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A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity
Rosario Tomasello1,2,3, Max Garagnani1,4, Thomas Wennekers2
1Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany.
Frontiers in Computational Neuroscience
|November 22, 2018
Summary
This study models how the brain organizes semantic knowledge using spiking neural networks. The model reveals that semantic hubs and category-specific areas emerge from neuroanatomy and learning during language acquisition.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Neurobiology
Background:
- The cortical organization of semantic knowledge and processing is a debated topic in cognitive neuroscience.
- Existing research identifies both general semantic hubs and modality-preferential areas involved in meaning representation.
- Understanding the brain's complex semantic organization requires biologically constrained neurocomputational models.
Purpose of the Study:
- To develop an improved neurocomputational model of semantics incorporating spiking neurons and realistic connectivity.
- To simulate semantic learning and symbol grounding through associative learning between neural populations.
- To investigate how neuroanatomical structure and neuronal activation patterns explain semantic brain organization.
Main Methods:
- Enhanced existing neurocomputational models of semantics by integrating spiking neurons and detailed network connectivity.
- Simulated semantic learning and symbol grounding via Hebbian associative learning between co-activated neuron populations.
- Analyzed the emergent properties of distributed cell assembly circuits across simulated cortical areas.
Main Results:
- Emergent, distributed semantic circuits exhibited category-specific topographical distributions, extending into motor and visual areas.
- Multimodal connector hub areas contained significant numbers of neurons involved in all semantic circuits.
- Semantic hubs showed some category-specificity, though less pronounced than modality-preferential cortices.
Conclusions:
- The neurocomputational model successfully integrates divergent experimental findings on conceptualization.
- Semantic hubs and category-specific areas emerge dynamically from neuroanatomical connectivity and correlated neuronal activation during learning.
- This model provides a causal explanation for the brain's organization of semantic knowledge.
Keywords:
Hebbian learningbrain-like connectivitydistributed neural assembliessemantic groundingspiking neural networkword acquisitionMore Related Videos
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