This chapter considers recent advances in computational neuroscience that are especially relevant for psychiatry. We offer a review of computational psychiatry in terms of its ambitions, emerging domains of application, and promises for the future. Our focus is on theoretical formulations of brain function that accommodate subjective beliefs and behavior within formal (computational) frameworks—frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the nature and principles that underlie functional brain architectures is, we assume, essential for understanding and phenotyping psychopathology and its pathophysiological underpinnings. To illustrate computational approaches to psychiatric disorders, we focus on active (Bayesian) inference and predictive coding. Specifically, we try to explain how the basic principles of neuronal computation are being used to understand psychiatric phenomena, ranging from affiliative behavior and theory of mind in autism to abnormalities of smooth pursuit eye movements in schizophrenia.
Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.
The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance - in relation to the ontological and epistemological status of representations - is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account: an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the aboutness or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.
Abstract There is growing recognition of cortical involvement in nociception. The present study is motivated by predictive coding formulations of pain perception that stress the importance of top-down and bottom-up information flow in the brain. It compares forward and backward effective connectivity - estimated from resting-state fMRI - between chronic osteoarthritic patients and healthy control subjects. Additionally, it assesses differences in effective connectivity between placebo responders and non-responders and asks whether these differences can be used to predict pain perception and placebo response. To assess hierarchical processing in nociception, we defined two primary cortical regions: primary somatosensory cortex (SSC) and posterior insula (PI) (primary interoceptive cortex) and lateral frontal pole (FP1), a terminal relay station of the pain processing pathways. The directed (effective) connectivity within and between these regions were estimated using spectral dynamic causal modeling (DCM). 56 osteoarthritis patients and 18 healthy controls were included in the analysis. Within the patient group, effective connectivity was compared between placebo responders and non-responders. In osteoarthritic patients, contra control group, forward connectivity from SSC to FP1 and from PI to FP1 was enhanced in the left hemisphere. Backward connections from FP1 to SSC were more inhibitory. Intrinsic (i.e., inhibitory recurrent or self-connectivity) of left FP1 increased. In placebo responders compared to non-responders, forward connections from bilateral SSC to PI, left SSC to FP1, left PI to left FP1 were more inhibitory. In addition, self-connections of bilateral PI and top-down connections from right FP1 to right SSC were disinhibited; whereas self-connections of right FP1 became increasingly inhibitory. We confirmed the robustness of these results in a leave-one-out cross-validation analysis of (out-of-sample) effect sizes. Overall, effective extrinsic and intrinsic effective connectivity among higher and lower cortical regions involved in pain processing emerges as a promising and quantifiable candidate marker of nociception and placebo response. The significance of these findings for clinical practice and neuroscience are discussed in relation to predictive processing accounts of placebo effects and chronic pain.
Abstract We have investigated the theoretical constraints of the interactions between coupled cortical columns. Each cortical column consists of a set of neural populations where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column is dependent on the type of interaction between the neuronal populations, i.e., the form of the synaptic kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. The interaction between semi-stable states of the cortical columns is studied where we derive the dynamics for the collected activity. For small excitations the dynamics follow the Kuramoto model; however, in contrast to previous work we derive coupled equations between phase and amplitude dynamics with the possibility of defining connectivity as a stationary and dynamic variable. The turbulent flow of phase dynamics which occurs in networks of Kuramoto oscillators would indicate turbulent changes in dynamic connectivity for coupled cortical columns which is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagation model which allowed for inversions using the Laplace assumption (Dynamic Causal Modelling). The seizure propagation model was trialed on simulated data, and future work will investigate the estimation of the connectivity matrix from empirical data. This model can be used to predict changes in seizure evolution after virtual changes in the connectivity network, something that could be of clinical use when applied to epilepsy surgical cases.
