Biology subjects

Blot, A.

Publications and source records attributed to Blot, A..

8 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↗

bioRxiv2025

Barcoded Rabies In Situ Connectomics for high-throughput reconstruction of neural circuits

Sequencing of oligonucleotide barcodes holds promise as a high-throughput approach for reconstructing synaptic connectivity at scale (1). Rabies viruses can act as a vehicle for barcode transmission, thanks to their ability to spread between synaptically connected cells (2, 3). However, applying barcoded rabies viruses to map synaptic connections in vivo has proved challenging (4-7). Here, we develop Barcoded Rabies In Situ Connectomics (BRISC) for high-throughput connectivity mapping in the mouse brain. To ensure that the majority of post-synaptic "starter" neurons are uniquely labeled with distinct barcode sequences, we first generated libraries of rabies viruses with sufficient diversity to label >1000 neurons uniquely. To minimize the probability of barcode transmission between starter neurons, we developed a strategy to tightly control their density. We then applied BRISC to map inputs of single neurons in the primary visual cortex (V1). Using in situ sequencing, we read out the expression of viral barcodes in rabies-infected neurons, while preserving spatial information. We then matched barcode sequences between starter and presynaptic neurons, mapping the inputs of 385 neurons and identifying 7,814 putative synaptic connections. The resulting connectivity matrix revealed layer- and cell-type-specific local connectivity rules and topographic organization of long-range inputs to V1. These results show that BRISC can simultaneously resolve the synaptic connectivity of hundreds of neurons while preserving spatial information, enabling reconstruction of neural circuits at an unprecedented scale.

neuroscience↗

bioRxiv2025

PhotoNeuro: A compact photodetector for synchronization of visual stimulus presentation during behavioral experiments in neuroscience.

Presenting visual stimuli in neuroscience experiments often requires the combination of analogue signals that carry information about the visual cue presented on the LCD display. Such signals are often sensed by photodetectors and recorded in analogue to digital converter (ADC) acquisition boards. The use of open-source visual programming languages such as Bonsai is becoming more and more popular. They are often used in combination with other open-source hardware such as Arduino development boards. These microcontroller-based boards can be used to automate behavioural experiments: e.g., actuate valves and motors and acquire analogue signals on their ADC channels. LCDs and other modern display allow fast presentation of arbitrary visual stimuli and are widely used for psychophysics and neuroscience experiments. However, most displays do not provide hardware timestamping options and are intrinsically nonlinear. Solving this limitation often requires a direct recording of the light emitted by the display with a photodiode. Such photodetectors are are often amplified at higher voltages and hard to integrate in most common recording systems that use microcontrollers. The other drawback commonly found by neuroscience researchers in commercial devices is the relatively big footprint that the sensor occupies on the screen which, ideally should be minimised so not to interfere with the stimuli presentation. In this paper we present a small footprint photodetector that can be easily replicated and operates at 5V making it suitable to use with common development boards and the visual programming language Bonsai that is commonly used for experiment creation and control. Additionally, we share a version that includes four photodiodes in small area (400 mm2). Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@7bafcforg.highwire.dtl.DTLVardef@5353ccorg.highwire.dtl.DTLVardef@a29f47org.highwire.dtl.DTLVardef@86f5d2org.highwire.dtl.DTLVardef@74da5f_HPS_FORMAT_FIGEXP M_TBL C_TBL

neuroscience↗

bioRxiv2024

A depth map of visual space in the primary visual cortex

Depth perception is essential for visually-guided behavior. Computer vision algorithms use depth maps to encode distances in three-dimensional scenes but it is unknown whether such depth maps are generated by animal visual systems. To answer this question, we focused on motion parallax, a depth cue relying on visual motion resulting from movement of the observer. As neurons in the mouse primary visual cortex (V1) are broadly modulated by locomotion, we hypothesized that they may integrate vision- and locomotion-related signals to estimate depth from motion parallax. Using recordings in a three-dimensional virtual reality environment, we found that conjunctive coding of visual and self-motion speeds gave rise to depth-selective neuronal responses. Depth-selective neurons could be characterized by three-dimensional receptive fields, responding to specific virtual depths and retinotopic locations. Neurons tuned to a broad range of virtual depths were found across all sampled retinotopic locations, showing that motion parallax generates a depth map of visual space in V1.

neuroscience↗

bioRxiv2021

Learning shapes cortical dynamics to enhance integration of relevant sensory input

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input. HighlightsO_LIA new theoretical principle links recurrent circuit dynamics to optimal sensory coding C_LIO_LIPredicts that high-SNR input dimensions activate slowly decaying modes of dynamics C_LIO_LIPopulation dynamics in primary visual cortex realign during learning as predicted C_LIO_LIStimulus-specific changes in E-I connectivity in recurrent circuits explain realignment C_LI

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.

neuroscience↗

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

Cerebellar contribution to preparatory activity in motor neocortex

In motor-related areas of the neocortex, preparatory neuronal activity predictive of specific actions emerges seconds in advance of movement and is maintained by a positive feedback loop with the thalamus. A major source of excitatory drive to the motor thalamus is the cerebellum, which has been implicated in coordination and timing of learned actions. However, the cerebellar contribution to neocortical signals coupled to movement planning remains poorly understood. Here we show that cerebellar output neurons in the dentate nucleus exhibit preparatory ramping activity in anticipation of expected rewards in a virtual reality conditioning task, a profile similar to that recorded in anterolateral motor neocortex (ALM). The preparatory activity in the dentate nucleus is controlled by a disinhibitory circuit involving inhibitory Purkinje cells in the cerebellar cortex, many of which suppress their firing in advance of rewards. Silencing the activity in the dentate nucleus by photoactivation of Purkinje cells caused robust, short-latency suppression in the majority of ALM neurons exhibiting preparatory activity. Thus, preparatory signals in motor neocortex require the output of the cerebellum. We suggest that the reciprocal circuitry between neocortex and cerebellum generates the sequence of activity required for planning and temporal coordination of learned, goal- directed actions.

neuroscience↗