Circuit control of competition
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Latent circuit inference from heterogeneous neural responses during cognitive tasks
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
Historical loss weakens competitive behavior by remodeling ventral hippocampal dynamics
Competitive interactions are pervasive within biological populations, where individuals engage in fierce disputes over vital resources for survival. Before the establishment of a social hierarchy within the population, this competition becomes even more intense. Historical experiences of competition significantly influence the competitive performance; individuals with a history of persistent loss are less likely to initiate attacks or win escalated contests. However, it remains unclear how historical loss directly affects the evolution of mental processes during competition and alters responses to ongoing competitive events. Here, we utilized a naturalistic food competition paradigm to track the competitive patterns of mutually unfamiliar competitors and found that a history of loss leads to reduced competitive performance. By tracking the activity of ventral hippocampal neuron ensembles, we identified clusters of neurons that responded differently to behavioral events during the competition, with their reactivity modulated by previous losses. Using a Recurrent Switch Linear Dynamical System (rSLDS), we revealed rotational dynamics in the ventral hippocampus (vHPC) during food competition, where different discrete internal states corresponded to different behavioral strategies. Moreover, historical loss modulates competitive behavior by remodeling the characteristic attributes of this rotational dynamic system. Finally, we found that an evolutionarily conserved glutamate receptor-associated protein, glutamate receptor-associated protein 1 (Grina), plays an important role in this process. By continuously monitoring the association between the attributes of the dynamic system and competitiveness, we found that restoring Grina expression effectively reversed the impact of historical loss on competitive performance. Together, our study reveals the rotational dynamics in the ventral hippocampus during competition and elucidates the underlying mechanisms through which historical loss shapes these processes.
Observation of non-Hermitian topological synchronization
Non-Hermitian topology plays a pivotal role in physical science and technology, exerting a profound impact across various scientific disciplines. Recently, the interplay between topological physics and nonlinear synchronization has aroused a great interest, leading to the emergence of an intriguing phenomenon known as topological synchronization, wherein nonlinear oscillators at boundaries synchronize through topological boundary states. To the best of our knowledge, however, this phenomenon has yet to be experimentally validated, and the study of non-Hermitian topological synchronization remains in its infancy. Here, we investigate non-Hermitian topological synchronization, uncovering the influence of system size and boundary site geometry on synchronization effects. We demonstrate that simply varying the lattice size allows transitions between three distinct types of non-Hermitian topological synchronization. Furthermore, we reveal that the geometry of the boundary sites introduces a degree of freedom, enabling the control over the configuration of non-Hermitian topological synchronization. These findings are experimentally validated using non-Hermitian nonlinear topological circuits. This work significantly broadens the scope of nonlinear non-Hermitian topological physics and opens new avenues for the application of synchronization phenomena in future technologies.
Transitivity and intransitivity in soil bacterial networks
Competition can lead to the exclusion of bacterial taxa when there is a transitive relationship among competitors with a hierarchy of competitive success. However, competition may not prevent bacterial coexistence if competitors form intransitive loops, in which none is able to outcompete all the rest. Both transitive and intransitive competition have been demonstrated in bacterial model systems. However, in natural soil microbial assemblages competition is typically understood as a dominance relationship leading to the exclusion of weak competitors. Here, we argue that transitive and intransitive interactions concurrently determine the structure of soil microbial communities. We explain why pairwise interactions cannot depict competition correctly in complex communities, and propose an alternative through the detection of strongly connected components (SCCs) in microbial networks. We finally analyse the existence of SCCs in soil bacterial communities in two Mediterranean ecosystems, for illustrative purposes only (rather than with the aim of providing a methodological tool) due to current limitations, and discuss future avenues to experimentally test the existence of SCCs in nature.
System-level modeling with temperature compensation for a CMOS-MEMS monolithic calorimetric flow sensing SoC
We present a system-level model with an on-chip temperature compensation technique for a CMOS-MEMS monolithic calorimetric flow sensing SoC. The model encompasses mechanical, thermal, and electrical domains to facilitate the co-design of a MEMS sensor and CMOS interface circuits on the EDA platform. The compensation strategy is implemented on-chip with a variable temperature difference heating circuit. Results show that the linear programming for the low-temperature drift in the SoC output is characterized by a compensation resistor Rc with a resistance value of 748.21 Ω and a temperature coefficient of resistance of 3.037 × 10−3 °C−1 at 25 °C. Experimental validation demonstrates that within an ambient temperature range of 0–50 °C and a flow range of 0–10 m/s, the temperature drift of the sensor is reduced to ±1.6%, as compared to ±8.9% observed in a counterpart with the constant temperature difference circuit. Therefore, this on-chip temperature-compensated CMOS-MEMS flow sensing SoC is promising for low-cost sensing applications such as respiratory monitoring and smart energy-efficient buildings.
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