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The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains. [Display omitted] • The ability to compare visual quantity is observed in naive animals • Units tuned to ratios and differences arise spontaneously in untrained networks • Feedforward wiring of monotonic neural activity induces quantity-comparison tuning • Slightly different nonlinear response functions induce distinct types of tuning Visual quantity comparisons with two distinct measures, ratio and difference, are observed even in newborn animals. Here, Lee et al. find that single units tuned to ratios and differences in visual quantities arise spontaneously in completely untrained neural networks and that they enable the network to perform quantity comparison tasks. [ABSTRACT FROM AUTHOR]

Amygdala circuitry encodes associations between conditioned stimuli and aversive unconditioned stimuli and also controls fear expression. However, whether and how non-threatening information for unpaired conditioned stimuli (CS−) is discretely processed remains unknown. The fear expression toward CS− is robust immediately after fear conditioning but then becomes negligible after memory consolidation. The synaptic plasticity of the neural pathway from the lateral to the anterior basal amygdala gates the fear expression of CS−, depending upon neuronal PAS domain protein 4 (Npas4)-mediated dopamine receptor D4 (Drd4) synthesis, which is precluded by stress exposure or corticosterone injection. Herein, we show cellular and molecular mechanisms that regulate the non-threatening (safety) memory consolidation, supporting the fear discrimination. [Display omitted] • Synaptic depotentiation of the LA-to-aBA pathway reduces fear expression toward CS− • Upregulation of Npas4-Drd4 axis mediates the consolidation of CS− memory • Consolidation of CS− memory can be disrupted by exposure to stressors or enhanced CORT level • A subset of LA neurons, which express Npas4, regulate fear expression in response to CS− Ko et al. identified cellular and molecular mechanisms underlying fear discrimination between CS− and CS+. Specifically, a subpopulation of amygdala neurons expressing Npas4 induce synaptic depotentiation in the LA-to-aBA pathway by safety signal-dependent induction of Npas4 and subsequent upregulation of its downstream target Drd4, which offsets fear expression to CS−. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in neuroanatomical tracing methods have helped unravel complicated neural connectivity in whole-brain tissue at single-cell resolution. However, in most cases, analysis of brain images remains dependent on highly subjective and sample-specific manual processing, preventing precise comparison across sample animals. In the present study, we introduce AMaSiNe, software for automated mapping of single neurons in the standard mouse brain atlas with annotated regions. AMaSiNe automatically calibrates misaligned and deformed slice samples to locate labeled neuronal positions from multiple brain samples into the standardized 3D Allen Mouse Brain Reference Atlas. We exploit the high fidelity and reliability of AMaSiNe to investigate the topographic structures of feedforward projections from the lateral geniculate nucleus to the primary visual area by reconstructing rabies-virus-injected brain slices in 3D space. Our results demonstrate that distinct organization of neural projections can be precisely mapped using AMaSiNe. • Software (AMaSiNe) is developed for automated neural mapping in a reference brain • Misaligned and deformed slices are calibrated using the minimum number of samples • Multiple brain data are precisely compared, mapped onto a common 3D space • Topographic neural projection from LGd to VISp is observed accurately Song et al. develop software for automated and calibrated single-neuron mapping (AMaSiNe) into the standardized 3D Allen Mouse Brain Reference Atlas, which enables precise comparison of multiple brain data on a standard brain atlas and reveals spatial organization of neural projections to the primary visual area. [ABSTRACT FROM AUTHOR]

In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry. • Determinants of columnar and salt-and-pepper patterns in visual cortex are sought • Retino-cortical mapping ratio solely predicts cortical patterns across species • Nyquist sampling model explains sharp parametric division of patterns • Controlled simulation of retino-cortical sampling ratio reproduces observed patterns Across mammalian species, orientation tuning in the primary visual cortex is arranged in different manners, such as columnar orientation map in primates or salt-and-pepper organization in rodents. Here, Jang et al. propose that the retina-to-cortex sampling ratio is the key factor in determining the organization of the orientation tuning. [ABSTRACT FROM AUTHOR]

Neurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons or if they represent a continuum of variation within a single class of cells. Herein, we show that simple and complex cells may arise commonly from the feedforward projections from the retina. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON–OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. From data in cats, we observed that clusters of simple and complex receptive fields correlate topographically with orientation maps, which supports our model prediction. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning. • Simple and complex cells in V1 can arise simultaneously from retinal afferents. • Simple/complex cells are organized into periodic clusters across visual cortex. • Simple/complex clusters are topographically correlated with orientation maps. • Development of clustered cells in V1 is explained by the Paik–Ringach model. [ABSTRACT FROM AUTHOR]

The brain successfully performs visual object recognition with a limited number of hierarchical networks that are much shallower than artificial deep neural networks (DNNs) that perform similar tasks. Here, we show that long-range horizontal connections (LRCs), often observed in the visual cortex of mammalian species, enable such a cost-efficient visual object recognition in shallow neural networks. Using simulations of a model hierarchical network with convergent feedforward connections and LRCs, we found that the addition of LRCs to the shallow feedforward network significantly enhances the performance of networks for image classification, to a degree that is comparable to much deeper networks. We found that a combination of sparse LRCs and dense local connections dramatically increases performance per wiring cost. From network pruning with gradient-based optimization, we also confirmed that LRCs could emerge spontaneously by minimizing the total connection length while maintaining performance. Ablation of emerged LRCs led to a significant reduction of classification performance, which implies these LRCs are crucial for performing image classification. Taken together, our findings suggest a brain-inspired strategy for constructing a cost-efficient network architecture to implement parsimonious object recognition under physical constraints such as shallow hierarchical depth. • Long-range connections (LRCs) enable recognition of various objects under constraints. • LRCs added to shallow networks enhance performance comparable to deeper networks. • LRCs can emerge spontaneously while balancing performance and wiring cost. • LRCs reveal a biological strategy for a cost-efficient neural network architecture. [ABSTRACT FROM AUTHOR]

The serial-position effect in working memory is considered important for studying how a sequence of sensory information can be retained and manipulated simultaneously in neural memory circuits. Here, via a precise analysis of the primacy and recency effects in human psychophysical experiments, we propose that stable and flexible codings take distinct roles of retaining and updating information in working memory, and that their combination induces serial-position effects spontaneously. We found that stable encoding retains memory to induce the primacy effect, while flexible encoding used for learning new inputs induces the recency effect. A model simulation based on human data, confirmed that a neural network with both flexible and stable synapses could reproduce the major characteristics of serial-position effects. Our new prediction, that the control of resource allocation by flexible–stable coding balance can modulate memory performance in sequence-specific manner, was supported by pre-cued memory performance data in humans. [ABSTRACT FROM AUTHOR]

A hallmark of the prefrontal cortex (PFC) is flexible representation of task-relevant variables. To investigate roles of different interneuron subtypes in this process, we examined discharge characteristics and inactivation effects of parvalbumin (PV)- and somatostatin (SST)-expressing neurons in the mouse PFC during probabilistic classical conditioning. We found activity patterns and inactivation effects differed between PV and SST neurons: SST neurons conveyed cue-associated quantitative value signals until trial outcome, whereas PV neurons maintained valence signals even after trial outcome. Also, PV, but not SST, neuronal population showed opposite responses to reward and punishment. Moreover, inactivation of PV, but not SST, neurons affected outcome responses and activity reversal of pyramidal neurons. Modeling suggested opposite responses of PV neurons to reward and punishment as an efficient mechanism for facilitating rapid cue-outcome contingency learning. Our results suggest primary roles of mPFC PV neurons in rapid value updating and SST neurons in predicting values of upcoming events. [ABSTRACT FROM AUTHOR]