Archerfish number discrimination

Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (Toxotes jaculatrix) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 vs. 6 elements; Exp. 2: 2 vs. 3 elements); these were controlled for several combinations of physical variables (elements’ size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 vs. 3, 5 vs. 8 and 6 vs. 9 in Exp. 1; 3 vs. 4, 3 vs. 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.

Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.

Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Feature extraction and selection are important parts of motor imagery electroencephalogram (EEG) decoding and have always been the focus and difficulty of brain-computer interface (BCI) system research. In order to improve the accuracy of EEG decoding and reduce model training time, new feature extraction and selection methods are proposed in this paper. First, a new spatial-frequency feature extraction method is proposed. The original EEG signal is preprocessed, and then the common spatial pattern (CSP) is used for spatial filtering and dimensionality reduction. Finally, the filter bank method is used to decompose the spatially filtered signals into multiple frequency subbands, and the logarithmic band power feature of each frequency subband is extracted. Second, to select the subject-specific spatial-frequency features, a hybrid feature selection method based on the Fisher score and support vector machine (SVM) is proposed. The Fisher score of each feature is calculated, then a series of threshold parameters are set to generate different feature subsets, and finally, SVM and cross-validation are used to select the optimal feature subset. The effectiveness of the proposed method is validated using two sets of publicly available BCI competition data and a set of self-collected data. The total average accuracy of the three data sets achieved by the proposed method is 82.39%, which is 2.99% higher than the CSP method. The experimental results show that the proposed method has a better classification effect than the existing methods, and at the same time, feature extraction and feature selection time also have greater advantages.

Optimization of Nano-Topography Distribution by Compensation Grinding

Optical surfaces are required to have high form accuracy and smoothness. The form accuracy must be below 50 nm. Form accuracy is currently on the order of several tens of nanometers or less; however, further improvement is required. To improve form accuracy, compensation grinding is performed based on form measurement results. However, when the form error is small, a small periodical waviness occurs on the ground surface, which is known as nano-topography. This waviness cannot be compensated for using conventional compensation methods because the nano-topography distributions are not reproducible. A previous study showed that grinding conditions affect the spatial frequency of nano-topography. Therefore, in this study, optimum grinding conditions are estimated from the view point of nano-topography distributions, and the grinding conditions are compensated to optimize these distributions.

Sensitivity of Visual System in 5-Day “Dry” Immersion With High-Frequency Electromyostimulation

The aim of this work was to study the sensitivity of the visual system in 5-day “dry” immersion with a course of high-frequency electromyostimulation (HFEMS) and without it. “Dry” immersion (DI) is one of the most effective models of microgravity. DI reproduces three basic effects of weightlessness: physical inactivity, support withdrawal and elimination of the vertical vascular gradient. The “dry” immersion included in the use of special waterproof and highly elastic fabric on of immersion in a liquid similar in density to the tissues of the human body. The sensitivity of the visual system was assessed by measuring contrast sensitivity and magnitude of the Müller-Lyer illusion. The visual contrast sensitivity was measured in the spatial frequency range from 0.4 to 10.0 cycles/degree. The strength of visual illusion was assessed by means of motor response using “tracking.” Measurements were carried out before the start of immersion, on the 1st, 3rd, 5th days of DI, and after its completion. Under conditions of “dry” immersion without HFEMS, upon the transition from gravity to microgravity conditions (BG and DI1) we observed significant differences in contrast sensitivity in the low spatial frequency range, whereas in the experiment with HFEMS—in the medium spatial frequency range. In the experiment without HFEMS, the Müller-Lyer illusion in microgravity conditions was absent, while in the experiment using HFEMS it was significantly above zero at all stages. Thus, we obtained only limited evidence in favor of the hypothesis of a possible compensating effect of HFEMS on changes in visual sensitivity upon the transition from gravity to microgravity conditions and vice versa. The study is a pilot and requires further research on the effect of HFEMS on visual sensitivity.

Intermediate, Wholistic Shape Representation in Object Recognition: A Pre-Attentive Stage of Processing?

Evidence is presented for intermediate, wholistic visual representations of objects and non-objects that are computed online and independent of visual attention. Short-term visual priming was examined between visually similar shapes, with targets either falling at the (valid) location cued by primes or at another (invalid) location. Object decision latencies were facilitated when the overall shapes of the stimuli were similar irrespective of whether the location of the prime was valid or invalid, with the effects being equally large for object and non-object targets. In addition, the effects were based on the overall outlines of the stimuli and low spatial frequency components, not on local parts. In conclusion, wholistic shape representations based on outline form, are rapidly computed online during object recognition. Moreover, activation of common wholistic shape representations prime the processing of subsequent objects and non-objects irrespective of whether they appear at attended or unattended locations. Rapid derivation of wholistic form provides a key intermediate stage of object recognition.

Binocular Summation Is Intact in Intermittent Exotropia After Surgery

Purpose: To determine binocular summation of surgically treated intermittent exotropia (IXT) patients by measuring the contrast threshold.Methods: We recruited 38 surgically treated IXT patients aged 8–24 years and 20 age-matched healthy controls. All participants had normal or corrected-to-normal visual acuity (Snellen ≥ 20/20) in both eyes. The IXT patients had undergone the surgery at least a year prior to the study. Twenty-one of them obtained good alignment and 17 experienced a recurrence of exotropia. We measured the observers' monocular and binocular contrast sensitivities (CS) at six spatial frequencies (1.5, 3, 6, 12, 18, 24 cycles/degree) as an index of visual information processing at the threshold level. Binocular summation was evaluated against a baseline model of simple probability summation based on the CS at each spatial frequency and the area under the log contrast sensitivity function (AULCSF).Results: The exo-deviation of IXTs with good alignment was −6.38 ± 3.61 prism diopters (pd) at 33 cm and −5.14 ± 4.07 pd at 5 m. For the patients with recurrence, it was −23.47 ± 5.53 pd and −21.12 ± 4.28 pd, respectively. There was no significant difference in the binocular summation ratio (BSR) between the surgically treated IXT patients, including those with good alignment and recurrence, and normal controls at each spatial frequency [F(2,55) = 0.416, P = 0.662] and AULCSF [F(2,55) = 0.469, P = 0.628]. In addition, the BSR was not associated with stereopsis (r = −0.151, P = 0.365).Conclusion: Our findings of normal contrast sensitivity binocular summation ratio in IXT after surgical treatment suggest that the ability of the visual cortex in processing binocular information is intact at the contrast threshold level.

Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex

Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.