Spatial integration during performance in pigeons

Investigating pigeon circovirus infection in a pigeon farm: molecular detection, phylogenetic analysis and complete genome analysis

BACKGROUND

Pigeon circovirus infections in pigeons (Columba livia domestica) have been reported worldwide. Pigeons should be PiCV-free when utilized as qualified experimental animals. However, pigeons can be freely purchased as experimental animals without any clear guidelines to follow. Herein, we investigated the status quo of PiCV infections on a pigeon farm in Beijing, China, which provides pigeons for experimental use.

RESULTS

PiCV infection was verified in at least three types of tissues in all forty pigeons tested. A total of 29 full-length genomes were obtained and deposited in GenBank. The whole genome sequence comparison among the 29 identified PiCV strains revealed nucleotide homologies of 85.8-100%, and these sequences exhibited nucleotide homologies of 82.7-98.9% as compared with those of the reference sequences. The cap gene displayed genetic diversity, with a wide range of amino acid homologies ranging from 64.5% to 100%. Phylogenetic analysis of the 29 full-genome sequences revealed that the PiCV strains in this study could be further divided into four clades: A (17.2%), B (10.4%), C (37.9%) and D (34.5%). Thirteen recombination events were also detected in 18 out of the 29 PiCV genomes obtained in this study. Phylogenetic research using the rep and cap genes verified the recombination events, which occurred between clades A/F, A/B, C/D, and B/D among the 18 PiCV strains studied.

CONCLUSIONS

In conclusion, PiCV infection, which is highly genetically varied, is extremely widespread on pigeon farms in Beijing. These findings indicate that if pigeons are to be used as experimental animals, it is necessary to evaluate the impact of PiCV infection on the results.

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories-"ramping" and "population-clock" theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches. To time different intervals, ramping neurons adjust their slopes, ramping steeply for short intervals and vice versa. In contrast, according to "population-clock" models, multiple neurons track time as a group, and each neuron can fire nonlinearly. As each neuron changes its rate at each point in time, a distinct pattern of activity emerges across the population. To time different intervals, the brain learns the population patterns that coincide with key events. Both model categories have empirical support. However, they often differ in plausibility when applied to certain behavioral effects. Specifically, behavioral data indicate that the timing system has a rich computational capacity, allowing observers to spontaneously compute novel intervals from previously learned ones. In population-clock theories, population patterns map to time arbitrarily, making it difficult to explain how different patterns can be computationally combined. Ramping models are viewed as more plausible, assuming upstream circuits can set the slope of ramping neurons according to a given computation. Critically, recent studies suggest that neurons with nonlinear firing profiles often scale to time different intervals-compressing for shorter intervals and stretching for longer ones. This "temporal scaling" effect has led to a hybrid-theory where, like a population-clock model, population patterns encode time, yet like a ramping neuron adjusting its slope, the speed of each neuron's firing adapts to different intervals. Here, we argue that these "relative" population-clock models are as computationally plausible as ramping theories, viewing population-speed and ramp-slope adjustments as equivalent. Therefore, we view identifying these "speed-control" circuits as a key direction for evaluating how the timing system performs computations. Furthermore, temporal scaling highlights that a key distinction between different neural models is whether they propose an absolute or relative time-representation. However, we note that several behavioral studies suggest the brain processes both scales, cautioning against a dichotomy.

Systematic Analysis of Pigeons' Discrimination of Pixelated Stimuli: A Hierarchical Pattern Recognition System Is Not Identifiable

Pigeons learned to discriminate two different patterns displayed with miniature light-emitting diode arrays. They were then tested with 84 interspersed, non-reinforced degraded pattern pairs. Choices ranged between 100% and 50% for one or other of the patterns. Stimuli consisting of few pixels yielded low choice scores whereas those consisting of many pixels yielded a broad range of scores. Those patterns with a high number of pixels coinciding with those of the rewarded training stimulus were preferred and those with a high number of pixels coinciding with the non-rewarded training pattern were avoided; a discrimination index based on this correlated 0.74 with the pattern choices. Pixels common to both training patterns had a minimal influence. A pixel-by-pixel analysis revealed that eight pixels of one pattern and six pixels of the other pattern played a prominent role in the pigeons’ choices. These pixels were disposed in four and two clusters of neighbouring locations. A summary index calculated on this basis still only yielded a weak 0.73 correlation. The individual pigeons’ data furthermore showed that these clusters were a mere averaging mirage. The pigeons’ performance depends on deep learning in a midbrain-based multimillion synapse neuronal network. Pixelated visual patterns should be helpful when simulating perception of patterns with artificial networks.