Sensory prediction errors increase coding efficiency in mouse visual cortex through gain amplification

The efficiency of sensory coding is affected both by past events (adaptation) and by expectation of future events (prediction). Here we employed a novel visual stimulus paradigm to determine whether expectation influences orientation selectivity in the primary visual cortex. We used two-photon calcium imaging (GCaMP6f) in awake mice viewing visual stimuli with different levels of predictability. The stimuli consisted of sequences of grating stimuli that randomly shifted in orientation or systematically rotated with occasionally unexpected rotations. At the single neuron and population level, there was significantly enhanced orientation-selective response to unexpected visual stimuli through a boost in gain, which was prominent in awake mice but also present to a lesser extent under anesthesia. We implemented a computational model to demonstrate how neuronal responses were best characterized when adaptation and expectation parameters were combined. Our results demonstrated that adaptation and prediction have unique signatures on activity of V1 neurons.

A Scalable and Modular Automated Pipeline for Stitching of Large Electron Microscopy Datasets

Serial section Electron Microscopy can produce high throughput imaging of large biological specimen volumes. The high-resolution images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. A high fidelity volume assembly is required to correctly reconstruct neural anatomy and synaptic connections. It involves seamless 2D stitching of the images within a serial section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP(Assembly Stitching and Alignment Pipeline) that is scalable and parallelized to work with distributed systems. The pipeline is built on top of the Render [18] services used in the volume assembly of the brain of adult Drosophila melanogaster [2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. The modularity of ASAP allows for easy adaptation to new algorithms without significant changes to the workflow. The software pipeline includes a complete set of tools to do stitching, automated quality control, 3D section alignment, and rendering of the assembled volume to disk. We also implemented a workflow engine that executes the volume assembly workflow in an automated fashion triggered following the transfer of raw data. ASAP has been successfully utilized for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25]. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Complex systems can be defined by "sloppy" dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and "stiff" dimensions whose changes result in considerable modifications. In the case of the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of spiking dynamics in the normal range despite a diversity of inputs and both short- and long-term changes to connectivity. Using simulations on neural networks with spiking matched to murine visual cortex, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Large-scale algorithmically-generated connectivity parameter values revealed that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across a range of different input classes with self-sustaining synaptic architectures occupying a smaller subspace as compared to the other input classes. We also find that experimentally estimated connectivity probabilities from mouse visual cortex are similarly stiff and sloppy when compared to the architectures that we identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Moreover, this study provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in neocortex.

Sensory prediction errors increase coding efficiency in mouse visual cortex through gain amplification

The efficiency of sensory coding is affected both by past events (adaptation) and by expectation of future events (prediction). Here we employed a novel visual stimulus paradigm to determine whether expectation influences orientation selectivity in the primary visual cortex. We used two-photon calcium imaging (GCaMP6f) in awake mice viewing visual stimuli with different levels of predictability. The stimuli consisted of sequences of grating stimuli that randomly shifted in orientation or systematically rotated with occasionally unexpected rotations. At the single neuron and population level, there was significantly enhanced orientation-selective response to unexpected visual stimuli through a boost in gain, which was prominent in awake mice but also present to a lesser extent under anesthesia. We implemented a computational model to demonstrate how neuronal responses were best characterized when adaptation and expectation parameters were combined. Our results demonstrated that adaptation and prediction have unique signatures on activity of V1 neurons.

CNN MouseNet: A biologically constrained convolutional neural network model for mouse visual cortex

Convolutional neural networks trained on object recognition derive inspiration from the neural architecture of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the deep hierarchical organization of primates, the visual system of the mouse has a shallower arrangement. Since mice and primates are both capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a novel framework for building a biologically constrained convolutional neural network model of the mouse visual cortex. The architecture and structural parameters of the network are derived from experimental measurements, specifically the 100-micrometer resolution interareal connectome, the estimates of numbers of neurons in each area and cortical layer, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. Using a well-studied image classification task as our working example, we demonstrate the computational capability of this mouse-sized network. Given its relatively small size, MouseNet achieves roughly 2/3rds the performance level on ImageNet as VGG16. In combination with the large scale Allen Brain Observatory Visual Coding dataset, we use representational similarity analysis to quantify the extent to which MouseNet recapitulates the neural representation in mouse visual cortex. Importantly, we provide evidence that optimizing for task performance does not improve similarity to the corresponding biological system beyond a certain point. We demonstrate that the distributions of some physiological quantities are closer to the observed distributions in the mouse brain after task training. We encourage the use of the MouseNet architecture by making the code freely available.