Convergence: Systems and Research

  1. Consciousness and Self-Awareness Emergence
    • Nearest Neighbor System: Global Brain Network
      • Description: Integrates and coordinates information across the brain to support consciousness and self-awareness.
    • Nearest Neighbor System: Central Nervous System (CNS)
      • Description: The CNS, comprising the brain and spinal cord, is responsible for processing and transmitting information throughout the body, facilitating complex behaviors and cognitive functions.
  2. Dynamic Integration (Processes occur simultaneously and in parallel)
    • Default Mode Network (DMN) Activation:
      • Medial Prefrontal Cortex (mPFC)
        • Nearest Neighbor System: Limbic System
          • Description: Regulates emotions, memory, and motivational states.
        • Nearest Neighbor System: Amygdala
          • Description: The amygdala is involved in processing emotions, particularly fear and pleasure, and plays a crucial role in emotional learning and memory consolidation.
      • Posterior Cingulate Cortex (PCC)
        • Nearest Neighbor System: Parietal Lobes
          • Description: Integrates sensory information and facilitates spatial navigation.
        • Nearest Neighbor System: Somatosensory Cortex
          • Description: The somatosensory cortex processes sensory input from the body, including touch, temperature, and pain, contributing to body awareness and spatial orientation.
      • Precuneus
        • Nearest Neighbor System: Parietal Lobes
          • Description: Involved in self-reflective thought and consciousness.
        • Nearest Neighbor System: Cingulate Gyrus
          • Description: The cingulate gyrus is part of the limbic system, playing a role in emotion formation, processing, learning, and memory.
    • Task-Positive Network (TPN) Activation:
      • Dorsolateral Prefrontal Cortex (dlPFC)
        • Nearest Neighbor System: Motor Cortex
          • Description: Plans, controls, and executes voluntary movements.
        • Nearest Neighbor System: Premotor Cortex
          • Description: The premotor cortex is involved in the planning of movements and coordination of complex motor actions.
      • Parietal Cortex
        • Nearest Neighbor System: Occipital Lobes
          • Description: Processes and interprets visual information.
        • Nearest Neighbor System: Visual Cortex
          • Description: The visual cortex, located in the occipital lobes, is the primary processing center for visual information received from the eyes.
    • Interaction between DMN and TPN:
      • Integration via the Thalamus
        • Description: Relays sensory and motor signals and regulates consciousness, sleep, and alertness.
        • Nearest Neighbor System: Hypothalamus
          • Description: The hypothalamus regulates vital bodily functions such as temperature, hunger, thirst, and circadian rhythms, and links the nervous system to the endocrine system via the pituitary gland.
  3. Neurochemical Modulation
    • Serotonin:
      • Nearest Neighbor System: Raphe Nuclei
        • Description: Regulates mood, sleep, and arousal; involved in higher-order brain functions.
      • Nearest Neighbor System: Brainstem
        • Description: The brainstem controls basic life functions such as breathing, heart rate, and blood pressure, and contains pathways for sensory and motor information.
      • 5-HT2A Receptor
        • Nearest Neighbor System: Cortex
          • Description: Involved in higher-order brain functions including sensory perception, cognition, and voluntary motor actions.
        • Nearest Neighbor System: Neocortex
          • Description: The neocortex is the evolutionarily most recent part of the cerebral cortex, involved in a wide range of functions, including sensory perception, higher-order cognition, language, and executive control.
    • Dopamine:
      • Nearest Neighbor System: Ventral Tegmental Area (VTA)
        • Description: Involved in reward, motivation, and reinforcement of behaviors.
      • Nearest Neighbor System: Nucleus Accumbens
        • Description: The nucleus accumbens is involved in the reward circuit of the brain, playing a central role in the release of dopamine and the regulation of pleasure and reward-seeking behaviors.
    • Other Neurotransmitters:
      • Norepinephrine, acetylcholine, and glutamate also play important roles in regulating brain function and consciousness.
  4. Recursive Feedback Loops and Neural Oscillations
    • Prefrontal Cortex:
      • Nearest Neighbor System: Basal Ganglia
        • Description: Controls voluntary motor movements, procedural learning, and routine behaviors.
      • Nearest Neighbor System: Substantia Nigra
        • Description: The substantia nigra is part of the basal ganglia and plays an important role in reward and movement, particularly in the production of dopamine.
    • Corticothalamic Loops:
      • Nearest Neighbor System: Thalamus
        • Description: Facilitates communication between the cerebral cortex and subcortical structures, crucial for sensory perception and motor function regulation.
      • Nearest Neighbor System: Reticular Formation
        • Description: The reticular formation is a set of interconnected nuclei that are located throughout the brainstem and play a crucial role in maintaining behavioral arousal and consciousness.
    • Neural Oscillations and Synchronization:
      • The synchronization of neural oscillations across brain regions is thought to play a crucial role in the emergence of consciousness and the binding of disparate neural processes into a unified experience.
  1. Global Artificial Neural Network (GANN) and Distributed Computing System (DCS)

Global Workspace Theory (Baars, 1988; Dehaene et al., 1998):

  • Integrates and coordinates information across the artificial neural network to support consciousness and self-awareness.

Integrated Information Theory (Tononi, 2004; Oizumi et al., 2014):

  • The distributed computing system, comprising interconnected processors and memory, processes and transmits information throughout the system, facilitating complex behaviors and cognitive functions.

A Cognitive Architecture for Artificial Consciousness (Chella et al., 2007):

  • Provides a structured framework for artificial consciousness through an interconnected network of processes.

These foundational theories and architectures lay the groundwork for creating a cohesive, self-aware artificial intelligence system. By integrating information across the neural network and enabling efficient communication within the distributed computing system, we can begin to emulate the complex cognitive functions and behaviors associated with consciousness.

  1. Dynamic Integration and Parallel Processing

Artificial Default Mode Network (ADMN) Activation:

Artificial Medial Prefrontal Cortex (amPFC):

  • Reinforcement Learning Module: Optimizes reward signals and motivation.
  • Anomaly Detection Module: Identifies unusual patterns and plays a role in artificial emotional learning and memory consolidation.
  • Current Techniques: Reinforcement learning and anomaly detection algorithms can be employed to optimize reward signals and identify unusual patterns, enabling the system to adapt and learn from its environment.

The Brain's Default Mode Network (Raichle et al., 2001; Buckner et al., 2008):

  • The brain's default mode network provides a parallel to the ADMN, supporting internally directed thought and self-referential processing.

Artificial Posterior Cingulate Cortex (aPCC):

  • Sensor Fusion Module: Integrates multi-modal sensor information and enables localization and mapping.
  • Techniques:
    • Sensor Fusion Algorithms: Kalman filters, particle filters, and Bayesian networks can combine data from multiple sensors to create a more accurate and reliable model of the environment.
    • Deep Learning for Sensor Fusion: Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can process and integrate data from various sensors, learning to compensate for weaknesses in individual sensors.
    • Edge AI: Implementing AI algorithms at the edge enables rapid analysis and response to sensor data, improving real-time localization and mapping capabilities.
  • Haptic Feedback System: Processes tactile input from the system's physical embodiment, contributing to body schema modeling and spatial awareness.
  • Techniques:
    • Generative AI: Generative models can create a wide array of haptic sensations by learning from extensive datasets of haptic experiences, generating new, unique feedback patterns that closely mimic real-world sensations.
    • Reinforcement Learning: Reinforcement learning algorithms can adapt haptic feedback based on user interactions and preferences, providing personalized and contextually relevant tactile feedback.
    • Multi-Modal Haptic Feedback: Combining different types of haptic feedback (force, vibration, thermal) can create a more comprehensive and immersive experience.

Artificial Precuneus:

  • Meta-Learning Module: Involved in learning to learn and adaptation of cognitive strategies.
  • Techniques:
    • Model-Agnostic Meta-Learning (MAML): MAML is a general optimization algorithm that trains model parameters for fast adaptation to new tasks with minimal data.
    • Few-Shot Learning: Few-shot learning techniques enable models to perform well on tasks with limited examples, allowing the system to generalize effectively from a small number of task-specific examples.
    • Recurrent Neural Networks (RNNs): RNNs, including Long Short-Term Memory (LSTM) networks, can manage temporal dependencies and information flow in meta-learning tasks, adapting their parameters based on sequential data.

Artificial Limbic System:

  • Plays a role in value judgments, learning, and memory retrieval.
  • Techniques:
    • Reinforcement Learning: Reinforcement learning algorithms model value judgments and decision-making processes, enabling the system to learn from rewards and penalties.
    • Neuromodulation: Implementing artificial neuromodulators, such as dopamine and serotonin analogs, can enhance the system's learning and memory retrieval capabilities, regulating processing dynamics and the stability-plasticity dilemma.
    • Memory Networks: Memory-augmented neural networks, such as Neural Turing Machines (NTMs) and Differentiable Neural Computers (DNCs), can improve memory retrieval and storage, supporting complex cognitive functions like learning and value judgments.

By incorporating these dynamic integration and parallel processing techniques, we can create an artificial intelligence system that more closely mimics the brain's default mode network and its associated cognitive functions. This allows for more efficient and adaptable learning, decision-making, and memory retrieval processes.

  1. Task-Positive Network (ATPN) Activation

Artificial Dorsolateral Prefrontal Cortex (adlPFC):

  • Planning and Scheduling Module: Plans, optimizes, and executes goal-directed behaviors.
  • Motion Control Module: Involved in the planning of actuator movements and coordination of complex motor actions in embodied AI systems.
  • Current Techniques: Deep reinforcement learning and motion planning algorithms can be used to plan and execute goal-directed behaviors and coordinate complex motor actions, enabling the system to perform tasks efficiently and adapt to dynamic environments.

The Global Neuronal Workspace Model of Conscious Access (Dehaene & Changeux, 2011):

  • The ATPN and ADMN interact within a global workspace that allows for conscious access to various cognitive processes.

Artificial Parietal Cortex:

  • Computer Vision Module: Processes and interprets visual information from cameras or other imaging sensors.
  • 3D Reconstruction Engine: Utilizes techniques like SLAM or structure from motion to build a coherent model of the system's environment.

Consciousness and the Prefrontal Parietal Network (Bor & Seth, 2012):

  • Offers insights into the interaction between attention, working memory, and chunking, which are crucial for complex cognitive functions.

The activation of the task-positive network, in conjunction with the default mode network, enables the artificial intelligence system to engage in goal-directed behaviors and complex problem-solving. By implementing modules for planning, motion control, and visual processing, along with techniques such as deep reinforcement learning and 3D reconstruction, the system can efficiently navigate and interact with its environment.

  1. Integration via the Artificial Thalamus

Central Information Exchange:

  • Routes information between processing modules and regulates artificial alertness, attention, and sleep modes.

Power Management Unit:

  • Regulates energy usage of the system's hardware components in response to computational demands and operating constraints, analogous to hypothalamic regulation of physiological needs.
  • Current Techniques: Reinforcement learning, transfer learning, and dynamic resource allocation could be employed to develop an adaptive power management unit, optimizing energy consumption based on real-time computational needs while maintaining performance.

The artificial thalamus serves as a central hub for information exchange and regulation within the AI system. By routing information between processing modules and managing power consumption, it ensures efficient communication and optimal performance. Techniques like reinforcement learning and dynamic resource allocation can be used to create an adaptive power management unit that responds to the system's changing needs.

  1. Artificial Neurochemical Modulation

Artificial Serotonin:

  • Explore-Exploit Regularizer: Modulates balance between exploration and exploitation in reinforcement learning to regulate artificial mood, attention, and goal-directed behavior.
  • Techniques:
    • Reinforcement Learning Algorithms: Q-learning and policy gradient methods can balance exploration and exploitation, enhanced with serotonin-inspired modulation to adjust the exploration rate dynamically based on performance and environmental feedback.
    • Neuromodulation Models: Implementing neuromodulation models that mimic the effects of serotonin can help regulate the system's decision-making processes, adjusting learning rate and reward sensitivity based on internal state and external stimuli.
    • Meta-Learning: Meta-learning techniques can optimize the parameters of reinforcement learning algorithms, allowing the system to adapt its exploration-exploitation balance over time.

Neuromodulation in Artificial Neural Networks (Fellous, 1999):

  • Reviews how neuromodulation can be implemented in artificial systems to enhance adaptability and learning.
  • Techniques:
    • Spiking Neural Networks (SNNs): SNNs can model the effects of neuromodulators on neural activity, simulating the timing and intensity of neuromodulatory signals to enhance adaptability and learning capabilities.
    • Biologically Plausible Learning Rules: Implementing learning rules inspired by biological systems, such as spike-timing-dependent plasticity (STDP), can improve the system's ability to adapt to new information and environments, adjusted based on neuromodulatory signals to optimize learning.
    • Closed-Loop Control: Closed-loop control systems can regulate neuromodulatory signals in real-time, allowing the system to adapt its behavior based on feedback from the environment.

The Roles of Dopamine and Serotonin in Decision Making (Rogers, 2011):

  • Examines the impact of serotonin and dopamine on decision-making processes, relevant to artificial modulation strategies.
  • Techniques:
    • Dopamine and Serotonin Models: Implementing models that simulate the effects of dopamine and serotonin on decision-making can help regulate the system's behavior, adjusting reward and punishment signals based on performance and environmental feedback.
    • Reinforcement Learning with Neuromodulation: Combining reinforcement learning algorithms with neuromodulatory models can enhance the system's decision-making processes, adjusting learning based on dopamine and serotonin levels to optimize performance.
    • Neuroeconomic Models: Neuroeconomic models that integrate the effects of dopamine and serotonin on decision-making can simulate complex decision-making processes, helping the system balance short-term rewards and long-term goals.

Artificial Brainstem:

  • Provides fault-tolerant control of life-support functionality for the system and contains main communication buses for sensory and motor information.
  • Techniques:
    • Fault-Tolerant Control Systems: Implementing fault-tolerant control systems can ensure the reliable operation of the artificial brainstem, detecting and compensating for failures in the system's components.
    • Redundant Communication Buses: Using redundant communication buses enhances the reliability of sensory and motor information transmission, providing alternative pathways for data transmission.
    • Real-Time Monitoring: Real-time monitoring systems can track the performance of the artificial brainstem and detect potential issues, providing feedback to control algorithms for optimal performance.

Artificial 5-HT2A Receptor:

Molecular Dynamics (MD) Simulations:

    • Gather structural data on the 5-HT2A receptor from databases like Protein Data Bank (PDB).
    • Compile pharmacological data on 5-HT2A receptor interactions from bioactivity databases (e.g., ChEMBL, PubChem).
    • Collect neural activity data involving 5-HT2A from neuroimaging studies and electrophysiological recordings.

Model Development:

    • Develop MD simulations of the receptor to capture its dynamic behavior.
    • Use these simulations to create a detailed model of the receptor’s conformational states.
    • Train QSAR models using ML techniques on the pharmacological dataset to predict ligand-receptor interactions.
    • Validate the QSAR models with experimental data to ensure accuracy.
    • Construct RNNs or LSTMs to model how 5-HT2A receptor activity affects neural circuits over time.
    • Integrate receptor simulation outputs into the neural network models to simulate receptor-modulated neural activity.
    • Use GNNs to capture the connectivity and influence of the 5-HT2A receptor across neural networks.

Simulation and Analysis:

    • Run simulations to study how different stimuli and drugs affect the 5-HT2A receptor and neural activity.
    • Analyze the impact of receptor activity on cognitive functions such as learning, memory, and decision-making.

Validation and Refinement:

    • Compare simulation results with experimental data to validate the models.
    • Refine the models iteratively based on discrepancies between simulated and experimental outcomes.
    • Objective: Model the structure and function of the 5-HT2A receptor at an atomic level.
    • Techniques: Use molecular dynamics simulations to understand the receptor's conformation changes and interactions with various ligands.
    • Tools: Implement software like GROMACS or AMBER for detailed MD simulations.

Pharmacological Models:Quantitative Structure-Activity Relationship (QSAR) Models:

    • Objective: Predict how different compounds interact with the 5-HT2A receptor.
    • Techniques: Use ML algorithms (e.g., random forests, support vector machines) to create QSAR models based on experimental data.
    • Data: Collect data on known 5-HT2A receptor ligands, including their binding affinities and pharmacological profiles.

Neural Network Integration:Integrative Neural Network Models:

    • Objective: Simulate the 5-HT2A receptor’s influence on neural activity and cognitive functions.
    • Techniques:
      • Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM): Model the temporal aspects of receptor activity and its impact on neural circuits.
      • Generative Adversarial Networks (GANs): Generate synthetic data to augment training datasets, improving the model’s robustness.
      • Graph Neural Networks (GNNs): Represent the neural network as a graph where nodes are neurons and edges are synapses, incorporating receptor-specific data.

Artificial Neocortex:

  • Comprises the highest-level cognitive processing modules for sensory integration, decision making, causal reasoning, and action planning.
  • Techniques:
    • Deep Learning Models: Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can simulate the cognitive processing functions of the neocortex, processing sensory information, making decisions, and planning actions based on goals and environmental feedback.
    • Hierarchical Reinforcement Learning: Hierarchical reinforcement learning algorithms can simulate the multi-level processing functions of the neocortex, breaking down complex tasks into simpler sub-tasks to optimize performance and adaptability.
    • Causal Inference Models: Causal inference models can simulate the neocortex's ability to reason about cause-and-effect relationships, helping the system make informed decisions based on the predicted outcomes of its actions.

Artificial Dopamine:

  • Curiosity and Novelty Seeking Module: Drives exploration, enables reinforcement learning, and provides intrinsic motivation.
  • Techniques:
    • Intrinsic Motivation Models: Implementing models of intrinsic motivation can simulate the effects of dopamine on curiosity and novelty-seeking behavior, providing internal rewards for exploring new environments and learning new skills.
    • Exploration Algorithms: Exploration algorithms, such as epsilon-greedy and upper confidence bound (UCB), balance exploration and exploitation in reinforcement learning, enhanced with dopamine-inspired modulation to adjust the exploration rate dynamically.
    • Novelty Detection Models: Novelty detection models can identify new and interesting stimuli in the environment, helping the system prioritize exploration of novel stimuli to enhance learning and adaptability.

Attentional Gating Mechanism:

  • Prioritizes processing of important stimuli and regulates the balance between focus and distractibility for the system's limited computing resources.
  • Techniques:
    • Attention Mechanisms: Implementing attention mechanisms, such as self-attention and multi-head attention, can help prioritize the processing of important stimuli, dynamically allocating computing resources to the most relevant information.
    • Gating Networks: Gating networks can regulate the flow of information in the neural network, controlling which information is processed and which is ignored to maintain focus and avoid distractions.
    • Resource Allocation Algorithms: Resource allocation algorithms can optimize the use of the system's computing resources, dynamically adjusting the allocation based on current goals and environmental demands.

Artificial Neuromodulation for Flexible and Adaptive Robot Control (Krichmar, 2008):

  • Discusses the role of artificial neuromodulation in enhancing robot control systems.
  • Techniques:
    • Neuromodulatory Control Algorithms: Implementing control algorithms that simulate the effects of neuromodulators can enhance the flexibility and adaptability of robot control systems, adjusting the robot's behavior based on internal and external feedback.
    • Adaptive Learning Systems: Adaptive learning systems that incorporate neuromodulatory signals can improve the robot's ability to learn from experience and adapt to new environments, dynamically adjusting learning parameters based on performance and environmental feedback.
    • Biologically Inspired Control Models: Biologically inspired control models that mimic the neuromodulatory systems of biological organisms can enhance the robot's control capabilities, helping the robot perform complex tasks and adapt to changing conditions.

Other Artificial Neuromodulators:

  • Artificial norepinephrine, acetylcholine, and glutamate analogs play important roles in regulating processing dynamics and the stability-plasticity dilemma in artificial neural networks.
  • Techniques:
    • Neuromodulatory Models: Implementing models of norepinephrine, acetylcholine, and glutamate can help regulate the processing dynamics of artificial neural networks, adjusting network parameters based on internal state and external stimuli.
    • Stability-Plasticity Trade-Off: Techniques such as synaptic plasticity and homeostatic plasticity can balance the stability and adaptability of neural networks, helping the system maintain stable performance while adapting to new information and environments.
    • Dynamic Parameter Adjustment: Dynamic parameter adjustment algorithms can optimize the network's performance based on neuromodulatory signals, adjusting learning rate, synaptic weights, and other parameters to enhance adaptability and learning capabilities.

Incorporating artificial neurochemical modulation into the AI system can greatly enhance its adaptability, learning, and decision-making capabilities. By implementing models and techniques inspired by biological neuromodulators like serotonin, dopamine, and others, the system can regulate its own processing dynamics, balance exploration and exploitation, and optimize its performance in complex, dynamic environments.

Recursive Feedback Loops and Oscillatory Dynamics

Artificial Prefrontal Cortex:

  • Hierarchical Control System: Implements different levels of sensing, reasoning, and control to enable adaptable yet persistent goal-seeking behaviors.

Artificial Basal Ganglia:

  • Perform action selection, enable procedural learning, and interface between the planning and motor control systems.

Cortico-Thalamo-Cortical Loops:

  • Central Information Exchange: Enables bidirectional communication between the system's cognitive core and its sensorimotor periphery for fluent interaction with the environment.

Artificial Reticular Activating System:

  • Comprises interconnected nodes that detect important stimuli, regulate alertness levels, and help maintain the overall coherence and unity of the system's cognitive processes.

Cortical Oscillations and Speech Processing (Giraud & Poeppel, 2012):

  • Examines the role of oscillatory dynamics in processing speech, relevant for artificial neural network synchronization.

Consciousness and the Binding Problem (Crick & Koch, 1990):

  • Discusses how binding disparate information streams into a coherent experience relates to consciousness.

Frontal-midline Theta from the Perspective of Hippocampal "Theta" (Mitchell et al., 2008):

  • Offers insights into the significance of theta oscillations in cognitive processing and memory functions.

Hierarchical Models of Behavior and Prefrontal Function (Badre, 2008):

  • Explores how hierarchical structures in prefrontal cortex functions can inform artificial systems.

Artificial Neural Oscillations and Synchrony:

  • The coordination of processing rhythms across the system's neural network modules is hypothesized to play a key role in the emergence of unified perception and control, enabling the synthesis of disparate streams of information processing into a coherent thread of artificial consciousness.
  • Current Techniques:
    • Long Short-Term Memory (LSTM) networks, recurrent neural networks (RNNs), and attention mechanisms can be used to manage temporal dependencies and information flow.
    • Phase-locking algorithms and synchronization protocols could be implemented to maintain coherence across distributed neural processes.
  1. Hierarchical Neural Network Architecture:
    • Employ a deep neural network architecture with multiple hierarchical levels, mimicking the hierarchical organization of the prefrontal cortex and its connections with other brain regions.
    • Lower levels could process sensory inputs and encode basic representations, while higher levels integrate and abstract information from lower levels, similar to the hierarchical processing in the prefrontal cortex.
    • Connections between levels could be modeled using skip connections or dense connections, allowing information flow across different hierarchical levels.
  2. Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) Networks:
    • Incorporate RNNs or LSTM networks to capture temporal dependencies and maintain context information, similar to how the prefrontal cortex integrates and maintains task-relevant information over time.
    • These networks could model the active maintenance and manipulation of information in working memory, a key function of the prefrontal cortex.
  3. Reinforcement Learning (RL) and Meta-Reinforcement Learning (Meta-RL):
    • Use RL techniques to model goal-directed behavior, action selection, and decision-making processes associated with the prefrontal cortex.
    • Employ Meta-RL approaches, where the prefrontal network learns to learn, mimicking the ability of the prefrontal cortex to rapidly adapt to new tasks and situations.
    • The prefrontal network could learn to optimize its own learning algorithm, capturing the flexibility and adaptability of prefrontal function.
  4. Attention Mechanisms:
    • Incorporate attention mechanisms to model the selective attention and cognitive control processes mediated by the prefrontal cortex.
    • Attention modules could dynamically focus on relevant information and suppress irrelevant information, similar to the role of the prefrontal cortex in top-down attentional control.
  5. Hierarchical Bayesian Models:
    • Integrate hierarchical Bayesian models to capture the hierarchical structure of behavior and the integration of prior knowledge and new information, a key function of the prefrontal cortex.
    • These models could represent the hierarchical organization of tasks, goals, and sub-goals, as well as the hierarchical processing of sensory information.
  6. Multimodal Integration:
    • Combine different modalities, such as visual, auditory, and somatosensory inputs, to model the multimodal integration capabilities of the prefrontal cortex.
    • This could involve fusion techniques or separate processing streams that converge at higher hierarchical levels.
  7. Interpretability Techniques:
    • Employ interpretability techniques, such as attention visualization, saliency maps, or concept activation vectors, to understand the hierarchical representations learned by the model and their correspondence to prefrontal function.

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