Learning & memory最新文献

Flexible decision-making requires a balance between exploring features of an environment and exploiting prior knowledge. Behavioral flexibility is typically measured by how long it takes subjects to consistently make accurate choices after reward contingencies switch or task rules change. This measure, however, only allows for tracking flexibility across multiple trials, and does not assess the degree of flexibility. Plus, although increases in decision-making accuracy are strong indicators of learning, other decision-making behaviors have also been suggested as markers of flexibility, such as the on-the-fly decision reversals known as vicarious trial and error (VTE) or switches to a different, but incorrect, strategy. We sought to relate flexibility, learning, and neural activity by comparing choice history-derived evaluation of strategy use with changes in decision-making accuracy and VTE behavior while recording from the medial prefrontal cortex (mPFC) in rats. Using a set-shifting task that required rats to repeatedly switch between spatial decision-making strategies, we show that a previously developed strategy likelihood estimation procedure could identify putative learning points based on decision history. We confirm the efficacy of learning point estimation by showing increases in decision-making accuracy aligned to the learning point. Additionally, we show increases in the rate of VTE behavior surrounding identified learning points. By calculating changes in strategy likelihoods across trials, we tracked flexibility on a trial-by-trial basis and show that flexibility scores also increased around learning points. Further, we demonstrate that VTE behaviors could be separated into indecisive and deliberative subtypes depending on whether they occurred during periods of high or low flexibility and whether they led to correct or incorrect choice outcomes. Field potential recordings from the mPFC during decisions exhibited increased beta band activity on trials with VTE compared to non-VTE trials, as well as increased gamma during periods when learned strategies could be exploited compared to prelearning, exploratory periods. This study demonstrates that increased behavioral flexibility and VTE rates are often aligned to task learning. These relationships can break down, however, suggesting that VTE is not always an indicator of deliberative decision-making. Additionally, we further implicate the mPFC in decision-making and learning by showing increased beta-based activity on VTE trials and increased gamma after learning.

Synaptic potentiation has been linked to learning in sensory cortex, but the connection between this potentiation and increased sensory-evoked neural activity is not clear. Here, we used longitudinal in vivo Ca²⁺ imaging in the barrel cortex of awake mice to test the hypothesis that increased excitatory synaptic strength during the learning of a whisker-dependent sensory-association task would be correlated with enhanced stimulus-evoked firing. To isolate stimulus-evoked responses from dynamic, task-related activity, imaging was performed outside of the training context. Although prior studies indicate that multiwhisker stimuli drive robust subthreshold activity, we observed sparse activation of L2/3 pyramidal (Pyr) neurons in both control and trained mice. Despite evidence for excitatory synaptic strengthening at thalamocortical and intracortical synapses in this brain area at the onset of learning-indeed, under our imaging conditions thalamocortical axons were robustly activated-we observed that L2/3 Pyr neurons in somatosensory (barrel) cortex displayed only modest increases in stimulus-evoked activity that were concentrated at the onset of training. Activity renormalized over longer training periods. In contrast, when stimuli and rewards were uncoupled in a pseudotraining paradigm, stimulus-evoked activity in L2/3 Pyr neurons was significantly suppressed. These findings indicate that sensory-association training but not sensory stimulation without coupled rewards may briefly enhance sensory-evoked activity, a phenomenon that might help link sensory input to behavioral outcomes at the onset of learning.

How does repeated stimulation of mechanoafferents affect feeding motor neurons? Monosynaptic connections from a mechanoafferent population in the Aplysia buccal ganglia to five motor followers with different functions were examined during repeated stimulus trains. The mechanoafferents produced both fast and slow synaptic outputs, which could be excitatory or inhibitory. In contrast, other Aplysia mechanoafferents produce only fast excitation on their followers. In addition, patterns of synaptic connections were different to the different motor followers. Some followers received both fast excitation and fast inhibition, whereas others received exclusively fast excitation. All followers showed strong decreases in fast postsynaptic potential (PSP) amplitude within a stimulus train. Fast and slow synaptic connections were of net opposite signs in some followers but not in others. For one follower, synaptic contacts were not uniform from all subareas of the mechanoafferent cluster. Differences in properties of the buccal ganglia mechanoafferents and other Aplysia mechanoafferents may arise because the buccal ganglia neurons innervate the interior of the feeding apparatus, rather than an external surface, and connect to motor neurons for muscles with different motor functions. Fast connection patterns suggest that these synapses may be activated when food slips, biasing the musculature to release food. The largest slow inhibitory synaptic PSPs may contribute to a delay in the onset of the next behavior. Additional functions are also possible.

Changes caused by learning that a food is inedible in Aplysia were examined for fast and slow synaptic connections from the buccal ganglia S1 cluster of mechanoafferents to five followers, in response to repeated stimulus trains. Learning affected only fast connections. For these, unique patterns of change were present in each follower, indicating that learning differentially affects the different branches of the mechanoafferents to their followers. In some followers, there were increases in either excitatory or inhibitory connections, and in others, there were decreases. Changes in connectivity resulted from changes in the amplitude of excitation or inhibition, or as a result of the number of connections, or of both. Some followers also exhibited changes in either within or between stimulus train plasticity as a result of learning. In one follower, changes differed from the different areas of the S1 cluster. The patterns of changes in connectivity were consistent with the behavioral changes produced by learning, in that they would produce an increase in the bias to reject or to release food, and a decrease in the likelihood to respond to food.

The brain constantly compares past and present experiences to predict the future, thereby enabling instantaneous and future behavioral adjustments. Integration of external information with the animal's current internal needs and behavioral state represents a key challenge of the nervous system. Recent advancements in dissecting the function of the Drosophila mushroom body (MB) at the single-cell level have uncovered its three-layered logic and parallel systems conveying positive and negative values during associative learning. This review explores a lesser-known role of the MB in detecting and integrating body states such as hunger, thirst, and sleep, ultimately modulating motivation and sensory-driven decisions based on the physiological state of the fly. State-dependent signals predominantly affect the activity of modulatory MB input neurons (dopaminergic, serotoninergic, and octopaminergic), but also induce plastic changes directly at the level of the MB intrinsic and output neurons. Thus, the MB emerges as a tightly regulated relay station in the insect brain, orchestrating neuroadaptations due to current internal and behavioral states leading to short- but also long-lasting changes in behavior. While these adaptations are crucial to ensure fitness and survival, recent findings also underscore how circuit motifs in the MB may reflect fundamental design principles that contribute to maladaptive behaviors such as addiction or depression-like symptoms.

Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.

The common fruit fly Drosophila melanogaster provides a powerful platform to investigate the genetic, molecular, cellular, and neural circuit mechanisms of behavior. Research in this model system has shed light on multiple aspects of brain physiology and behavior, from fundamental neuronal function to complex behaviors. A major anatomical region that modulates complex behaviors is the mushroom body (MB). The MB integrates multimodal sensory information and is involved in behaviors ranging from sensory processing/responses to learning and memory. Many genes that underlie brain disorders are conserved, from flies to humans, and studies in Drosophila have contributed significantly to our understanding of the mechanisms of brain disorders. Genetic mutations that mimic human diseases-such as Fragile X syndrome, neurofibromatosis type 1, Parkinson's disease, and Alzheimer's disease-affect MB structure and function, altering behavior. Studies dissecting the effects of disease-causing mutations in the MB have identified key pathological mechanisms, and the development of a complete connectome promises to add a comprehensive anatomical framework for disease modeling. Here, we review Drosophila models of human neurodevelopmental and neurodegenerative disorders via the effects of their underlying mutations on MB structure, function, and the resulting behavioral alterations.

Octopamine, the functional analog of noradrenaline, modulates many different behaviors and physiological processes in invertebrates. In the central nervous system, a few octopaminergic neurons project throughout the brain and innervate almost all neuropils. The center of memory formation in insects, the mushroom bodies, receive octopaminergic innervations in all insects investigated so far. Different octopamine receptors, either increasing or decreasing cAMP or calcium levels in the cell, are localized in Kenyon cells, further supporting the release of octopamine in the mushroom bodies. In addition, different mushroom body (MB) output neurons, projection neurons, and dopaminergic PAM cells are targets of octopaminergic neurons, enabling the modulation of learning circuits at different neural sites. For some years, the theory persisted that octopamine mediates rewarding stimuli, whereas dopamine (DA) represents aversive stimuli. This simple picture has been challenged by the finding that DA is required for both appetitive and aversive learning. Furthermore, octopamine is also involved in aversive learning and a rather complex interaction between these biogenic amines seems to modulate learning and memory. This review summarizes the role of octopamine in MB function, focusing on the anatomical principles and the role of the biogenic amine in learning and memory.

When animals learn the association of a conditioned stimulus (CS) with an unconditioned stimulus (US), later presentation of the CS invokes a representation of the US. When the expected US fails to occur, theoretical accounts predict that conditioned inhibition can accrue to any other stimuli that are associated with this change in the US. Empirical work with mammals has confirmed the existence of conditioned inhibition. But the way it is manifested, the conditions that produce it, and determining whether it is the opposite of excitatory conditioning are important considerations. Invertebrates can make valuable contributions to this literature because of the well-established conditioning protocols and access to the central nervous system (CNS) for studying neural underpinnings of behavior. Nevertheless, although conditioned inhibition has been reported, it has yet to be thoroughly investigated in invertebrates. Here, we evaluate the role of the US in producing conditioned inhibition by using proboscis extension response conditioning of the honeybee (Apis mellifera). Specifically, using variations of a "feature-negative" experimental design, we use downshifts in US intensity relative to US intensity used during initial excitatory conditioning to show that an odorant in an odor-odor mixture can become a conditioned inhibitor. We argue that some alternative interpretations to conditioned inhibition are unlikely. However, we show variation across individuals in how strongly they show conditioned inhibition, with some individuals possibly revealing a different means of learning about changes in reinforcement. We discuss how the resolution of these differences is needed to fully understand whether and how conditioned inhibition is manifested in the honeybee, and whether it can be extended to investigate how it is encoded in the CNS. It is also important for extension to other insect models. In particular, work like this will be important as more is revealed of the complexity of the insect brain from connectome projects.

In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.