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In this thesis I have used computational modelling to understand the function of neuronal oscillations in the brain. The integration of computational neuroscience into functional neuroimaging has allowed us to access latent variables, assumed to have a functional role in neuronal processing, and use these to predict neural responses observable with M/EEG or fMRI. This method therefore extends previous work in which experimental variables are used as proxies for computational estimates in order to provide more specific hypotheses about the mechanisms underlying particular behaviours and their neurophysiological correlates. However, this method cannot address the underlying neuronal dynamics which play an integral role in neuronal processing. Biophysical models aim to understand the causes of neural responses by modelling the intrinsic and extrinsic connections within and between cortical sources of activity. DCMs therefore allow experimenters to hypothesise how experimental perturbations influence effective connectivity between regions and the balance of excitatory and inhibitory connections within brain areas. Sophisticated neural mass models are based on electrophysiological data and therefore provide a more realistic inference about the mechanisms that explain observed neuronal responses. This is an important step to advance our current

understanding of the brain and ensure that we focus our interpretations of neuroimaging data in terms of what information the brain can see, use and communicate.

DCMs use Bayesian inversion to determine the efficacy of the generative model, therefore Bayesian model comparison can be used to determine the model that best explained the data based on the Bayesian model evidence. However, this highlights a fundamental problem with the current use of modelling in neuroimaging and cognitive neuroscience: there is no guarantee that the true model was amongst those tested. It is therefore

difficult to conclude anything about how the brain actually works from these methods. We rely on the assumptions of the models we use being correct with no guarantee that they are. The use of biophysical models has the advantage that the models are built in

accordance with current empirical evidence from electrophysiological studies; however, these will only be as accurate as the latest cellular evidence and recording techniques. The models referred to in this thesis are Bayesian, therefore the inferences I have made allude to the brain acting as a Bayesian inference machine. However, there is a lack of understanding of how Bayesian inference is computed in the brain. Bayesian models have proved very useful at predicting people’s behaviour across a number of contexts including sensorimotor control (Ernst and Banks, 2002; Gopnik et al., 2004; Knill, 1998; Körding and Wolpert, 2004; Tenenbaum et al., 2006; van Beers et al., 1999; Wolpert et al., 1995).

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However, these studies do not demonstrate that the brain actually uses Bayesian statistics to integrate information; non-Bayesian models could also be used to explain the

integration of information (Gigerenzer and Brighton, 2009; Juslin et al., 2009). In order to determine if the brain uses Bayesian inference, it is imperative to demonstrate that neurons encode uncertainty or probability distributions, however the current literature is not conclusive (Deneve, 2007; Knill and Pouget, 2004; Ma et al., 2006; Rao, 2004). In this thesis, I have demonstrated that sensorimotor beta oscillations correlate with uncertainty estimates, which supports previous studies regarding the role of this activity (Tan et al., 2016); however, this work fails to demonstrate whether and how the brain utilises this information. Even if the machinery is present for the brain to be able to compute Bayesian statistics, this doesn’t mean that the brain actually does. More work needs to focus on identifying how neuronal firing rates can integrate information at the neuronal and population levels across different domains and how this information is transmitted between cortical regions potentially by using DCMs. Before these fundamental studies have been completed, we cannot confirm that Bayes-optimal behaviours are actually Bayesian.

One issue with Bayesian models, and a key criticism of the active inference framework, is that they are so flexible, they cannot be falsified. Bayesian models have numerous free parameters and degrees of freedom, which allow the modeller to generate any predictions they want. Indeed, Brown et al (2013) created a generative model designed to explain how a decrease in sensory precision is necessary for movement by linking these parameters such that an increased expectation of an internally generated force would decrease sensory precision. In this way, the authors could guarantee the model would be able to explain existing evidence of perceptual and physiological sensory attenuation based on this hypothesis. Indeed, the active inference framework was built upon existing literature about the neurophysiology of connections within the sensorimotor system, therefore novel empirical work to test the model will most likely confirm these findings and thus the model becomes self-fulfilling. Moreover, the framework accommodates new data to generate and extend the model; it is therefore difficult to design an experiment that will definitively disprove this theory. Nevertheless, this theory generates a number of novel predictions, which will increase the number of new paradigms developed and can only enhance our understanding of the brain further. It is imperative that we

acknowledge the flexibility of many models in cognitive neuroscience and therefore design stringent and constrained experiments to provide empirical evidence for well-defined experimental predictions.

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