Description
When humans interact, multiple types of adaptivity occur, concerning their behaviour toward each other. For example, these types of behavioural adaptivity include short-term effects such as affiliation but also long-term effects such as bonding. Moreover, some forms of behavioural adaptivity apply to specific persons, whereas other forms apply in a generic manner to any person. The latter is addressed in theories such as attachment theory, describing how behavioural adaptations acquired in one relationship also have their effects in other relationships. All these forms of adaptivity or learning can be considered first-order adaptivity. However, their occurrence and strength depend on contextual circumstances. This dependence functions as a form of context-sensitive control of adaptivity and can be conceptualised as second-order adaptivity. Within neuroscience, it has been found that central mechanisms in the causal pathways leading to such forms of adaptivity or plasticity can be based on synaptic plasticity (adaptive connections, for example based on Hebbian learning) or nonsynaptic plasticity (adaptive intrinsic properties of neurons such as excitability thresholds). Moreover, it also has been found within neuroscience that metaplasticity occurs to control plasticity in a context-sensitive manner. In this plenary speech, it is discussed which learning or adaptation principles can apply to describe the different types of pathways to behavioural adaptivity mentioned above and how they relate to the learning or adaptation mechanisms identified within neuroscience. The work discussed here is based on a series of computational analyses that have been conducted last year and this year using a self-modeling network modeling approach. A Springer Nature book about this work will come out in 2023.| Period | 11 Nov 2022 |
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| Event title | The 4th International Conference on Machine Learning and Intelligent Systems |
| Event type | Conference |
| Location | SeoulShow on map |
| Degree of Recognition | International |