Frontiers in Neural Circuits最新文献

Hippocampal networks required for associative memory formation are involved in cue- and context-dependent threat conditioning. The hippocampus is functionally heterogeneous at its dorsal and ventral poles, and recent investigations have focused on the specific roles required from each sub-region for associative conditioning. Cumulative evidence suggests that contextual and emotional information is processed by the dorsal and ventral hippocampus, respectively. However, it is not well understood how these two divisions engage in threat conditioning with cues of different sensory modalities. Here, we compare the involvement of the dorsal and ventral hippocampus in two types of threat conditioning: olfactory and auditory. Our results suggest that the dorsal hippocampus encodes contextual information and is activated upon recall of an olfactory threat memory only if contextual cues are relevant to the threat. Overnight habituation to the context eliminates dorsal hippocampal activation, implying that this area does not directly support cue-dependent threat conditioning. The ventral hippocampus is activated upon recall of olfactory, but not auditory, threat memory regardless of habituation duration. Concurrent activation of the piriform cortex is consistent with its direct connection with the ventral hippocampus. Together, our study suggests a unique role of the ventral hippocampus in olfactory threat conditioning.

Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input. The discrepancy between a prediction and the actual input results in prediction error. GMs then execute action policies predicted to minimize the prediction error. The free-energy principle provides a rationale for AIF by stipulating that information-processing open systems must constantly minimize their free energy (through suppressing the cumulative prediction error) to avoid decay. The theory of homeostasis and allostasis has a similar logic. Homeostatic set points are expectations of living organisms. Discrepancies between set points and actual states generate stress. For optimal functioning, organisms avoid stress by preserving homeostasis. Theories of AIF and homeostasis have recently converged, with AIF providing a formal account for homeo- and allostasis. In this paper, we present bacterial chemotaxis as molecular AIF, where mutual constraints by extero- and interoception play an essential role in controlling bacterial behavior supporting homeostasis. Extending this insight to the brain, we propose a conceptual model of the brain homeostatic GM, in which we suggest partition of the brain GM into cognitive and physiological homeostatic GMs. We outline their mutual regulation as well as their integration based on the free-energy principle. From this analysis, affect and self-efficacy emerge as the main regulators of the cognitive homeostatic GM. We suggest fatigue and depression as target neurocognitive phenomena for studying the neural mechanisms of such regulation.

Parental care plays a crucial role in the physical and mental well-being of mammalian offspring. Although sexually naïve male mice, as well as certain strains of female mice, display aggression toward pups, they exhibit heightened parental caregiving behaviors as they approach the time of anticipating their offspring. In this Mini Review, I provide a concise overview of the current understanding of distinct limbic neural types and their circuits governing both aggressive and caregiving behaviors toward infant mice. Subsequently, I delve into recent advancements in the understanding of the molecular, cellular, and neural circuit mechanisms that regulate behavioral plasticity during the transition to parenthood, with a specific focus on the sex steroid hormone estrogen and neural hormone oxytocin. Additionally, I explore potential sex-related differences and highlight some critical unanswered questions that warrant further investigation.

If a full visual percept can be said to be a ‘hypothesis’, so too can a neural ‘prediction’ – although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of ‘predictive coding’, at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, ‘precision’ neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a ‘prediction’? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects’ expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.