Neuromodulator-Mediated Neural System for Adaptive Behavior
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Solution Overview
Problem
Existing artificial neural networks struggle to efficiently adapt and learn in changing environments, particularly in forming flexible associations between sensory cues and motor actions to maximize rewards while minimizing energy consumption.
Innovation Solution
A system based on neuromodulator-mediated meta-plasticity and gain control, which modulates neural activity modes to switch between exploration and exploitation, using norepinephrine and dopamine to adjust synaptic transmission and learning rules, enabling quick association and disassociation of sensorimotor strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial neural networks use traditional learning methods to adapt to changing environments, then they can learn from observations, but they struggle to efficiently form flexible associations between sensory cues and motor actions
Solution Approach 1:
The patent changes the operational parameters of neural networks by introducing neuromodulation signals that dynamically adjust synaptic transmission probabilities and learning rates. This allows the network to switch between exploration and exploitation modes, efficiently forming associations when needed while adapting to environmental changes.
Solution Approach 2:
The patent introduces neuromodulator units as intermediary components that mediate between sensory inputs and motor outputs. These units release neuromodulation signals that regulate synaptic plasticity and gain control, enabling flexible sensorimotor association formation without requiring complete network retraining.
2Adaptability or versatility
If neural networks continuously explore new strategies to maximize rewards, then they can adapt to changing environments, but they consume excessive energy
Solution Approach 1:
The patent implements dynamic gain control that adjusts the level of neural activity and synaptic plasticity based on environmental stability. When the environment is stable, the system reduces exploration and conserves energy by maintaining current sensorimotor associations. When environmental changes are detected, exploration is intensified to update associations, optimizing the balance between adaptability and energy consumption.
Solution Approach 2:
The patent dynamically changes the parameter of synaptic plasticity strength based on neuromodulation signals. During exploitation phases, plasticity is reduced to minimize energy consumption, while during exploration phases triggered by environmental changes, plasticity is increased to enable rapid learning of new associations.
3Productivity
If neural networks switch between different activity modes to optimize performance, then they can efficiently gather rewards, but the control mechanism becomes more complex
Solution Approach 1:
The patent designs neuromodulator units with multi-functionality, where single units perform multiple roles: they receive sensory inputs, generate neuromodulation signals, regulate synaptic plasticity, and control gain modulation. This universal design reduces overall system complexity compared to having separate dedicated components for each function.
Solution Approach 2:
The gain control mechanism operates autonomously by detecting environmental changes and automatically adjusting neural activity modes without external intervention. The system self-regulates the balance between exploration and exploitation based on internal neuromodulation signals, reducing the need for complex external control systems.
Data Source
AI summary
Certain aspects of the present disclosure provide methods and apparatus for generating neural adaptive behavior, which may be based on neuromodulator-mediated meta-plasticity and/or gain control. In this manner, flexible associations between sensory cues and motor actions are generated, which enable an agent to efficiently gather rewards in a changing environment. One example method generally includes receiving one or more input stimuli; processing the received input stimuli to generate an output signal, wherein the processing is modulated with a first neuromodulation signal generated by a gain control unit; controlling the gain control unit to switch between at least two different neural activity modes, wherein at least one of a level or timing of the first neuromodulation signal generated by the gain control unit is determined based on the neural activity modes; and sending the output signal to an output unit.