ArticleScientific reports2018
In vitro neural networks minimise variational free energy.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
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- As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference.Entropy (Basel, Switzerland) · 2025Article
- Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Deviance detection and regularity sensitivity in dissociated neuronal cultures.Frontiers in neural circuits · 2025Article
- Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.PLoS computational biology · 2024Article
- Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks.Nano letters · 2024Article
- Moral considerability of brain organoids from the perspective of computational architecture.Oxford open neuroscience · 2024Article
- Experimental validation of the free-energy principle with in vitro neural networks.Nature communications · 2023Article
- An Overview of In Vitro Biological Neural Networks for Robot Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Article
- Canonical neural networks perform active inference.Communications biology · 2022Article
- Unification of free energy minimization, spatiotemporal energy, and dimension reduction models of V1 organization: Postnatal learning on an antenatal scaffold.Frontiers in computational neuroscience · 2022Article
- Spontaneous and Perturbational Complexity in Cortical Cultures.Brain sciences · 2021Article
- Artificial neurovascular network (ANVN) to study the accuracy vs. efficiency trade-off in an energy dependent neural network.Scientific reports · 2021Article
- Markov blankets in the brain.Neuroscience and biobehavioral reviews · 2021Review
- Message Passing and Metabolism.Entropy (Basel, Switzerland) · 2021Article
- Bayesian mechanics of perceptual inference and motor control in the brain.Biological cybernetics · 2021Article
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Abstract
In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle. In brief, we stimulated an in vitro cortical cell culture with stimulus trains that had a known statistical structure. We then asked whether recorded neuronal responses were consistent with variational message passing based upon free energy minimisation (i.e., evidence maximisation). Effectively, this required us to solve two problems: first, we had to formulate the Bayes-optimal encoding of the causes or sources of sensory stimulation, and then show that these idealised responses could account for observed electrophysiological responses. We describe a simulation of an optimal neural network (i.e., the ideal Bayesian neural code) and then consider the mapping from idealised in silico responses to recorded in vitro responses. Our objective was to find evidence for functional specialisation and segregation in the in vitro neural network that reproduced in silico learning via free energy minimisation. Finally, we combined the in vitro and in silico results to characterise learning in terms of trajectories in a variational information plane of accuracy and complexity.
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