The representational capacity and inherent function of any neuron, neuronal population or cortical area in the brain is dynamic and context‐sensitive. Functional integration, or interactions among brain systems, that employ driving (bottom up) and backward (top‐down) connections, mediate this adaptive and contextual specialisation. A critical consequence is that neuronal responses, in any given cortical area, can represent different things at different times. This can have fundamental implications for the design of brain imaging experiments and the interpretation of their results. Our arguments are developed under generative models of brain function, where higher‐level systems provide a prediction of the inputs to lower‐level regions. Conflict between the two is resolved by changes in the higher‐level representations, which are driven by the ensuing error in lower regions, until the mismatch is “cancelled”. From this perspective the specialisation of any region is determined both by bottom‐up driving inputs and by top‐down predictions. Specialisation is therefore not an intrinsic property of any region but depends on both forward and backward connections with other areas. Because the latter have access to the context in which the inputs are generated they are in a position to modulate the selectivity or specialisation of lower areas. The implications for classical models (e.g., classical receptive fields in electrophysiology, classical specialisation in neuroimaging and connectionism in cognitive models) are severe and suggest these models may provide incomplete accounts of real brain architectures. Here we focus on the implications for cognitive neuroscience in the context of neuroimaging.
ANATOMICAL and physiological studies have shown that there is an area specialized for the processing of colour (area V4) in the prestriate cortex of macaque monkey brain1. Earlier this century, suggestive clinical evidence for a colour centre in the brain of man2,3 was dismissed4-8 because of the association of other visual defects with the defects in colour vision4,5,7. However, since the demonstration of functional specialization in the macaque cortex9, the question of a colour centre in man has been reinvestigated,based on patients with similar lesions in the visual cortex10-12. In order to study the colour centre in normal human subjects, we used the technique of positron emission tomography (PET), which measures increases in blood flow resulting from increased activity in the cerebral cortex. A comparison of the results of PET scans of subjects viewing multi-coloured and black-and-white displays has identified a region of normal human cerebral cortex specialized for colour vision.
Depression is a chronic disorder that impacts millions worldwide. Traditional treatments may not always work. Inflammation seems to be an underlying cause for chronicity and treatment non-response. We present a computational model that elucidates the interplay between inflammation, serotonin levels, and brain activity. The model delineates how inflammation impacts extracellular serotonin, while cerebral activity reciprocally influences serotonin concentration. Understanding the reciprocal interplay between the immune system and brain dynamics is important, as unabated inflammation can lead to relapsing depression. The model predicts dynamics within the prefrontal cortex (PFC) and subcallosal cingulate cortex (SCC), mirroring patterns observed in depressive conditions. It also accommodates pharmaceutical interventions that encompass anti-inflammatory and antidepressant agents, concurrently evaluating their efficacy with regard to the severity of depressive symptoms Our model shows that for mild and moderate levels of depression anti-depressant agents or anti-inflammatory agents acting in isolation can bring serotonergic levels and brain activity to control levels. However, for severe depression only joint treatment of anti-depressant and anti-inflammatory agents can bring the serotonergic levels and activity to control levels. This study is a first step to mechanistically understand the intricate link between the immune system and depression, the role of inflammation and potential treatments. It explores the impact of anti-depressant and anti-inflammatory drug treatments and assesses their relevance with regard to depression severity.
In this paper, complex dynamical synchronization in a non-linear model of a neural system is studied, and the computational significance of the behaviours is explored. The local neural dynamics is determined by voltage-and ligand-gated ion channels and feedback between densely interconnected excitatory and inhibitory neurons. A mesoscopic array of local networks is modelled by introducing coupling between the local networks via weak excitatory-to-excitatory connectivity. It is shown that with modulation of this long-range synaptic coupling, the system undergoes a transition from independent oscillations to stable chaotic synchronization. Between these states exists a 'weakly' stable state associated with complex, intermittent behaviour in the temporal domain and clusters of synchronous regions in the spatial domain. The paper concludes with a discussion of the putative relevance of such processes in the brain, including the role of neuromodulatory systems and the mechanisms underlying sensory perception, adaptation, computation and complexity.
This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.
This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamics – based on transmembrane currents – with neural field equations, describing the propagation of spikes over the cortical surface. This model allows for fairly realistic inter-and intra-laminar intrinsic connections that underlie spatiotemporal neuronal dynamics. We focus on the response functions of expected neuronal states (such as depolarisation) that generate observed electrophysiological signals (like LFP recordings and EEG). These response functions characterise the model’s transfer functions and implicit spectral responses to (uncorrelated) input. Our main finding is that both the evoked responses (impulse response functions) and induced responses (transfer functions) show qualitative differences depending upon whether one uses a neural mass or field model. Furthermore, there are differences between the equivalent convolution and conductance models. Overall, all models reproduce a characteristic increase in frequency, when inhibition was increased by increasing the rate constants of inhibitory populations. However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the opposite effect. These differences suggest that conductance based field models may be important in empirical studies of cortical gain control or pharmacological manipulations.
Statistical parametric mapping is the application of Random Field Theory to make inferences about the topological features of statistical processes that are continuous functions of space or time. It is usually used to identify regionally specific effects (e.g., brain activations) in neuroimaging data to characterize functional anatomy and disease-related changes.
The brain appears to adhere to two principles of functional organization; functional segregation and functional integration . The integration within and between functionally specialized areas is mediated by functional or effective connectivity . The characterization of this sort of connectivity is an important theme in many areas of neuroscience. This article presents one approach that has been used in functional imaging. This article reviews the basic distinction between functional and effective connectivity (as the terms are used in neuroimaging) and their role in addressing several aspects of functional organization (e.g. the topography of distributed system, integration between cortical areas, time‐dependent changes in connectivity and nonlinear interactions). Emphasis is placed on the points of contact between the apparently diverse applications of these concepts and in particular the central role of eigenimages or spatial modes. Although the framework that has been developed is inherently linear, it has been extended to assess nonlinear interactions among cortical areas. ©1994 Wiley‐Liss, Inc.
This article proposes a formal model that integrates cognitive and psychodynamic psychotherapeutic models of psychopathy to show how two major psychopathic traits called
In previous papers, we proposed that the dorsal attention system's top-down control is regulated by the dorsal division of the limbic system, providing a feedforward or impulsive form of control generating expectancies during active inference. In contrast, we proposed that the ventral attention system is regulated by the ventral limbic division, regulating feedback constraints and error-correction for active inference within the neocortical hierarchy. Here, we propose that these forms of cognitive control reflect vertical integration of subcortical arousal control systems that evolved for specific forms of behavior control. The feedforward impetus to action is regulated by phasic arousal, mediated by lemnothalamic projections from the reticular activating system of the lower brainstem, and then elaborated by the hippocampus and dorsal limbic division. In contrast, feedback constraint-based on environmental requirements-is regulated by the tonic activation furnished by collothalamic projections from the midbrain arousal control centers, and then sustained and elaborated by the amygdala, basal ganglia, and ventral limbic division. In an evolutionary-developmental analysis, understanding these differing forms of active affordance-for arousal and motor control within the subcortical vertebrate neuraxis-may help explain the evolution of active inference regulating the cognition of expectancy and error-correction within the mammalian 6-layered neocortex.
