Publications

Shared Protentions in Multi-Agent Active Inference

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

Activation of reward circuitry in human opiate addicts

Abstract The neurobiological mechanisms of opiate addictive behaviour in humans are unknown. A proposed model of addiction implicates ascending brainstem neuromodulatory systems, particularly dopamine. Using functional neuroimaging, we assessed the neural response to heroin and heroin‐related cues in established opiate addicts. We show that the effect of both heroin and heroin‐related visual cues are maximally expressed in the sites of origin of ascending midbrain neuromodulatory systems. These context‐specific midbrain activations predict responses to salient visual cues in cortical and subcortical regions implicated in reward‐related behaviour. These findings implicate common neurobiological processes underlying drug and drug‐cue‐related effects.

Representation and agency

Abstract Gilead et al. raise some fascinating issues about representational substrates and structures in the predictive brain. This commentary drills down on a core theme in their arguments; namely, the structure of models that generate predictions. In particular, it highlights their factorial nature – both in terms of deep hierarchies over levels of abstraction and, crucially, time – and how this underwrites agency.

Granger causality revisited

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling.

The Dynamical and Structural Basis of Brain Activity

Global network dynamics over distributed brain areas emerge from the local dynamics of each brain area.Conversely, global dynamics constrain local activity such that the whole system becomes self-organizing.The implicit coupling between local and global scales induces a form of circular causality that is characteristic of complex, coupled systems that show self-organization, such as the brain.Here we present a network model based on spiking neurons at the local level and large-scale anatomic connectivity matrices at the global level.We demonstrate that this multiscale network displays endogenous or autonomous dynamics of the sort observed in resting-state studies.Our special focus here is on the genesis of itinerant (wandering) dynamics and the role of multistable attractors, which are involved in the generation of empirically known functional connectivity patterns, if the global coupling causes the dynamics to operate in the critical regime.Our results provide once again support for the hypothesis that endogenous brain activity is critical.

Quantification of degeneracy in Hodgkin–Huxley neurons on Newman–Watts small world network

A pet study of word generation in obligate carriers of the predisposition to schizophrenia

PsPM-SCRV6: Skin conductance responses to pain by electric stimulation

This dataset includes skin conductance response (SCR) measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 21.8+/-3.3 years) in response to 10 discomforting electric shocks. Stimuli are 0.5ms wide square current pulse repeated at 500Hz for 100ms. Amplitude is varied (mean +/- SD: 0.78mA +/- 0.43mA). ITI is selected randomly on each trial from 29s, 34s or 39s.

The Relationship between Global and Local Changes in PET Scans

In order to localize cerebral cognitive or sensorimotor function, activation paradigms are being used in conjunction with PET measures of cerebral activity (e.g., rCBF). The changes in local cerebral activity have two components: a global, region independent change and a local or regional change. As the first step in localizing the regional effects of an activation, global variance must be removed by a normalization procedure. A simple normalization procedure is division of regional values by the whole brain mean. This requires the dependence of local activity on global activity to be one of simple proportionality. This is shown not to be the case. Furthermore, a systematic deviation from a proportional relationship across brain regions is demonstrated. Consequently, any normalization must be approached on a pixel-by-pixel basis by measuring the change in local activity and change in global activity. The changes associated with an activation can be partitioned into global and local effects according to two models: one assumes that the increase in local activity depends on global values and the other assumes independence. It is shown that the increase in activity due to a cognitive activation is independent of global activity. This independence of the (activation) condition effect and the confounding linear effect of global activity on observed local activity meet the requirements for an analysis of covariance, with the “nuisance” variable as global activity and the activation condition as the categorical independent variable. These conclusions are based on analysis of data from 24 scans: six conditions over four normal subjects using a verbal fluency paradigm. A technique is described based on ANCOVA and using statistical parametric mapping to localize foci in the brain that have been significantly perturbed by the cognitive tasks. This technique represents a fundamental and necessary departure from ROI-based approaches allowing the separation of global and local effects pixel by pixel, and provides an image of affected regions whose significance can be quantified. The specificity and sensitivity of the described method of change detection is assessed.

Author response: A Bayesian model of context-sensitive value attribution

Positron Emission Tomography in Psychopharmacology

Positron Emission Tomography (PET) represents a major advance in neuroimaging techniques enabling for the first time the quantitative acquisition of in. vivo pharmacological information in humans. This review will outline the range of its applications as a pharmacological tool with specific reference to its use in the measurement of neuroreceptors and in pharmacodynamic studies. A new and potentially more exciting application of PET is in pharmacological challenge combined with cognitive activation paradigms. The key issues in the design and implementation of this latter approach are detailed.

Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys

We apply Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys performing a problem-solving task that engages working memory, and induces time-on-task effects. We thus provide a computational account of changes in effective connectivity within two regions of the fronto-parietal network, the dorsolateral prefrontal cortex and the pre-supplementary motor area. We find that forward connections between the two regions increased in strength when task demands increased, and as the experimental session progressed. Similarities in the effects of task demands and time on task allow us to interpret changes in frontal connectivity in terms of increased attentional effort allocation that compensates cognitive fatigue.

Celebration of the 25th anniversary of <i>Neuropsychoanalysis</i> : <i>Saturday, December 2, 2023</i>

Gene deletion mapping of the X chromosome

THE COLOR CENTER IN THE PRESTRIATE CORTEX OF MAN

Anatomically informed basis functions in multisubject studies

We describe the use of anatomically informed basis functions (AIBF) in the analysis of multisubject functional imaging studies. AIBF are used to specify an anatomically informed spatial model that embodies anatomical knowledge for the statistical analysis of neuroimaging data. In a previous communication, we showed how AIBF can be used to incorporate prior anatomical constraints in single subject functional magnetic resonance image (fMRI) analyses to augment their anatomical precision. In this paper, we extend AIBF such that it can be applied to multisubject studies using fMRI or PET. The key concept is that, after spatial normalization, a canonical cortical surface can be used to generate a forward model of signal sources for all subjects. By estimating the hemodynamic signal in this canonical AIBF-space and then projecting it back into the voxel-space, one effectively extracts functional activity that is smooth, within and only within, the cortical sheet while attenuating other components unrelated to the physiological process of interest. The ensuing procedure can be considered as a highly non-stationary, anisotropic anatomically informed [de]convolution or smoothing. It is shown that this procedure offers various advantages compared to existing conventional methods for the analysis of multisubject studies, in particular it is more sensitive to underlying activations.

Functional Imaging Studies of Neuropsychological Patients: Applications and Limitations

Functional imaging studies of neuropsychologically impaired patients have not enjoyed the immediate success that was attained by functional imaging studies of normal subjects. This is largely because it has taken time to appreciate some of the deeper issues surrounding study design, analysis and interpretation. The most significant limitation is that functional imaging experiments with patients need tasks that the patients can perform. This precludes direct investigations of the physiological correlates of cognitive deficits. Nevertheless, functional imaging studies of brain-damaged patients who retain task competence can provide information that is not available from structural imaging, behavioural assessments or functional imaging with normal subjects. This is because intact task performance, following a brain lesion, does not necessarily entail normal neuronal responses in undamaged cortical areas. Abnormal neuronal responses, in the context of normal performance, can indicate alternative neuronal and cognitive mechanisms for supporting the same task. This, in turn, has important implications for understanding the mechanisms that mediate recovery and the organizational principles that underlie functional architectures in the human brain.

Editorial: Probabilistic Perspectives on Brain (Dys)function

EDITORIAL article Front. Artif. Intell., 07 June 2021Sec. Medicine and Public Health https://doi.org/10.3389/frai.2021.710179