תובנה - Computational neuroscience - # One-shot learning of paired association navigation

One-shot learning of multiple paired associations in a navigation task using biologically plausible neural schemas

Q: How can the proposed schemas be extended to account for other types of one-shot learning, such as in language or object recognition tasks?

The proposed schemas, particularly the LEARN FLAVOR-LOCATION and LEARN METRIC REPRESENTATION schemas, can be adapted for one-shot learning in language and object recognition tasks by modifying the input representations and the associative memory mechanisms. For language tasks, the schemas could incorporate linguistic features as sensory cues, allowing the agent to associate specific words or phrases with their meanings or contexts. This could involve using embeddings that capture semantic relationships, enabling the agent to generalize from a single exposure to new vocabulary or grammatical structures. In object recognition, the LEARN FLAVOR-LOCATION schema could be extended to include visual features extracted from images, allowing the agent to form associations between visual stimuli and their corresponding labels or categories. The neural implementation could leverage convolutional neural networks (CNNs) to process visual inputs, while the associative memory could be structured to store and retrieve object representations based on visual cues. By integrating these modifications, the schemas could facilitate rapid learning in diverse domains, enhancing the agent's ability to adapt to new tasks with minimal training data.

Q: What are the limitations of the current schema-based approach, and how can it be improved to handle more complex environments and tasks?

The current schema-based approach has several limitations, particularly in its ability to generalize to complex environments with dynamic elements, such as moving obstacles or varying sensory inputs. One significant challenge is the reliance on a fixed metric representation, which may not adequately capture the nuances of more intricate spatial layouts or the interactions between multiple agents. Additionally, the schemas may struggle with tasks that require multi-modal integration, where information from different sensory modalities must be combined to inform decision-making. To improve the schema-based approach, several strategies can be employed. First, incorporating more sophisticated representations, such as hierarchical or graph-based structures, could enhance the agent's ability to model complex relationships within the environment. Second, integrating reinforcement learning techniques with the schema framework could allow for adaptive learning policies that evolve based on environmental feedback, enabling the agent to navigate and learn in real-time. Finally, enhancing the working memory mechanisms to better filter out distractions and prioritize relevant cues could significantly improve performance in cluttered or dynamic settings.

Q: Could the principles of schema-based learning be applied to develop more efficient and flexible artificial intelligence systems?

Yes, the principles of schema-based learning can be effectively applied to develop more efficient and flexible artificial intelligence (AI) systems. By leveraging schemas as knowledge structures that encapsulate relationships between information and actions, AI systems can achieve rapid learning and adaptation in various contexts. This approach aligns well with the goals of transfer learning and meta-learning, where prior knowledge is utilized to accelerate learning in new tasks. Incorporating schema-based learning into AI systems can lead to more robust models that can generalize across different domains, such as natural language processing, computer vision, and robotics. For instance, an AI system designed for language understanding could utilize schemas to quickly adapt to new linguistic patterns or dialects, while a vision-based system could leverage schemas to recognize objects in diverse environments with minimal training data. Moreover, the biologically plausible implementations of schemas, such as those based on Hebbian learning principles, can inspire the development of more efficient neural architectures that mimic human cognitive processes. This could result in AI systems that not only learn faster but also exhibit greater flexibility in handling unforeseen challenges, ultimately leading to more intelligent and adaptable technologies.

מושגי ליבה

Biologically plausible neural schemas can enable one-shot learning of multiple paired associations in a navigation task, outperforming a reinforcement learning-based actor-critic agent.

תקציר

The content describes a study on one-shot learning of multiple paired associations in a navigation task, using both symbolic and neural implementations of biologically plausible schemas.

Key highlights:

The authors introduce three schemas - LEARN METRIC REPRESENTATION, LEARN FLAVOR-LOCATION, and NAVIGATE - and demonstrate how they can be implemented using biologically plausible neural networks and learning rules.
They compose these schemas into a symbolic agent and a neural agent, and compare their performance to an actor-critic agent in a delayed match-to-place (DMP) task and a multiple paired association (MPA) task.
The schema agents demonstrate one-shot learning in both the DMP and MPA tasks, while the actor-critic agent struggles.
For navigation with an obstacle, the authors develop hybrid actor-critic-schema agents that outperform both the pure actor-critic and pure schema agents.
The neural schema agent can learn to store and recall up to 8 paired associations after a single trial, with the capacity depending on the network size and learning rule.
The schema agents fail to demonstrate one-shot learning in a new maze condition, as the learned metric representation becomes invalid. However, continued training allows them to relearn the metric and demonstrate one-shot learning of new paired associations.

The study provides a biologically plausible computational framework for understanding schema-dependent one-shot learning observed in rodent experiments, and offers testable predictions for future neuroscience research.

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arxiv.org

סטטיסטיקה

"The actor-critic agent's latency decreased from 182 ± 9.1 s in the first trial to 58 ± 4.4 s in the last trial."
"The time spent at the goal increased from PT1 to PT3 for the actor-critic (t= 35.1, p< 10-6), actor-critic-symbolic (32.8, p< 10-6), and actor-critic-neural (F= 37.0, p< 10-6) agents."
"The symbolic agent learned 11.58 ± 0.12 paired associates after just one session of learning."
"The neural agent with a reservoir of 2048 units trained using the EH rule learned 3.9 ± 0.17 paired associates after one trial."

ציטוטים

"Schemas are knowledge structures or frameworks that describe relationships among information and actions (Rumelhart, 1980; Rumelhart & Ortony, 1977). Importantly, schemas can aid rapid learning (Bartlett & Burt, 1932)."
"Schemas, their role in enabling one-shot learning, and their biologically plausible implementation remains unclear."
"Our agent recapitulates learning behavior observed in experiments and provides testable predictions that can be probed in future experiments."

תובנות מפתח מזוקקות מ:

One-shot learning of paired association navigation with biologically plausible schemas

by M Ganesh Kum... ב- arxiv.org 09-11-2024

https://arxiv.org/pdf/2106.03580.pdf

One-shot learning of paired association navigation with biologically plausible schemas

שאלות מעמיקות

How can the proposed schemas be extended to account for other types of one-shot learning, such as in language or object recognition tasks?

The proposed schemas, particularly the LEARN FLAVOR-LOCATION and LEARN METRIC REPRESENTATION schemas, can be adapted for one-shot learning in language and object recognition tasks by modifying the input representations and the associative memory mechanisms. For language tasks, the schemas could incorporate linguistic features as sensory cues, allowing the agent to associate specific words or phrases with their meanings or contexts. This could involve using embeddings that capture semantic relationships, enabling the agent to generalize from a single exposure to new vocabulary or grammatical structures.
In object recognition, the LEARN FLAVOR-LOCATION schema could be extended to include visual features extracted from images, allowing the agent to form associations between visual stimuli and their corresponding labels or categories. The neural implementation could leverage convolutional neural networks (CNNs) to process visual inputs, while the associative memory could be structured to store and retrieve object representations based on visual cues. By integrating these modifications, the schemas could facilitate rapid learning in diverse domains, enhancing the agent's ability to adapt to new tasks with minimal training data.

What are the limitations of the current schema-based approach, and how can it be improved to handle more complex environments and tasks?

The current schema-based approach has several limitations, particularly in its ability to generalize to complex environments with dynamic elements, such as moving obstacles or varying sensory inputs. One significant challenge is the reliance on a fixed metric representation, which may not adequately capture the nuances of more intricate spatial layouts or the interactions between multiple agents. Additionally, the schemas may struggle with tasks that require multi-modal integration, where information from different sensory modalities must be combined to inform decision-making.
To improve the schema-based approach, several strategies can be employed. First, incorporating more sophisticated representations, such as hierarchical or graph-based structures, could enhance the agent's ability to model complex relationships within the environment. Second, integrating reinforcement learning techniques with the schema framework could allow for adaptive learning policies that evolve based on environmental feedback, enabling the agent to navigate and learn in real-time. Finally, enhancing the working memory mechanisms to better filter out distractions and prioritize relevant cues could significantly improve performance in cluttered or dynamic settings.

Could the principles of schema-based learning be applied to develop more efficient and flexible artificial intelligence systems?

Yes, the principles of schema-based learning can be effectively applied to develop more efficient and flexible artificial intelligence (AI) systems. By leveraging schemas as knowledge structures that encapsulate relationships between information and actions, AI systems can achieve rapid learning and adaptation in various contexts. This approach aligns well with the goals of transfer learning and meta-learning, where prior knowledge is utilized to accelerate learning in new tasks.
Incorporating schema-based learning into AI systems can lead to more robust models that can generalize across different domains, such as natural language processing, computer vision, and robotics. For instance, an AI system designed for language understanding could utilize schemas to quickly adapt to new linguistic patterns or dialects, while a vision-based system could leverage schemas to recognize objects in diverse environments with minimal training data.
Moreover, the biologically plausible implementations of schemas, such as those based on Hebbian learning principles, can inspire the development of more efficient neural architectures that mimic human cognitive processes. This could result in AI systems that not only learn faster but also exhibit greater flexibility in handling unforeseen challenges, ultimately leading to more intelligent and adaptable technologies.