A Circuit Mechanism Linking Past And Future Learning Through Shifts in Perception Part 1

Long-term memory formation is energetically costly. Neural mechanisms that guide an animal to identify fruitful associations therefore have important survival benefits. Here, we elucidate a circuit mechanism in Lymnaea, which enables memory to shape new memory formation through changes in perception. Specifically, strong classical conditioning drives a positive shift in perception that facilitates the robust learning of a subsequent and otherwise ineffective weak association. 

Long-term memory is one of the most important types of memory in the human brain. It is the basis for people to learn new knowledge and experiences in their daily lives. Through long-term memory, people can better understand and deal with various problems in life and improve their intelligence and thinking ability. Memory is the ability of the human brain to operate memory, which determines the amount and quality of information that people can remember and save.

There is an inseparable relationship between long-term memory and memory. Only with better memory can people better use and apply long-term memory and better acquire knowledge and experience from it. At the same time, long-term memory can also promote the improvement of memory. Because if people often learn new knowledge in their lives and continue to accumulate and apply new knowledge, they can gradually improve their memory level.

To make long-term memory and memory function more effectively, people need to pay attention to some methods. First of all, pay attention to details and attention, and pay attention to and remember important information as much as possible in study, work, and life. Secondly, you can improve your memory through continuous training, such as reading, playing games, or regular exercise to improve the brain's memory ability. In addition, maintaining a positive attitude and curiosity, and being willing to constantly accept new knowledge and experiences are also important factors in improving long-term memory and memory.

To sum up, long-term memory and memory are a pair of mutually reinforcing factors. Only by giving full play to both can we benefit more in life and exercise our intelligence and thinking abilities. Therefore, let us actively learn, pay attention to details, remain curious, accept new knowledge and experiences, and constantly improve our memory and long-term memory capabilities. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material with many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

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Circuit dissection approaches reveal the neural control network responsible, characterized by a mutual inhibition motif. This both sets the perceptual state and acts as the master controller for gating new learning. Pharmacological circuit manipulation in vivo fully substitutes for strong paradigm learning, shifting the network into a more receptive state to enable subsequent weak paradigm learning. 

Thus, perceptual change provides a conduit to link past and future memory storage. We propose that this mechanism alerts animals to learning-rich periods, lowering the threshold for new memory acquisition.

INTRODUCTION

Learning and long-term memory (LTM) formation are important but energetically costly processes, particularly for animals on a limited metabolic budget (1, 2). As such, neural strategies that could help identify the most relevant or fruitful associations are likely to be highly beneficial for survival. Neuronal mechanisms to support such guided learning, however, are not established. A candidate to help instruct new memory formation is an animal’s prior experience, but how past and future learning might be linked is poorly understood. 

One potential insight comes from human studies indicating that prior learning tunes perception via attentional modulation, which guides perceptual processing in future tasks and enhances performance (3–5). This raises the intriguing possibility that perceptual changes could be an important conduit that serves to link past and new learning.

Simple invertebrate models have been extensively exploited to elucidate fundamental neural mechanisms for learning and action selection (6–14). Here, we used Lymnaea, an established molluscan system (15–22), to directly probe the relationship between memory, perception, and new learning. This animal can rapidly form associations between stimuli after a single training session with the resulting memory lasting for several weeks (23, 24). 

The neurons in the central nervous system are large, identifiable, and accessible, and the underlying circuits for the expression of the conditioned response have been extensively characterized (15, 25–27), making this a compelling choice for detailed behavioral and circuit-level investigations. Here, we subjected animals to two types of appetitive classical conditioning paradigms, a strong one that induces a stable LTM and a weak one that does not. When presented sequentially (strong then weak), the conditioned stimulus used in the weak training paradigm subsequently produces a robust conditioned response. 

Using both behavioral and electrophysiological readouts, we demonstrate that while the stimuli used during weak training are originally perceived by the animal as ambiguous/bistable (both positive and negative), the prior strong learning shifts this perception to a stable positive state. We also identify the control microcircuit responsible, demonstrating that it works by controlling competitive interactions between two antagonistic feeding behaviors, enabling the threshold for positive learning associations to be dynamically altered. 

Pharmacological replication of the altered perception in vivo can fully substitute for the acquisition of the strong memory, biasing the network toward a more receptive state to enable new positive associations. Our study reveals a key mechanism for coupling past and future learning through changes in perception. We hypothesize that this serves to signal to the animal a potentially learning-rich environment, allowing new positive associations to form to cues that would otherwise be ignored.

RESULTS

Previous learning facilitates new memory acquisition

Lymnaea forms robust and long-lasting memories after only a single pairing of a neutral and rewarding stimulus (23, 24). Here, we exploited this system to examine how different learning events interact. We established two different training protocols: The first used a single pairing of amyl acetate [AA; the conditioned stimulus (CS)] and 0.33% sucrose [S; the unconditioned stimulus (US)], inducing an LTM (17) that was expressed as an increased feeding response to the CS tested 4 or 24 hours after training (Fig. 1, A and B; fig. S1A; and movie S1). 

Based on the robustness of the learned outcome, we classified this as a “strong” training paradigm. In the second protocol, we paired an alternative CS, gamma-nonalactone (GNL), which did not differ from AA in its effect as a neutral stimulus (fig. S1B), with a lower concentration of sucrose (s; 0.11%, US). This pairing was insufficient to induce a memory when tested at 1, 3, 4, or 24 hours after training (Fig. 1C and Fig. S1C) and therefore was classified as a “weak” training paradigm. Next, we examined whether interactions between these training protocols might influence the outcome of learning. To do this, we established a double training paradigm in which animals received the strong training followed, after a 4-hour interval, by the weak training (Fig. 1D). Notably, using this protocol, we found that a robust LTM was now formed because of the weak training (Fig. 1D and fig. S2A). 

This outcome was not explained by stimulus generalization since strong training (AA + S) alone did not change the GNL response compared to naïve animals (Fig. 1D and Fig. S2A). Moreover, memory after strong training (AA + S) was not disrupted by the weak training (GNL + s), confirming that no retroactive interference between the memories occurred (Fig. 1D and Fig. S2A). The effect was also not dependent on which CS was used in the strong or weak training; reversing the paradigm to use GNL as the CS in strong training and AA as the CS in weak training also produced the same outcome (fig. S2B). In a test of the persistence of weak training memory following strong training, we demonstrated that the memory trace was still present 48 hours later (fig. S2C). Presentation of the strong US alone 4 hours before the weak training did not increase the GNL response (fig. S2D), suggesting that the effect was specifically dependent on the acquisition of the previous strong memory. 

To further test the necessity of prior strong memory acquisition, we performed a double training paradigm in which animals received two weak pieces of training 4 hours apart (AA + s/ GNL + s or GNL + s/AA + s) and found that there was no memory for either CS (fig. S2E). Next, we considered the importance of the timing interval between strong and weak training. We found that weak training was sufficient to induce an LTM when presented between 30 min and 4 hours after strong training, suggesting that there is a critical post-learning time window in which new memory acquisition can be facilitated (fig. S3A). 

Last, we determined the importance of the temporal order of training, specifically whether strong training could retroactively enhance a memory trace induced by weak training. We found that weak training was insufficient to induce an LTM when it preceded strong training by 4 hours (Fig. 1E and Fig. S3B), showing that the effectiveness of weak training critically depends on prior strong training. Together, these results suggest that previous acquisition and consolidation of a strong memory act to lower the threshold for new appetitive learning.

Previous learning changes the perception of a new learning event

How does prior strong learning enhance new memory formation? Given the strict order of training needed (strong followed by weak), we hypothesized that the strong memory might be changing the animal’s perception of the combination of the CS + US during subsequent weak training, increasing the positive value of the weak training such that the association becomes enhanced. 

To test this idea, we took advantage of the fact that the perception of a presented stimulus, from positive to negative, can be read out directly in Lymnaea by quantifying the number of ingestion and egestion events that occur (Fig. 2A) (16). During the pairing of the CS + US in the weak training protocol, animals performed a mixture of ingestion and egestion bites, often flip-flopping between bouts of the two behaviors (Fig. 2B). By contrast, when this was preceded by strong training, the ratio of ingestion to egestion was significantly higher (Fig. 2, C and D) and more animals performed no egestion bites at all in response to the weak training (Fig. 2E). 

Furthermore, to examine perceptual stability during weak training, we compared pairs of bites elicited during the CS + US presentation to determine whether they switched between states (ingest/egest and egest/ingest) or remained stable (ingest/ingest and egest/egest). This revealed a significant reduction in the transition probability between states, suggesting that prior strong training shifts, and stabilizes, the perception of the training (fig. S4, A to E). Presentation of the strong training US alone, 4 hours before weak training, did not cause any change in the ingestion/egestion behaviors compared to animals that did not receive the strong training US, demonstrating that the energetic value of high sucrose alone during strong training was not the source of the bias toward ingestion behavior (fig. S5, A to D). 

Furthermore, prior weak training (AA + s) 4 hours earlier also did not change the perception of subsequent weak training, confirming that the acquisition of a past strong memory is necessary to alter the perception of the weak training (fig. S5, A to D). We also ruled out the possibility that previous strong or weak training was simply altering the responsiveness to the CS or US used in the weak training (fig. S6, A to D). Together, our findings demonstrate that naïve animals perceive the CS + US used during weak training as an ambiguous/bistable stimulus and that previous strong training shifts the animal’s perception of the weak training to a stable state, biasing behavioral expression toward positive (ingestion) versus negative (egestion) behavior.

Shifts in the perception of weak training are driven by changes in the network state

We next set out to identify the neural changes that underlie the shift in perception of the CS + US used during weak training that is correlated with new memory acquisition. This is readily achievable in Lymnaea thanks to the highly accessible and well-defined neural circuits that control its feeding behavior. Specifically, we made intracellular recordings from the phase-switching feeding motoneuron, B11, which provides a readout of the relative activity in both the ingestion and egestion circuits and thus serves as an in vitro measure of the internal network state (Fig. 2F). In isolated brains, where sensory pathways were absent, we compared this readout in both naïve animals versus those that had previously received strong training. 

Matching our findings in vivo, preparations from naïve animals showed many rhythmic activity patterns known to underlie egestion cycles (fictive egestion), while those that had experienced strong training were almost exclusively biased toward the electrophysiological expression of ingestion cycles (fictive ingestion; Fig. 2, G to J, and fig. S7A). Furthermore, by examining pairs of cycles, we found that naïve preparations transition between ingestion and egestion cycles at a higher rate than preparations that have received strong training (fig. S7, B to E). Preparations that received prior weak training showed no significant difference in their fictive motor programs compared to naïve preparations (fig. S7, F to K). This provides compelling evidence to suggest that strong training elicits a learning-induced shift and stabilization in the network state.

Next, we tested whether strong training altered the “responsiveness” of the feeding circuitry. To do this, we stimulated the medial lip nerve (MLN), a projection pathway carrying chemosensory input from the lip structures to the central nervous system (CNS) (28), which is known to drive strong fictive feeding (29). This stimulation does not encode specific information about the nature of the stimulus but activates all chemosensory projections, allowing us to test whether strong training induced a generalized sensitization effect regardless of the appetitive cue. We monitored this on B9, a motoneuron that provides a readout of feeding cycles in the circuit, and the cerebral giant cell (CGC), a key identified modulatory neuron that receives excitation from appetitive cues (30). We found that neither the number of feeding cycles nor the CGC spike frequency were significantly different between naïve and trained preparations (fig. S8, A to D) following lip nerve stimulation.

Together, these results demonstrate that strong training does not elicit a generalized sensitization of the animal that enhances appetitive cue responses. Rather, there is a learning-induced shift in the network state after strong training that biases the network toward the generation of ingestion cycles. We propose that this underlies the altered perception that is observed in these animals, in turn enhancing their capability to acquire new memories.

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