Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex

Host-guest Interaction at Molecular Interfaces: Binding of Cucurbit[7]uril on Ferrocenyl Self-assembled Monolayers on Gold

Ferrocene (Fc) encapsulated cucurbit[7]uril (CB[7]) supramolecular host-guest complex  (Fc@CB[7]) as a synthetic recognition pair has been widely adapted for coupling biomolecules and nanomaterials due to its ultra-high binding affinity. In this paper, we have explored the binding of CB[7] on binary ferrocenylundecanethiolate/octanethiolate self-assembled monolayer on gold  (FcC11S-/C8S-Au), a model system to deepen our understanding of host-guest chemistry at molecular interfaces. It has been shown that upon incubation with CB[7] solution, the redox behavior FcC11S-/C8S-Au changes remarkably, i.e., a new pair of peaks appeared at more positive potential with narrowed widths. The ease of quantitation of surface bound-redox species (Fc+/Fc and  Fc+@CB[7]/ Fc@CB[7]) enabled us to determine the thermodynamic formation constant of  Fc@CB[7] at FcC11S-/C8S-Au (7.3±1.8 × 104 M-1). With time-dependent redox responses, we were able to, for the first time, deduce both the binding and dissociation rate constants, 2.8±0.3 × 103  M-1s-1 and 0.08±0.01 s-1, respectively. These results showed substantial differences both thermodynamically and kinetically for the formation of host-guest inclusion complex at molecular interfaces with respect to solution-diffused, homogenous environments

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances (https://www.science.org/doi/10.1126/sciadv.adi1480

Dual functions of the ZmCCT-associated quantitative trait locus in flowering and stress responses under long-day conditions

Gene ontology enrichment of differentially expressed genes in HZ4 and HZ4-NIL in three development stages. (XLS 21 kb

Metabolomic Analysis of Alfalfa (Medicago sativa L.) Root-Symbiotic Rhizobia Responses under Alkali Stress

Alkaline salts (e.g., NaHCO3 and Na2CO3) causes more severe morphological and physiological damage to plants than neutral salts (e.g., NaCl and Na2SO4) due to differences in pH. The mechanism by which plants respond to alkali stress is not fully understood, especially in plants having symbotic relationships such as alfalfa (Medicago sativa L.). Therefore, a study was designed to evaluate the metabolic response of the root-nodule symbiosis in alfalfa under alkali stress using comparative metabolomics. Rhizobium-nodulized (RI group) and non-nodulized (NI group) alfalfa roots were treated with 200 mmol/L NaHCO3 and, roots samples were analyzed for malondialdehydyde (MDA), proline, glutathione (GSH), superoxide dismutase (SOD), and peroxidase (POD) content. Additionally, metabolite profiling was conducted using gas chromatography combined with time-of-flight mass spectrometry (GC/TOF-MS). Phenotypically, the RI alfalfa exhibited a greater resistance to alkali stress than the NI plants examined. Physiological analysis and metabolic profiling revealed that RI plants accumulated more antioxidants (SOD, POD, GSH), osmolytes (sugar, glycols, proline), organic acids (succinic acid, fumaric acid, and alpha-ketoglutaric acid), and metabolites that are involved in nitrogen fixation. Our pairwise metabolomics comparisons revealed that RI alfalfa plants exhibited a distinct metabolic profile associated with alkali putative tolerance relative to NI alfalfa plants. Data provide new information about the relationship between non-nodulized, rhizobium-nodulized alfalfa and alkali resistance

Multifunctional magnetic Fe3O4 nanoparticles combined with chemotherapy and hyperthermia to overcome multidrug resistance

Yanyan Ren1,2,*, Haijun Zhang1,2,*, Baoan Chen1, Jian Cheng1, Xiaohui Cai1, Ran Liu1, Guohua Xia1, Weiwei Wu1, Shuai Wang1, Jiahua Ding1, Chong Gao1, Jun Wang1, Wen Bao1, Lei Wang1, Liang Tian1, Huihui Song1, Xuemei Wang1,2 1Department of Hematology and Oncology, Key Medical Discipline, Jiangsu Province, Zhongda Hospital, and Faculty of Oncology, Medical School, Nanjing, 2State Key Laboratory of Bioelectronics, Southeast University, Nanjing, People's Republic of China*These authors contributed equally to this workBackground: Multidrug resistance in cancer is a major obstacle for clinical therapeutics, and is the reason for 90% of treatment failures. This study investigated the efficiency of novel multifunctional Fe3O4 magnetic nanoparticles (Fe3O4-MNP) combined with chemotherapy and hyperthermia for overcoming multidrug resistance in an in vivo model of leukemia.Methods: Nude mice with tumor xenografts were randomly divided into a control group, and the treatment groups were allocated to receive daunorubicin, 5-bromotetrandrine (5-BrTet) and daunorubicin, Fe3O4-MNP, and Fe3O4-MNP coloaded with daunorubicin and 5-bromotetrandrine (Fe3O4-MNP-DNR-5-BrTet), with hyperthermia in an alternating magnetic field. We investigated tumor volume and pathology, as well as P-glycoprotein, Bcl-2, Bax, and caspase-3 protein expression to elucidate the effect of multimodal treatment on overcoming multidrug resistance.Results: Fe3O4-MNP played a role in increasing tumor temperature during hyperthermia. Tumors became significantly smaller, and apoptosis of cells was observed in both the Fe3O4-MNP and Fe3O4-MNP-DNR-5-BrTet groups, especially in the Fe3O4-MNP-DNR-5-BrTet group, while tumor volumes in the other groups had increased after treatment for 12 days. Furthermore, Fe3O4-MNP-DNR-5-BrTet with hyperthermia noticeably decreased P-glycoprotein and Bcl-2 expression, and markedly increased Bax and caspase-3 expression.Conclusion: Fe3O4-MNP-DNR-5-BrTet with hyperthermia may be a potential approach for reversal of multidrug resistance in the treatment of leukemia.Keywords: magnetic nanoparticles, daunorubicin, 5-bromotetrandrine, multidrug resistance, hyperthermi

Interactions between depositional regime and climate proxies in the northern South China Sea since the Last Glacial Maximum

Sedimentary deposits from the northern South China Sea (SCS) can provide important constraints on past changes in ocean currents and the East Asian summer monsoon (EASM) in this region. However, the interpretation of such records spanning the last deglaciation is complicated because sea-level change may also have influenced the depositional processes and patterns. Here, we present new records of grain size, clay mineralogy, and magnetic mineralogy spanning the past 24 kyr from both shallow and deep-water sediment cores in the northern SCS. Our multi-proxy comparison among multiple cores helps constrain the influence of sea-level change, providing confidence in interpreting the regional climate-forced signals. After accounting for the influence of sea-level change, we find that these multi-proxy records reflect a combination of changes in (a) the strength of the North Pacific Intermediate Water inflow, (b) the EASM strength, and (c) the Kuroshio Current extent. Overall, this study provides new insights into the roles of varying terrestrial weathering and oceanographic processes in controlling the depositional record on the northern SCS margin in response to climate and sea-level fluctuations