Accurate target detection in natural environments is an important function of the visual systems of vertebrates and has a direct impact on animal survival and environmental adaptation. Existing studies have shown that the mammalian prefrontal cortex plays an important role in target detection. However, target detection mechanisms in brain regions similar to other species, such as the avian nidopallium caudolaterale, have not been well studied. Here, we selected pigeons, known for their excellent target detection ability, as an animal model and studied the dynamic changes in the nidopallium caudolaterale neural network features while they performed a target detection task in a maze. The results showed that the average node degree increased significantly during the target detection process while modularity decreased significantly. This indicated that functional connectivity in pigeon brains was enhanced during the task execution, the frequency of brain interactions increased, and the neural network shifted from distributed processing to more efficient integrated processing. The decoding results based on the average node degree and modularity and the combination of both showed that the accuracy of target decoding corresponding to the combination of both was higher. Taken together, our results confirmed the important role of the above properties for encoding target information. We provided evidence to support the view that the NCL is critical for target detection tasks and that studying key features of its neural network provides a powerful tool for revealing the functional state of the brain.

Numerosity perception is an evolutionarily conserved ability observed across diverse taxa, from insects and fish to birds and primates. Unlike traditional visual categories, numerosity possesses an intrinsic metric structure, making it well suited for quantitatively investigating how categorical representations emerge along the visual hierarchy. While numerical representations are well characterized in high-level associative areas of non-human primates and avian species, neuronal processing of numerosity in upstream regions remains largely unexplored. To address this gap, we recorded single-unit activity in the pigeon entopallium during a delayed match-to-numerosity task. We found a subset of neurons that encoded numerosity independently of the non-numerical feature controlled in the corresponding recording session, exhibiting quasi-monotonic increasing (QMI) or decreasing (QMD) response profiles. Compared with QMI neurons, QMD neurons exhibited a markedly later coding window and stronger category discriminability. Moreover, in both neuronal populations, the normalized response functions and category discriminability were better described by compressed numerosity scales than by a linear scale, with the logarithmic scale showing a modest overall advantage. These findings help refine current theoretical frameworks for how numerosity information is extracted and represented along the avian visual processing hierarchy, providing a comparative basis for cross-species research.

Understanding how birds perceive and recognize objects remains a fundamental question in neuroscience. The entopallium, a key node in the avian tectofugal pathway, is involved in complex visual processing, yet its functional organization remains incompletely understood. Here, we systematically mapped entopallium neuronal responses in pigeons using controlled visual stimuli varying in color, shape, and motion. Entopallium neurons exhibited strong hue selectivity invariant to luminance, pronounced orientation tuning to shape stimuli, and robust direction selectivity to motion. Spatial analyses revealed distinct functional segregation, with color-selective neurons localized anteroventrally, shape-selective neurons dorsally, and motionselective neurons posteriorly. Despite this segregation, a subset of neurons exhibited joint tuning across stimulus dimensions, suggesting an integrated yet modular organizational scheme. Notably, entopallium neurons exhibited a moderate level of feature integration, sharing key characteristics with early to intermediate stages of mammalian visual processing. Together, these findings establish the entopallium as a central hub for multidimensional visual processing in birds and provide new insights into the convergent evolution of sophisticated visual systems across vertebrates.

SSA serves as a fundamental mechanism for optimizing sensory processing by selectively reducing neural responses to prevalent stimuli. While well-characterized in mammals, its evolutionary conservation and functional impact in avian species, particularly on neural coding and decoding, remain largely unexplored. Here, we investigated visual SSA in the avian nucleus Imc by simultaneously analyzing LFP spectral power and single-unit spiking activity in pigeons. During exposure to constant-order moving dot stimuli, repeated presentations induced sustained suppression of both low-gamma-band (31–70 Hz) and high-gamma (71–120 Hz) LFP power, which recovered upon novel stimulus introduction. A similar adaptive pattern was observed in spike firing rates. Stimulus-specific indices revealed that over 80% of Imc neurons exhibited SSA in gamma-band LFP energy, compared to 65.6% based on spike rates. To quantify the functional consequence, we employed machine learning decoders to assess the discrimination of moving targets. Prior to adaptation, decoding based on the spike rate achieved up to 81.8% accuracy, outperforming LFP-based decoding. Critically, SSA may impair the encoding of directional information in sustained responses, reducing the decoding accuracy of classifiers using spike rates, LFP features, or their combinations to near-baseline levels. These findings indicate that gamma-band LFP energy in the Imc primarily reflects stimulus salience, whereas spike rates concurrently encode both identity and salience. This study elucidates the differential roles SSA and its effect on multiplexed neural codes, advancing the understanding of invariant visual representation in the avian brain.

As the data features acquired nowadays are becoming increasingly complex and distinctive, traditional single-layer spiking neural networks (SNNs) are increasingly struggling to train such data. Deep SNN (DSNN) can effectively provide biological representations and effective architectures for the above-mentioned data. The weights of traditional deep artificial neural networks (DANN) can be transferred into those of equivalent DSNN via conversion method. However, the shortcomings such as the non-differentiability of spiking information, poor interpretability, and excessive randomness make it challenging to train DSNN using backpropagation-based techniques. To improve the performance of DSNN, enhance its feature learning capability, and reduce the complexity of parameter design, this paper proposes a hybrid DSNN structure integrated with group method of data handling (GMDH) network and an SNN classifier for pattern classification tasks. In the hybrid GMDH-SNN model, complex categorical data are first decomposed into several simplified modes via Variational mode decomposition (VMD). Subsequently, the multi-layer GMDH module can effectively extract discriminative features from the decomposed input data through self-organizing inductive learning. This design not only improves the interpretability and reduces the structural complexity of DSNN, but also achieves competitive recognition accuracy comparable to DANN. Extensive classification benchmarks are used to validate the effectiveness of the GMDH-SNN model. Nonparametric statistical test, feature visualization results and experimental outcomes reveal that the GMDH-SNN with VMD not only outperforms some traditional classifiers, recently proposed ANN-based classifiers, DSNN classifiers, and GMDH-based classifier, but also reduces the complexity of DSNN.