Biology subjects

Hofer, S. B.

Publications and source records attributed to Hofer, S. B..

11 recordsLinked to original sources

bioRxiv2026

High-throughput serial two-photon tomography with BakingTray and BrainSaw

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

neuroscience↗

bioRxiv2024

Dynamic consensus-building between neocortical areas via long-range connections

The neocortex is organized into functionally specialized areas. While the functions and underlying neural circuitry of individual neocortical areas are well studied, it is unclear how these regions operate collectively to form percepts and implement cognitive processes. In particular, it remains unknown how distributed, potentially conflicting computations can be reconciled. Here we show that the reciprocal excitatory connections between cortical areas orchestrate neural dynamics to facilitate the gradual emergence of a consensus across areas. We investigated the joint neural dynamics of primary (V1) and higher-order lateromedial (LM) visual areas in mice, using simultaneous multi-area electrophysiological recordings along with focal optogenetic perturbations to causally manipulate neural activity. We combined mechanistic circuit modeling with state-of-the-art data-driven nonlinear system identification, to construct biologically-constrained latent circuit models of the data that we could further interrogate. This approach revealed that long-range, reciprocal excitatory connections between V1 and LM implement an approximate line attractor in their joint dynamics, which promotes activity patterns encoding the presence of the stimulus consistently across the two areas. Further theoretical analyses revealed that the emergence of line attractor dynamics is a signature of a more general principle governing multi-area network dynamics: reciprocal inter-area excitatory connections reshape the dynamical landscape of the network, specifically slowing down the decay of activity patterns that encode stimulus features congruently across areas, while accelerating the decay of inconsistent patterns. This selective dynamic amplification leads to the emergence of multi-dimensional consensus between cortical areas about various stimulus features. Our analytical framework further predicted the timescales of specific activity patterns across areas, which we directly verified in our data. Therefore, by linking the anatomical organization of inter-area connections to the features they reconcile across areas, our work introduces a general theory of multi-area computation.

neuroscience↗

bioRxiv2024

Overwriting an instinct: visual cortex instructs learning to suppress fear responses

Fast instinctive responses to environmental stimuli can be crucial for survival, but are not always optimal. Based on prior experience, animals can thus adapt their behavior and suppress instinctive reactions. However, the neural pathways mediating such ethologically relevant forms of learning remain unclear. We show that posterolateral higher visual areas (plHVAs) are crucial for learning to suppress escapes from innate visual threats through a top-down pathway involving the ventrolateral geniculate nucleus (vLGN). plHVAs are no longer necessary after learning: instead, the learnt behavior relies on plasticity within vLGN populations that exert inhibitory control over fear responses. vLGN neurons receiving input from plHVAs enhance their responses to visual threat stimuli during learning through endocannabinoid-mediated long-term suppression of their inhibitory inputs. We thus reveal the detailed circuit, cellular and synaptic mechanisms underlying experience-dependent suppression of fear responses through a novel corticofugal pathway.

neuroscience↗

bioRxiv2023

A subcortical switchboard for exploratory, exploitatory, and disengaged states

To survive in evolving environments with uncertain resources, animals need to dynamically adapt their behavior and exhibit flexibility in choosing appropriate behavioral strategies, for example, to exploit familiar choices, to explore and acquire novel information, or to disengage altogether. Previous studies have mainly investigated how forebrain regions represent choice costs and values as well as optimal decision strategies during explore/exploit trade-offs. However, the neural mechanisms by which the brain implements alternative behavioral strategies such as exploiting, exploring or disengaging from the environment, remains poorly understood. Here we identify a neural hub critical for flexible switching between behavioral strategies, the median raphe nucleus (MRN). Using cell-type specific optogenetic manipulations, calcium fiber photometry and circuit tracing in mice performing diverse instinctive and learnt behavioral tasks, we found that the MRNs main cell types, GABAergic, glutamatergic (VGluT2-positive), and serotonergic neurons, have complementary functions and drive exploitation, exploration and disengagement, respectively. Suppression of MRN GABAergic neurons, for instance through inhibitory input from lateral hypothalamus which conveys strong positive valence to the MRN, leads to perseverance in current actions and goals, and thus promotes exploitatory behavior. In contrast, activation of MRN VGluT2+ neurons drives exploratory behavior. Activity of serotonergic MRN neurons is necessary for general task engagement. Input from the lateral habenula conveying negative valence suppresses serotonergic MRN neurons, leading to disengagement. These findings establish the MRN as a central behavioral switchboard, uniquely positioned to flexibly control behavioral strategies. These circuits thus may also play an important role in the etiology and possible treatment of major mental pathologies such as depressive or obsessive-compulsive disorders.

neuroscience↗

bioRxiv2023

Cooperative thalamocortical circuit mechanism for sensory prediction errors

The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events1-10. Despite the importance of prediction error signals for various neural computations across multiple brain regions, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit required for generating sensory prediction errors in mouse primary visual cortex (V1). Using calcium imaging with optogenetic manipulations as mice traverse a familiar virtual environment, we show that violation of animals predictions by an unexpected visual stimulus preferentially boosts responses of layer 2/3 V1 neurons most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing a non-specific surprise or difference signal about how the visual input deviates from animals predictions. Selective amplification of unexpected visual input is implemented by a cooperative mechanism requiring thalamic input from the pulvinar, and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing (SOM) inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response-amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Brain-wide representations of prior information in mouse decision-making

The neural representations of prior information about the state of the world are poorly understood. To investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACAd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

neuroscience↗

bioRxiv2021

Dynamic causal communication channels between neocortical areas

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behaviour. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence visual cortical areas had on each other was surprisingly variable over time. Both feedforward and feedback pathways reliably affected different subpopulations of target neurons at different moments during processing of a visual stimulus, resulting in dynamically rotating communication dimensions between the two cortical areas. The influence of feedback on primary visual cortex (V1) became even more dynamic when visual stimuli were associated with a reward, impacting different subsets of V1 neurons within tens of milliseconds. This, in turn, controlled the geometry of V1 population activity in a behaviourally relevant manner. Thus, distributed neural populations interact through dynamically reorganizing and context-dependent communication channels to evaluate sensory information.

neuroscience↗

bioRxiv2021

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance, and across seconds when animals switch attention. While both phenomena are expressed in the same cortical circuit, it is unknown whether they rely on similar mechanisms. We imaged activity of the same neuronal populations in primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the task relevant stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM and VIP inhibitory cell classes. Circuit modelling revealed that cell class-specific top-down inputs best explained attentional modulation, while the reorganization of local functional connectivity accounted for learning related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter time scales.

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bioRxiv2020

Visual intracortical and transthalamic pathways carry distinct information to cortical areas

Sensory processing involves information flow between neocortical areas, assumed to rely on direct intracortical projections. However, cortical areas may also communicate indirectly via higher-order nuclei in the thalamus, such as the pulvinar or lateral posterior nucleus (LP) in the visual system. The fine-scale organization and function of these cortico-thalamo-cortical pathways remains unclear. We find that responses of mouse LP neurons projecting to higher visual areas likely derive from feedforward input from primary visual cortex (V1) combined with information from many cortical and subcortical areas, including superior colliculus. Signals from LP projections to different higher visual areas are tuned to specific features of visual stimuli and their locomotor context, distinct from the signals carried by direct intracortical projections from V1. Thus, visual transthalamic pathways are functionally specific to their cortical target, different from feedforward cortical pathways and combine information from multiple brain regions, linking sensory signals with behavioral context.

neuroscience↗

bioRxiv2018

Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex

In the cerebral cortex, the interaction of excitatory and inhibitory synaptic inputs shapes the responses of neurons to sensory stimuli, stabilizes network dynamics1 and improves the efficiency and robustness of the neural code2-4. Excitatory neurons receive inhibitory inputs that track excitation5-8. However, how this co-tuning of excitation and inhibition is achieved by cortical circuits is unclear, since inhibitory interneurons are thought to pool the inputs of nearby excitatory cells and provide them with non-specific inhibition proportional to the activity of the local network9-13. Here we show that although parvalbumin-expressing (PV) inhibitory cells in mouse primary visual cortex make connections with the majority of nearby pyramidal cells, the strength of their synaptic connections is structured according to the similarity of the cells responses. Individual PV cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This fine-tuning of synaptic weights supports co-tuning of inhibitory and excitatory inputs onto individual pyramidal cells despite dense connectivity between inhibitory and excitatory neurons. Our results indicate that individual PV cells are preferentially integrated into subnetworks of inter-connected, co-tuned pyramidal cells, stabilising their recurrent dynamics. Conversely, weak but dense inhibitory connectivity between subnetworks is sufficient to support competition between them, de-correlating their output. We suggest that the history and structure of correlated firing adjusts the weights of both inhibitory and excitatory connections, supporting stable amplification and selective recruitment of cortical subnetworks.

neuroscience↗