Recovery from disorders of consciousness: Lesions and GABAergic modulation in a biologically inspired spiking neural network The effects of positive allosteric modulators (PAMs) of the GABAA receptor in disorders of consciousness (DoC)—such as the hypnotic zolpidem in coma—are frequently reported but remain poorly understood. Although several hypotheses have been proposed to explain their paradoxical effect, biologically plausible computational models that test why only a subset of patients with DoC respond to inhibitory-enhancing medications may clarify this phenomenon and guide therapeutic strategies. We extended a spiking neural network model to mechanistically investigate inhibitory neuron enhancement in DoC. The model featured biologically inspired elements, including a ratio of 80:20 excitatory-to-inhibitory neurons and 12 Hz of spontaneous intrinsic activity. Neurons were spatially organized, and the network performed a trace conditioning task to investigate conscious access. We implemented localized and diffuse lesions affecting excitatory and inhibitory neuron populations, individually and in combination. The positive allosteric modulation of GABAA receptors was simulated by increasing inhibitory synaptic weights. Experiments revealed distinct recovery patterns depending on damage type and lesion distribution. Spontaneous recovery was more impaired by excitatory than inhibitory lesions, with local lesions generally affecting performance more than diffuse ones. Inhibitory postsynaptic potentiation produced dose-dependent recovery after inhibitory lesions, with therapeutic windows varying by lesion type. Our findings suggest that the paradoxical effect of GABAA receptor PAMs arises from the restoration of excitatory–inhibitory balance when inhibitory networks are moderately disrupted. This computational framework offers a testable account in which insufficient inhibitory regulation represents a common pathway underlying DoC cases responsive to GABAA receptor PAMs across diverse etiologies.
Pluralism within limits: how to make the idea of multiple NCC kinds useful Recently, several authors have argued that the current impasse in consciousness science supports a pluralistic interpretation: the view that seemingly contradictory findings may both be valid because consciousness can be brought about in different ways, each sufficient to generate conscious experience. We analyze different versions of pluralism to determine under what conditions it offers a viable interpretation of consciousness science. We argue that viable pluralism requires a two-factor framework distinguishing general consciousness factors (mechanisms rendering contents conscious) from content encoding itself. Moreover, neural correlates of consciousness (NCC) kinds must be finite in number and systematically ordered to enable a unified scientific model. While pluralism enables testable hypotheses, significant ontological tensions arise: how can consciousness constitute a unified natural kind if realized by fundamentally distinct NCC kinds? Drawing on philosophy of science, we propose an alternative interpretation in terms of scientific rather than natural kinds. Our goal is not to advocate for pluralism, but rather, to critically examine its viability and implications. Based on this, we urge the field to engage more explicitly with the foundational assumptions that shape consciousness research.
Resolving the Abstract–Concrete Paradox in the Angular Gyrus: A Multimethod Investigation Despite its well-established role in memory-guided cognition, whether and how the angular gyrus (AG) contributes to semantic processing remains unresolved. Connectomic work links the AG to abstract mentation, yet neuroimaging studies paradoxically show greater AG engagement for concrete than abstract semantics. To address this discrepancy, we conducted a multimethod investigation integrating neurostimulation, neuroimaging, and experience sampling across five studies (127 human participants; 53 males; 74 females). Using the contrast between concrete and abstract semantics as a diagnostic test case, we show that this apparent contradiction reflects multiple interacting neurocognitive factors shaping the AG’s functional repertoire. In Study 1, causal disruption of the AG disproportionately impaired abstract semantics and temporary retention of task-relevant information, demonstrating its role in abstract and memory-guided cognition. In Study 2, the apparent concreteness effect was abolished after controlling for performance speed/accuracy, indicating that AG activity is more strongly associated with low-demand mental states than semantic concreteness. In Study 3, the AG showed enhanced functional coupling with regions in the semantic network for abstract words, suggesting sustained integration with the semantic system despite reduced activation. In Study 4, experience sampling showed that concrete words are associated with experiences of mental imagery and automaticity, providing a phenomenological account of heightened AG engagement under low-demand states. Finally, Study 5 clarified how baseline choices influence the interpretation and polarity of AG activity. Together, these findings show that AG engagement is best understood along a continuum of memory-guided cognition, clarifying when and why this region supports abstract versus concrete semantics across situations.
Developmental reorganization of functional and structural connectivity in children’s language networks is consistent with interactive specialization Interactive Specialization hypothesizes that brain regions initially have poorly defined roles and gradually specialize through interactions with the environment and other brain regions during postnatal development. The mechanisms underlying language acquisition are without consensus, split between innate maturation and experience-dependent theories. We analyzed a four-year longitudinal dataset of children aged 5, 7, and 9, which included developmental surveys, psychoeducational tests, fMRI, and diffusion-weighted imaging data. We observed evidence of increased functional integration over time, with region-specific improvements in brain network efficiency and functional activity correlations changing substantially. Notably, the functional coupling between the left and right cerebral white matter regions changed significantly across this age window (), declining from a strong correlation of task-related BOLD activity at age 5 () to a substantially weaker correlation by age 9 (). Within language-associated networks, relay-involved tracts were consistently longer than non-relay tracts ( at each age) and this gap widened between ages 5 and 9, whereas no comparable difference or developmental trend was observed in non-language networks, indicating a network-specific reconfiguration toward a more economical wiring configuration. While findings support the theory of interactive specialization, no direct link was found between daily activities and either language outcomes or network efficiency, and associations with standardized language ability were small and mixed in direction.
Permissible presupposition When is it proper or acceptable for a speaker to presuppose a proposition in the course of performing a speech act? Taking Stalnaker’s common ground model as my background, I argue that this is best framed as a matter of the conversational permissibility of the speaker’s presupposition, and that we should not construe this permissibility in terms of the reasonableness of a speaker’s presupposition. Instead, I propose that the permissibility of a speaker’s presupposition is fixed by what is mutually conversationally salient to all participants, where this can in principle outstrip what any of the participants reasonably regards as mutually salient.
When word order matters: human brains represent sentence meaning differently from large language models Large language models based on the transformer architecture are now capable of producing human-like language. But do they encode and process linguistic meaning in a human-like way? Here, we address this question by analysing 7T fMRI data from 30 participants reading 108 sentences each. These sentences are carefully designed to disentangle sentence structure from word meaning, thereby testing whether transformer representations of sentence meaning resemble those formed by the brain. We found that while transformer models match brain representations better than models that completely ignore word order, all transformer models performed poorly overall. Further, transformers were significantly inferior to models explicitly designed to encode the structural relations between words. Our results provide insight into the nature of sentence representation in the brain, highlighting the critical role of sentence structure. They also cast doubt on the claim that transformers represent sentence meaning similarly to the human brain.
Medial temporal default mode network selectively encodes autobiographical visual imagery The human brain’s capacity to imagine visual scenes from memory is thought to rely on the medial temporal subsystem of the default mode network (MT-DMN), yet the neural codes supporting this ability remain poorly understood. We combined functional magnetic resonance imaging (fMRI) with vision and language artificial intelligence models to characterize neural codes during autobiographical imagination. Fifty participants imagined reexperiencing 20 natural scenarios while undergoing fMRI when cued by generic text prompts (e.g., wedding, exercising, and driving). Individual scenes were modeled using Stable Diffusion to generate personalized synthetic images from verbal descriptions of the scenarios imagined, collected beforehand. These depictions were then transformed into image recognition network hidden state representations. Representational similarity analysis revealed that the MT-DMN encoded the participant-specific representational structure of image representations, even when controlling for semantic features derived from a large language model. This effect was absent in other brain networks and during reading without active imagination, identifying the MT-DMN as a core substrate for the visual reconstruction of autobiographical experiences.
Reciprocal connections dynamically build consensus between neocortical areas The neocortex is organized into specialized areas. Although computations within individual areas have been well studied, it is unclear how these regions function collectively and reconcile potential conflicts to form coherent percepts and decisions. We investigated the joint dynamics of primary (V1) and higher-order lateromedial (LM) visual areas in mice using simultaneous multi-area electrophysiological recordings along with focal optogenetic perturbations to causally manipulate neural activity. We used data-driven nonlinear system identification to construct biologically constrained latent circuit models of both areas. This approach revealed that reciprocal excitatory connections between V1 and LM implement an approximate line attractor in their joint dynamics: this selectively slows the decay of congruent activity patterns while accelerating the decay of inconsistent ones, thereby dynamically achieving inter-area consensus. This mechanism predicts different timescales for consistent versus inconsistent activity patterns across areas, which we verified in our data. These findings, together with our mechanistic theory, identify dynamic consensus building as a general principle of distributed cortical computation.