Biological constraints on neural network models of cognitive function.
Friedemann Pulvermüller1,2,3,4, Rosario Tomasello5,6, Malte R Henningsen-Schomers5,6
1Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany. friedemann.pulvermuller@fu-berlin.de.
Nature Reviews. Neuroscience
|June 29, 2021
Summary
Neural network models can enhance understanding of brain functions when made neurobiologically realistic. Improving models of neurons, learning, and connectivity is key for advancing cognitive science and potential clinical applications.
Area of Science:
- Computational Neuroscience
- Cognitive Science
- Artificial Intelligence
Background:
- Neural network models offer insights into complex brain functions.
- Current models, despite advancements, have limitations in neurobiological realism.
- Bridging the gap between artificial and biological neural networks is crucial.
Purpose of the Study:
- To discuss different types of neural network models.
- To identify areas for improving the biological plausibility of these models.
- To explore how brain-constrained models advance cognitive theories and explain brain functions.
Main Methods:
- Review of various neural network architectures (localist, auto-associative, hetero-associative, deep, whole-brain).
- Analysis of key aspects for enhancing biological realism: neuron models, synaptic plasticity, learning rules, inhibition, control, and neuroanatomy.
- Integration of biologically grounded theories with neural models.
Main Results:
- Identified specific improvements for neural models in neuron types, plasticity mechanisms, and connectivity.
- Demonstrated the utility of brain-constrained models in explaining higher brain functions.
- Highlighted progress in biologically grounded cognitive theories.
Conclusions:
- Enhancing the neurobiological realism of neural networks is essential for understanding brain function.
- Brain-constrained models provide a powerful framework for cognitive neuroscience.
- Future clinical applications of these models are anticipated.
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