change detection task with a set of eight natural scene images, viewing these images thousands of times. Subsequently, during 2-photon imaging experiments, mice performed the task with these familiar images and three additional sets of novel images. Novel images evoked stronger overall activity in both excitatory and VIP populations, and familiar images were more sparsely coded by excitatory cells. The temporal dynamics of VIP activity differed markedly between novel and familiar images: VIP cells were stimulus-driven by novel images but displayed ramping activity during the inter-stimulus interval for familiar images. Moreover, when a familiar stimulus was omitted from an expected sequence, VIP cells showed extended ramping activity until the subsequent image presentation. This prominent shift in response dynamics suggests that VIP cells may adopt different modes of processing during familiar versus novel conditions.\n\nHIGHLIGHTSO_LIExperience with natural images in a change detection task reduces overall activity of cortical excitatory and VIP inhibitory cells\nC_LIO_LIEncoding of natural images is sharpened with experience in excitatory neurons\nC_LIO_LIVIP cells are stimulus-driven by novel images but show pre-stimulus ramping for familiar images\nC_LIO_LIVIP cells show strong ramping activity during the omission of an expected stimulus\nC_LI

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bioRxiv2019

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWStructural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic model of the mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically-detailed or point-neurons, with identical network connectivity. Both variants were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. While constructing and tuning these networks to recapitulate experimental data, we identified a set of rules governing cell-class specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single cell abstraction, spatially extended or point-models, both perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

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bioRxiv2018

Distinct higher-order thalamic circuits channel parallel streams of visual information in mice

Higher-order thalamic nuclei, such as the visual pulvinar, play essential roles in cortical function by connecting functionally-related cortical and subcortical brain regions. Yet a coherent framework describing pulvinar function remains elusive due to its anatomical complexity and involvement in diverse cognitive processes. Here we combined large-scale anatomical circuit mapping with high-density electrophysiological recordings to dissect a homolog of pulvinar in mice, the lateral posterior nucleus (LP). We define three broad LP subregions based on correspondence between input/output connectivity and functional properties. These subregions form corticothalamic loops biased towards ventral or dorsal stream cortical areas and contain separate representations of visual space. To reveal which input sources drive LP activity, we silenced visual cortex or superior colliculus and found they drive visual tuning properties in separate LP subregions. Thus, by specifying the driving input sources, functional properties, and downstream targets of LP circuits, our data provide a roadmap for understanding the mechanisms of higher-order thalamic function in vision.

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