CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data

แนวคิดหลัก

A neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits in mammalian brains learns to produce an output after accumulating evidence from a stream of observed data, providing improved accuracy and robustness in image classification tasks.

บทคัดย่อ

The CBGT-Net is inspired by primate decision-making models that accumulate evidence over time.
It outperforms traditional neural network models and LSTM-based models in image classification tasks.
The model's architecture includes an Evidence Encoder, Evidence Accumulator, and Decision Threshold Module.
Evaluation on MNIST and CIFAR-10 environments shows superior performance and robustness to decreasing information in observations.
The CBGT-Net requires fewer training episodes than LSTM models for convergence.

สถิติ

This work was supported by the Army Research Lab awards W911NF-19-
2-0146 and W911NF-22-2-0115, and AFOSR / AFRL award FA9550-18-1-
0251.
The CBGT-Net consistently outperforms the LSTM model in terms of training efficiency across environments.
The CBGT-Net required 75.4% fewer training episodes than the LSTM model for the MNIST environments, and 89.4% fewer episodes for the CIFAR-10 environments.

คำพูด

"The CBGT circuitry deliberates over potential actions based on a stream of noisy or incomplete information from multiple cortical areas."
"Our model’s performance is robust to decreasing information in observations compared to LSTM models."
"The CBGT-Network provides improved accuracy and robustness compared to traditional neural network models."

ข้อมูลเชิงลึกที่สำคัญจาก

CBGT-Net

by Shreya Sharm... ที่ arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15974.pdf

สอบถามเพิ่มเติม

How can the evidence accumulation aspect of the CBGT model be extended to incorporate non-linear or temporal dynamics?

The evidence accumulation aspect of the CBGT model can be extended to incorporate non-linear or temporal dynamics by introducing additional components that capture these dynamics. One approach could involve integrating recurrent connections within the Evidence Accumulator component to allow for feedback loops and memory of past evidences. This would enable the model to consider not only current observations but also previous accumulated evidence, adding a temporal dimension to the decision-making process.
Furthermore, incorporating non-linearities in the form of activation functions within the accumulator could enhance its capacity to capture complex relationships between different pieces of evidence. By introducing non-linear transformations, the model can learn more intricate patterns and dependencies among observed data points, leading to more sophisticated decision-making capabilities.
In summary, extending the CBGT model's evidence accumulation aspect with recurrent connections for temporal dynamics and non-linear activations for capturing complex relationships can enhance its ability to integrate information over time in a more nuanced and adaptive manner.

What are the potential benefits of transparent deliberation in human-autonomy collaborations with the CBGT model?

Transparent deliberation in human-autonomy collaborations with the CBGT model offers several potential benefits:

Interpretability: The transparent nature of how decisions are made allows humans to understand why certain choices are being recommended by autonomous systems. This transparency fosters trust and confidence in machine-generated decisions.

Explainability: Transparent deliberation enables users to comprehend how an autonomous system arrived at a particular decision based on accumulated evidence. This explanation is crucial for accountability and compliance requirements in various domains.

Collaborative Decision-Making: Human operators can intervene or provide guidance during decision-making processes when they have visibility into how evidence is being accumulated and which factors influence final choices. This collaborative approach enhances synergy between humans and machines.

Error Detection and Correction: Transparency facilitates error detection as humans can identify discrepancies between expected outcomes and actual decisions made by autonomous systems based on visible evidential support.

Adaptability: Understanding how decisions evolve over time through transparent deliberation allows for real-time adjustments or refinements based on changing circumstances or new information inputs, enhancing adaptability in dynamic environments.

How could dynamic decision thresholds be learned within the CBGT architecture for more adaptive decision-making?

Dynamic decision thresholds within the CBGT architecture can be learned through a combination of reinforcement learning techniques and adaptive algorithms:

Reinforcement Learning (RL): Implementing RL methods such as Q-learning or policy gradient approaches enables agents using CBGT models to learn optimal decision thresholds based on rewards obtained from successful actions taken after crossing specific thresholds.

Temporal Difference Learning: Utilizing Temporal Difference (TD) learning algorithms like SARSA (State-Action-Reward-State-Action) allows agents employing CBGT architectures to update their estimates dynamically as they interact with environments, adjusting decision thresholds accordingly.

3Neural Network Adaptations: Introducing neural network components that predict optimal threshold values based on contextual cues extracted from observed data streams empowers agents utilizing CBGt models with adaptively changing criteria tailored towards specific tasks or environmental conditions.
4Meta-Learning Techniques: Leveraging meta-learning strategies where agents optimize their learning processes across multiple tasks enables them using CGBT architectures to quickly adapt their threshold settings accordingto varying contexts encountered during operations
By combining these methodologies withintheCBTarchitectureagentscanlearnadaptiveandcontext-specificdecisionthresholdsleadingtomoreflexibleandefficientdecisionmakingcapabilitiesbasedonreal-timedatastreams