Neural Models that Convince: Model Hierarchies and Other Strategies to Bridge the Gap Between Behavior and the Brain

Abstract

Computational modeling of the brain holds great promise as a bridge from brain to behavior. To fulfill this promise, however, it is not enough for models to be ‘biologically plausible’: models must be structurally accurate. Here, we analyze what this entails for so-called psychobiological models, models that address behavior as well as brain function in some detail. Structural accuracy may be supported by (1) a model's a priori plausibility, which comes from a reliance on evidence-based assumptions, (2) fitting existing data, and (3) the derivation of new predictions. All three sources of support require modelers to be explicit about the ontology of the model, and require the existence of data constraining the modeling. For situations in which such data are only sparsely available, we suggest a new approach. If several models are constructed that together form a hierarchy of models, higher-level models can be constrained by lower-level models, and low-level models can be constrained by behavioral features of the higher-level models. Modeling the same substrate at different levels of representation, as proposed here, thus has benefits that exceed the merits of each model in the hierarchy on its own.

Keywords:

• Brain, Behavior
• Computational Modeling
• Hippocampus
• Neural Networks
• Vision

Acknowledgements

This research was supported by a VENI grant to the first author and a PIONIER grant to the third author, both from the Netherlands Society for Scientific Research (NWO).

Notes

Notes

[1]  There are computational models that operate at the level of cortical columns, but these have generally failed to get traction; perhaps because of a mismatch with available empirical techniques.

[2]  Even when they do the answers are usually still in need of an explanation. For example, memory decay may be part of the psychological answer to the psychological question of why we forget, it is still an interesting question how that decay occurs. To that question a psychological answer is unlikely.

[3]  This is generally the case for theories that describe the same phenomena at two levels, as the debate on reductionism has shown (e.g., Schaffner, 1967; Sklar, 1967).

[4]  Interleaved learning refers to mixing learning trials for new patterns with repetition trials for old, already stored patterns.