Publications

Autism is a psychiatric syndrome characterized by impairments in three domains: social interaction, communication, and restricted and repetitive behaviours and interests. Recent findings implicate the amygdala in the neurobiology of autism. In this paper, we report the results of a series of novel experimental investigations focusing on the structure and function of the amygdala in a group of children with autism. The first section attempts to determine if abnormality of the amygdala can be identified in an individual using magnetic resonance imaging in vivo. Using single-case voxel-based morphometric analyses, abnormality in the amygdala was detected in half the children with autism. Abnormalities in other regions were also found. In the second section, emotional modulation of the startle response was investigated in the group of autistic children. Surprisingly, there were no significant differences between the patterns of emotional modulation of the startle response in the autistic group compared with the controls.

Tinnitus is a common disorder that often complicates hearing loss. Its mechanisms are incompletely understood. Current theories proposing pathophysiology from the ear to the cortex cannot individually - or collectively - explain the range of experimental evidence available. We propose a new framework, based on predictive coding, in which spontaneous activity in the subcortical auditory pathway constitutes a 'tinnitus precursor' which is normally ignored as imprecise evidence against the prevailing percept of 'silence'. Extant models feature as contributory mechanisms acting to increase either the intensity of the precursor or its precision. If precision (i.e., postsynaptic gain) rises sufficiently then tinnitus is perceived. Perpetuation arises through focused attention, which further increases the precision of the precursor, and resetting of the default prediction to expect tinnitus.

This paper considers the relationship between thermodynamics, information and inference. In particular, it explores the thermodynamic concomitants of belief updating, under a variational (free energy) principle for self-organization. In brief, any (weakly mixing) random dynamical system that possesses a Markov blanket—i.e. a separation of internal and external states—is equipped with an information geometry. This means that internal states parametrize a probability density over external states. Furthermore, at non-equilibrium steady-state, the flow of internal states can be construed as a gradient flow on a quantity known in statistics as Bayesian model evidence. In short, there is a natural Bayesian mechanics for any system that possesses a Markov blanket. Crucially, this means that there is an explicit link between the inference performed by internal states and their energetics—as characterized by their stochastic thermodynamics. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

Abstract Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes statistical dependencies between data, effective connectivity rests on a mechanistic model of the causal effects that generated the data. This review addresses the conceptual and methodological basis of established techniques for characterizing effective connectivity using functional magnetic resonance imaging (fMRI) data. In particular, we focus on dynamic causal modeling (DCM) of fMRI data and emphasize the importance of model selection procedures and nonlinear mechanisms for context‐dependent changes in connection strengths. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Neuroscience > Cognition

Abstract Gotts, Chow and Martin provide a very nice review of repetition priming and suppression that reaches a compelling conclusion—we need to look more closely at synchronization in learning and priming. Indeed, current modeling work focuses on this issue—namely, the dynamic causal modeling of electrophysiological responses to address the role of synchrony in Bayesian explaining away. This commentary revisits the nature and relationships among the four theories in Gotts et al. and nuances some of their empirical predictions. In particular, I emphasize precision or uncertainty in predictive coding as a unifying consideration.

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e., inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive - and alternate between - two distinct percepts. We hypothesised that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker's cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologues of particular neurotransmitters - i.e., acetylcholine, noradrenaline, dopamine - that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception.

Attempts to define a personality profile of individuals disposed principally to self-serving and antisocial behaviour can be traced at least as far back as Pinel's concept of 'manie sans delire'.1 Rush2 relabelled it as 'moral alienation of the mind', and contributed strongly to the Anglo-American conception of psychopathy as predominantly antisocial. Prichard3 concerned himself with the question of antisocial actions under the control of the will, and stimulated a debate that shaped the concept of criminal responsibility, though his term 'moral insanity' had a limited lifespan. Subsequently, Partridge4 suggested the designation 'sociopathy' for personality disturbances whose outstanding feature is antisocial behaviour. Cleckley,5 in his monograph The Mask of Sanity, proposed the use of the term 'psychopathy' (which had been coined by Koch,6 in 1891) for individuals showing lifelong maladaptive behaviour not caused by neurosis, psychosis or mental handicap. Cleckley's definitions were developed in the diagnostic and statistical manual of mental disorders (DSM) and International Classification of Diseases (ICD) schemes.

This paper shows how gamma oscillations can be combined with neural population models and <em>dynamic causal modeling</em> (DCM) to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to <em>simulate</em> neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields <em>quantitatively</em>—to <em>fit</em> empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG) data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.