Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.Comment: Accepted to 3rd Workshop on Deep Learning in Medical Image Analysis (http://cs.adelaide.edu.au/~dlmia3/

Prefrontal cortex output circuits guide reward seeking through divergent cue encoding

The prefrontal cortex is a critical neuroanatomical hub for controlling motivated behaviours across mammalian species. In addition to intra-cortical connectivity, prefrontal projection neurons innervate subcortical structures that contribute to reward-seeking behaviours, such as the ventral striatum and midline thalamus. While connectivity among these structures contributes to appetitive behaviours, how projection-specific prefrontal neurons encode reward-relevant information to guide reward seeking is unknown. Here we use in vivo two-photon calcium imaging to monitor the activity of dorsomedial prefrontal neurons in mice during an appetitive Pavlovian conditioning task. At the population level, these neurons display diverse activity patterns during the presentation of reward-predictive cues. However, recordings from prefrontal neurons with resolved projection targets reveal that individual corticostriatal neurons show response tuning to reward-predictive cues, such that excitatory cue responses are amplified across learning. By contrast, corticothalamic neurons gradually develop new, primarily inhibitory responses to reward-predictive cues across learning. Furthermore, bidirectional optogenetic manipulation of these neurons reveals that stimulation of corticostriatal neurons promotes conditioned reward-seeking behaviour after learning, while activity in corticothalamic neurons suppresses both the acquisition and expression of conditioned reward seeking. These data show how prefrontal circuitry can dynamically control reward-seeking behaviour through the opposing activities of projection-specific cell populations

Optimization of interneuron function by direct coupling of cell migration and axonal targeting

Neural circuit assembly relies on the precise synchronization of developmental processes, such as cell migration and axon targeting, but the cell-autonomous mechanisms coordinating these events remain largely unknown. Here we found that different classes of interneurons use distinct routes of migration to reach the embryonic cerebral cortex. Somatostatin-expressing interneurons that migrate through the marginal zone develop into Martinotti cells, one of the most distinctive classes of cortical interneurons. For these cells, migration through the marginal zone is linked to the development of their characteristic layer 1 axonal arborization. Altering the normal migratory route of Martinotti cells by conditional deletion of Mafb—a gene that is preferentially expressed by these cells—cell-autonomously disrupts axonal development and impairs the function of these cells in vivo. Our results suggest that migration and axon targeting programs are coupled to optimize the assembly of inhibitory circuits in the cerebral cortex

Regulation of neuronal input transformations by tunable dendritic inhibition

Transforming synaptic input into action potential output is a fundamental function of neurons. The pattern of action potential output from principal cells of the mammalian hippocampus encodes spatial and nonspatial information, but the cellular and circuit mechanisms by which neurons transform their synaptic input into a given output are unknown. Using a combination of optical activation and cell type–specific pharmacogenetic silencing in vitro, we found that dendritic inhibition is the primary regulator of input-output transformations in mouse hippocampal CA1 pyramidal cells, and acts by gating the dendritic electrogenesis driving burst spiking. Dendrite-targeting interneurons are themselves modulated by interneurons targeting pyramidal cell somata, providing a synaptic substrate for tuning pyramidal cell output through interactions in the local inhibitory network. These results provide evidence for a division of labor in cortical circuits, where distinct computational functions are implemented by subtypes of local inhibitory neurons