1,987 publications from this institution
Neural models describe brain activity at different scales, ranging from single cells to whole brain networks. Here, we attempt to reconcile models operating at the microscopic (compartmental) and mesoscopic (neural mass) scales to analyse data from microelectrode recordings of intralaminar neural activity. Although these two classes of models operate at different scales, it is relatively straightforward to create neural mass models of ensemble activity that are equipped with priors obtained after fitting data generated by detailed microscopic models. This provides generative (forward) models of measured neuronal responses that retain construct validity in relation to compartmental models. We illustrate our approach using cross spectral responses obtained from V1 during a visual perception paradigm that involved optogenetic manipulation of the basal forebrain. We find that the resulting neural mass model can distinguish between activity in distinct cortical layers – both with and without optogenetic activation – and that cholinergic input appears to enhance (disinhibit) superficial layer activity relative to deep layers. This is particularly interesting from the perspective of predictive coding, where neuromodulators are thought to boost prediction errors that ascend the cortical hierarchy.
This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectivity matrices (from diffusion spectrum imaging) and investigate their capacity to generate various types of resting state activity. In particular, we study emergent patterns of activity for realistic connectivity configurations together with approximations formulated in terms of neural mass or field models. We find that homogenous connectivity matrices, of the sort of assumed in certain neural field models give rise to damped spatially periodic modes, while more localised modes reflect heterogeneous coupling topologies. When simulating resting state fluctuations under realistic connectivity, we find no evidence for a spectrum of spatially periodic patterns, even when grouping together cortical nodes into communities, using graph theory. We conclude that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain.
Position emission tomography measurements of regional cerebral blood flow (rCBF) were performed in normal volunteers during two auditory—verbal memory tasks: a subspan and supraspan task. The difference in rCBF between tasks was used to identify brain areas/systems involved in auditory—verbal long-term memory. Increases in rCBF were observed in the left and right prefrontal cortex, precuneus and the retrosplenial area of the cingulate gyrus. Decreases in blood flow were centred in the superior temporal gyrus bilaterally. Separate comparisons were also made between each span task and a resting state. Brain regions showing increases in rCBF in these comparisons included the thalamus, left anterior cingulate, right parahippocampal gyrus, cerebellum and the superior temporal gyrus. The brain areas identified in these comparisons define a number of the neuroanatomical components of a distributed system for signal processing and storage relevant to auditory—verbal memory function.
Abstract Introduction In an effort to build intelligent, autonomous systems, robotics has repeatedly used neuroscience as a source of inspiration for perception and control. This corresponds well with advances in neuroscience, particularly the free energy principle; a unified theory of the brain, integrating together notions of action, perception, and learning. Crucially for robotics, it provides a simple recipe to synthesise autonomous agents under the framework of active inference. Method We conducted a literature search assessing the practicality of autonomous agency within surgical robotics, and to understand the potential of applying the free energy principle to surgical robotics in a bid to optimise locomotion, spatial awareness, planning, and artificial curiosity. Results Numerous real-life examples demonstrate the feasibility of applying bayesian inference to humanoid robotics. Here we demonstrate a similar approach to adapting surgical robotics through the representation of sensory data through a probability density function over a group of unknown variables. In this manner, surgical robotics will be able to utilise sensory signals and can provide different weights to sensory cues, thereby efficiently integrating sensory input over space and time. Conclusions The free energy principle may be applied to surgical robotics to provide additional feedback, and control to improve patient prognosis.
Non-invasive physiological measures of in vivo brain function, derived from positron emission tomography (PET) and functional magnetic resonance (fMRI), are now standard tools in cognitive neuroscience. These techniques provide a powerful context for addressing critical questions regarding both the localization and mechanisms of higher brain functions. A general overview of the history and development of imaging techniques in relation to cognitive science is that of Posner & Raichle (1994). The conceptual background and methodological approaches available for neurobiological based psychiatric research, in conjunction with functional imaging, provides the focus for the present review.Major psychiatric disorders, such as schizophrenia and depression, represent disturbances at the highest level of brain function. Providing a neurobiological account of these conditions presents the most formidable problem in clinical neuroscience. Questions posed by neurobiological perspectives on psychiatric disorders are necessarily embedded in theoretical assumptions about how we think that the brain works. From this, it follows that an important limiting factor in any neurobiological account of psychiatric disease is the stage of development and theoretical conceptualization of brain function in general.
Abstract This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function—and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity—accompanied with adaptation of firing thresholds—is a sufficient neuronal substrate to attain Bayes optimal inference and control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.
