Targeting a disintegrin and metalloprotease (ADAM) 17-CD122 axis enhances CD8+ T cell effector differentiation and anti-tumor immunity.

CD8+ T cell immune responses are regulated by multi-layer networks, while the post-translational regulation remains largely unknown. Transmembrane ectodomain shedding is an important post-translational process orchestrating receptor expression and signal transduction through proteolytic cleavage of membrane proteins. Here, by targeting the sheddase A Disintegrin and Metalloprotease (ADAM)17, we defined a post-translational regulatory mechanism mediated by the ectodomain shedding in CD8+ T cells. Transcriptomic and proteomic analysis revealed the involvement of post-translational regulation in CD8+ T cells. T cell-specific deletion of ADAM17 led to a dramatic increase in effector CD8+ T cell differentiation and enhanced cytolytic effects to eliminate pathogens and tumors. Mechanistically, ADAM17 regulated CD8+ T cells through cleavage of membrane CD122. ADAM17 inhibition led to elevated CD122 expression and enhanced response to IL-2 and IL-15 stimulation in both mouse and human CD8+ T cells. Intriguingly, inhibition of ADAM17 in CD8+ T cells improved the efficacy of chimeric antigen receptor (CAR) T cells in solid tumors. Our findings reveal a critical post-translational regulation in CD8+ T cells, providing a potential therapeutic strategy of targeting ADAM17 for effective anti-tumor immunity.

The Transcription Factor Zfp335 Promotes Differentiation and Survival of Effector Th1 Cells by Directly Regulating Lmna Expression.

Ag-specific effector CD4+ T cells play a crucial role in defending against exogenous pathogens. However, the mechanisms governing the differentiation and function of IFN-γ-producing effector CD4+ Th1 cells in immune responses remain largely unknown. In this study, we elucidated the pivotal role of zinc finger protein 335 (Zfp335) in regulating effector Th1 cell differentiation and survival during acute bacterial infection. Mice with Zfp335 knockout in OT-II cells exhibited impaired Ag-specific CD4+ T cell expansion accompanied by a significant reduction in resistance to Listeria infection. Furthermore, Zfp335 deficiency restricted the effector CD4+ Th1 cell population and compromised their survival upon Listeria challenge. The expression of T-bet and IFN-γ was accordingly decreased in Zfp335-deficient Th1 cells. Mechanistically, Zfp335 directly bound to the promoter region of the Lmna gene and regulated its expression. Overexpression of Lmna was able to rescue the survival and function of Zfp335-deficient effector Th1 cells. Therefore, our study provides novel insights into the mechanisms governing effector Th1 cell differentiation and survival during acute infection.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex.

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications.

PURPOSE: To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. METHODS: A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan-Meier curves were used to compare the DFS between different groups pf patients based on the predictors. RESULTS: The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858-0.960) and 0.833 (95% CI: 0.675-0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669-0.836) and 0.704 (95% CI: 0.532-0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p < .001). CONCLUSION: MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time.

Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE: To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE: Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION: 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE: 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT: AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS: The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION: An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Zfp335 establishes eTreg lineage and neonatal immune tolerance by targeting Hadha-mediated fatty acid oxidation.

Regulatory T cells (Tregs) are instrumental in maintaining immune tolerance and preventing destructive autoimmunity, but how heterogeneous Treg populations are established remains largely unknown. Here, we show that Zfp335 deletion in Tregs failed to differentiate into effector Tregs (eTregs) and lose Treg-suppressive function and that KO mice exhibited early-onset lethal autoimmune inflammation with unrestricted activation of conventional T cells. Single-cell RNA-Seq analyses revealed that Zfp335-deficient Tregs lacked a eTreg population and showed dramatic accumulation of a dysfunctional Treg subset. Mechanistically, Zfp335-deficient Tregs displayed reduced oxidative phosphorylation and dysfunctional mitochondrial activity. Further studies revealed that Zfp335 controlled eTreg differentiation by regulating fatty acid oxidation (FAO) through direct targeting of the FAO enzyme Hadha. Importantly, we demonstrate a positive correlation between ZNF335 and HADHA expression in human eTregs. Our findings reveal that Zfp335 controls FAO-driven eTreg differentiation to establish immune tolerance.

Stimulus-dependent functional network topology in mouse visual cortex.

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Prolonged fasting induces significant germ cell loss in chickens after hatching.

Germ cell loss is a crucial biological event during germ cell development. The number of female germ cells determines the reproductive performance and egg production of hens. Various intrinsic and extrinsic factors affect germ cell loss, such as germ cell nest breakdown in early life and nutritional deficiencies during daily husbandry. Here, we examined the effect of fasting on the germ cell number of chicks. The results showed that 72 h fasting resulted in a higher germ cell loss than that by 24 h fasting in chicks. The RNA-seq analysis revealed that the genes of ribosome pathway were down-regulated and the biological processes of protein processing in endoplasmic reticulum were inhibited in starved chicks. Furthermore, in female chicks treated with 72 h fasting, the qPCR of ovaries showed down-regulation of ribosome-related genes, and transmission electron microscopy imaging of ovaries showed fewer ribosomes. The blood biochemical indices indicated that 72 h fasting reduced the liver functions and affected the glucose metabolism, lipid metabolites and ion metabolites. In summary, the present results concluded negative impacts on the germ cell pool by prolonged fasting in the early life of chicks and manifested that adequate management should be cared for fasted time for breeding.

Associations between in vitro , in vivo and in silico cell classes in mouse primary visual cortex.

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex.

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.

OBJECTIVES: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS: A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.

Association Between Abdominal Adipose Tissue Distribution and Risk of Endometrial Cancer: A Case-Control Study.

Background: Obesity contributes to endometrial cancer (EC). However, it is not clear whether the distribution of adipose tissue affects the occurrence of endometrial carcinoma. This study aimed to evaluate the relationship between abdominal adipose tissue distribution and EC. Methods: We designed a case-control study with 115 women with EC and a control group. The total abdominal adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue were measured by single slice computerized tomography at the level of umbilicus. Univariate and multivariate logistic regression models were used to calculate odds ratios (ORs) for the risk of EC associated with adipose tissue distribution. Furthermore, we analyzed the correlation between adipose tissue distribution and clinicopathologic features of endometrial carcinoma. Results: Multivariate analysis showed that a larger visceral adipose tissue ratio was associated with an increased risk of EC after adjusting for body mass index (BMI) and diabetes (OR = 1.046, 95% confidence interval = [1.008-1.079]). The ratio of International Federation of Obstetrics and Gynecology (FIGO) stage I and type I EC was higher in EC patients with larger visceral adipose tissue (84.5% vs 63.2%, P = .009; 91.4% vs 75.4%, P = .021). There was a higher positive ratio of progesterone receptor in EC patients with a larger subcutaneous adipose tissue area (91.2% vs 77.6%; P = .044). Conclusions: Higher visceral adipose tissue ratio, independent of BMI, was associated with an increased risk of EC. Therefore, this study demonstrated that women with normal BMI, but abnormal abdominal adipose tissue distribution, have an increased risk for EC.

Quantitative proteomics analysis of chicken embryos reveals key proteins that affect right gonadal degeneration in females.

In birds, embryonic gonads of females develop in a way different from mammals, with the left one develops into a functional ovary, while the right one degenerates during embryogenesis. Here, we examined the proteomics profiles of the female and male left and right gonads at embryonic day 6.5 (E6.5) with the label free tandem mass spectrometry proteomics technique. The relative protein abundance of the left and right gonads of female and male embryos was determined to identify their differential proteins. Overall, a total of 7726 proteins were identified, of which 79 and 54 proteins were significantly different in female and male right gonads compared with female left gonads and male left gonads respectively. Bioinformatics analysis showed that the proteins DMRT1, ZFPM2, TSHZ3 were potentially associated with the degeneration of the right gonads in female embryos. The proteomics in this study provide clues for further elucidation of the pathways of sex determination, sex differentiation, and right gonadal degeneration in birds.

Dietary Lasia spinosa Thw. improves reproductive performance of aged roosters.

The application of artificial insemination is particularly, owing to which breeder animals are considered an important resource in breeding farms. However, the reproductive performance of roosters typically declines with age, and the economic loss experienced by breeders is attributable to this shortened reproductive lifespan. Lasia spinosa Thw. (LST) reportedly improved reproductive capacity in male rodents. The objective of this study was to investigate the effects of LST on the reproductive performance of aged roosters. Male Guangxi Partridge chicken (mean weight, 3032.41 ± 34.48 g; age, 500 days; n = 72) randomly received the following three dietary treatments: LST0 group (a basal diet), LST2 group (a basal diet with 2% LST powder), and LST4 group (a basal diet with 4% LST powder). Computer-aided sperm analysis revealed that dietary LST supplementation significantly improved semen volume, sperm motility, and concentration. Furthermore, the most potent effects were observed in the treatment group with the administration of 2% LST, which significantly improved the weight of the testes. Hematoxylin-eosin staining revealed the increase in diameter of the seminiferous tubule and height of the seminiferous tubule epithelium possibly caused as a result of LST treatment. A significant increase in fructose and glucose concentrations were observed in the testis and seminal plasma; in addition, a significant increase was observed in the α-glycosidase levels in the testis and spermatozoa. However, the monoaldehyde levels in the spermatozoa appeared to decline significantly. Additionally, the fertility rate increased significantly following 2% LST supplementation. RNA-seq analysis revealed that 34 and 16 unigenes were upregulated and downregulated, respectively, in testicular tissues from roosters that received dietary supplementation of 2% LST. The assigned functions of the unigenes revealed that LST primarily influenced the mechanisms underlying catalytic activity and cellular processes. Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that spermatogenesis-related pathways were significantly enriched, including ABC transporters, ribosome biogenesis in eukaryotes, and VEGF, cAMP, and ErbB signaling pathways.

Detecting and monitoring tumors in orthotopic colorectal liver metastatic animal models with high-resolution ultrasound.

The ability to noninvasively detect and monitor the growth of orthotopic liver transplantation tumors is critical for replicating advanced colorectal cancer liver metastases (CRLMs) in animal models. We assessed the use of high-resolution ultrasound (HRU) to monitor CRLMs transplanted using various cell concentrations. Sixty BALB/c female mice were randomly divided into 3 groups, and murine colonic CT26 cells were injected into the left liver lobe at concentrations of 1 × 102 (group 1), 1 × 103 (group 2), or 1 × 104 (group 3). Tumor presentation, location, number, size, shape, and echogenicity were assessed daily with 24-MHz center frequency HRU starting 6 days after injection. Animals were sacrificed when the largest tumor was ≥ 1 cm in diameter. Sensitivity, specificity, and area under curve (AUC) of CRLMs diagnosed with HRU were calculated using receiver operating characteristic curve analysis. In group 1, 94% of mice formed < 5 tumors, and 41% formed a single tumor. Tumors were first detected with HRU on day 12 in group 1, day 10 in group 2, and day 7 in group 3; tumor volume doubling times were 14-15 days, 11-12 days, and 7-8 days, respectively. With a long diameter threshold of 2.4 mm, diagnostic sensitivity and specificity of HRU were 94.1% and 88.7%, respectively, and the AUC was 0.962. These findings suggest that HRU can be used to accurately detect and monitor the growth of CRLMs in an orthotopic transplantation mouse model, especially when a lower concentration of cells is used.

Multi-regional module-based signal transmission in mouse visual cortex.

The visual cortex is hierarchically organized, yet the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow both between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module transmits feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.

How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?

Humans learn from visual inputs at multiple timescales, both rapidly and flexibly acquiring visual knowledge over short periods, and robustly accumulating online learning progress over longer periods. Modeling these powerful learning capabilities is an important problem for computational visual cognitive science, and models that could replicate them would be of substantial utility in real-world computer vision settings. In this work, we establish benchmarks for both real-time and life-long continual visual learning. Our real-time learning benchmark measures a model's ability to match the rapid visual behavior changes of real humans over the course of minutes and hours, given a stream of visual inputs. Our life-long learning benchmark evaluates the performance of models in a purely online learning curriculum obtained directly from child visual experience over the course of years of development. We evaluate a spectrum of recent deep self-supervised visual learning algorithms on both benchmarks, finding that none of them perfectly match human performance, though some algorithms perform substantially better than others. Interestingly, algorithms embodying recent trends in self-supervised learning - including BYOL, SwAV and MAE - are substantially worse on our benchmarks than an earlier generation of self-supervised algorithms such as SimCLR and MoCo-v2. We present analysis indicating that the failure of these newer algorithms is primarily due to their inability to handle the kind of sparse low-diversity datastreams that naturally arise in the real world, and that actively leveraging memory through negative sampling - a mechanism eschewed by these newer algorithms - appears useful for facilitating learning in such low-diversity environments. We also illustrate a complementarity between the short and long timescales in the two benchmarks, showing how requiring a single learning algorithm to be locally context-sensitive enough to match real-time learning changes while stable enough to avoid catastrophic forgetting over the long term induces a trade-off that human-like algorithms may have to straddle. Taken together, our benchmarks establish a quantitative way to directly compare learning between neural networks models and human learners, show how choices in the mechanism by which such algorithms handle sample comparison and memory strongly impact their ability to match human learning abilities, and expose an open problem space for identifying more flexible and robust visual self-supervision algorithms.

Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology.

Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex.

Temporal continuity of object identity is a feature of natural visual input and is potentially exploited - in an unsupervised manner - by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here, we investigated whether plasticity of individual IT neurons underlies human core object recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior-linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude, and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed - at least in part - by naturally occurring unsupervised temporal contiguity experience.

A bear is a bear, regardless of how far away it is, or the angle at which we view it. And indeed, the ability to recognize objects in different contexts is an important part of our sense of vision. A brain region called the inferior temporal (IT for short) cortex plays a critical role in this feat. In primates, the activity of groups of IT cortical nerve cells correlates with recognition of different objects ­ and conversely, suppressing IT cortical activity impairs object recognition behavior. Because these cells remain selective to an item despite changes of size, position or orientation, the IT cortex is thought to underly the ability to recognise an object regardless of variations in its visual properties. How does this tolerance arise? A property called 'temporal continuity' is thought to be involved ­ in other words, the fact that objects do not blink in and out of existence. Studies in nonhuman primates have shown that temporal continuity can indeed reshape the activity of nerve cells in the IT cortex, while behavioural experiments with humans suggest that it affects the ability to recognize objects. However, these two sets of studies used different visual tasks, so it is still unknown if the cellular processes observed in monkey IT actually underpin the behavioural effects shown in humans. Jia et al. therefore set out to examine the link between the two. In the initial experiments, human volunteers were given, in an unsupervised manner, a set of visual tasks designed similarly to the previous tests in nonhuman primates. The participants were presented with continuous views of the same or different objects at various sizes, and then given tests of object recognition. These manipulations resulted in volunteers showing altered size tolerance over time. Aiming to test which cellular mechanism underpinned this behavioural effect, Jia et al. built a model that simulated the plasticity of individual IT cells and the IT networks, to predict the changes of object recognition observed in the volunteers. A high predictability of the model revealed that the plasticity in IT cortex did indeed account for the behavioral changes in the volunteers. These results shed new light on the role that temporal continuity plays in vision, refining our understanding of the way the IT cortex helps to assess the world around us.