How Continuous Concreteness Shapes Brain Processing and Concept Representation Across Diverse Tasks: Insights from an ERP Study Behavioural research over several decades has found that concrete words are processed more quickly and accurately than abstract words (concreteness effect). This advantage is also reflected in their different processing and representation in the human brain. In this electrophysiological study, we explored the interplay of concreteness as a continuous measure and task effects on word processing. Electrophysiological responses of 58 participants were recorded during semantic, affective, and grammatical decision tasks on words parametrically varied in concreteness. Our findings uncovered a fine-grained concreteness effect within four distinct spatiotemporal windows when a coding of semantic information is required. In the semantic decision task, we detected a higher parietal positivity within the P300 time range and an increased left temporo-lateral negative-going amplitude for less concrete concepts. We also identified a concreteness effect characterised by greater negativity in the N400 component. In the affective task, we observed a more pronounced parietal P600-like component for more abstract concepts. Interestingly, we found that the neural representational similarity (starting from 450 ms with a widespread scalp distribution) conformed to the concreteness similarity among concepts (regardless of the task), implying that their semantic representations may be characterised along the concreteness dimension in the brain. Our study transcends the conventional dichotomy of abstract versus concrete words, unearthing not only varied processing dynamics along the concreteness dimension but also distinct fine-grained neural representations. This novel insight positions concreteness as a structural dimension, enriching our comprehension of how the brain organises and processes semantic information.Show less
Are Familiar Objects More Likely to Be Noticed in an Inattentional Blindness Task? People often fail to notice the presence of unexpected objects when their attention is engaged elsewhere. In dichotic listening tasks, for example, people often fail to notice unexpected content in the ignored speech stream even though they occasionally do notice highly familiar stimuli like their own name (the “cocktail party” effect). Some of the first studies of inattentional blindness were designed as a visual analog of such dichotic listening studies, but relatively few inattentional blindness studies have examined how familiarity affects noticing. We conducted four preregistered inattentional blindness experiments (total N = 1700) to examine whether people are more likely to notice a familiar unexpected object than an unfamiliar one. Experiment 1 replicated evidence for greater noticing of upright schematic faces than inverted or scrambled ones. Experiments 2–4 tested whether participants from different pairs of countries would be more likely to notice their own nation’s flag or petrol company logo than those of another country. These experiments repeatedly found little or no evidence that familiarity affects noticing rates for unexpected objects. Frequently encountered and highly familiar stimuli do not appear to overcome inattentional blindness.
Awareness is needed for contextual effects in ambiguous object recognition Despite its centrality to human experience, the functional role of conscious awareness is not yet known. One hypothesis suggests that consciousness is necessary for allowing high-level information to refine low-level processing in a “top-down” manner. To test this hypothesis, in this work we examined whether consciousness is needed for integrating contextual information with sensory information during visual object recognition, a case of top-down processing that is automatic and ubiquitous to our daily visual experience. In three experiments, 137 participants were asked to determine the identity of an ambiguous object presented to them. Crucially, a scene biasing the interpretation of the object towards one option over another (e.g., a picture of a tree when the object could equally be perceived as a fish or a leaf) was presented either before, after, or alongside the ambiguous object. In all three experiments, the scene biased perception of the ambiguous object when it was consciously perceived, but not when it was processed unconsciously. The results therefore suggest that conscious awareness may be needed for top-down contextual processes.
Is attention necessary for the representational advantage of good exemplars over bad exemplars? Real-world (rw-) statistical regularities, or expectations about the visual world learned over a lifetime, have been found to be associated with scene perception efficiency. For example, good (i.e., highly representative) exemplars of basic scene categories, one example of an rw-statistical regularity, are detected more readily than bad exemplars of the category. Similarly, good exemplars achieve higher multivariate pattern analysis (MVPA) classification accuracy than bad exemplars in scene-responsive regions of interest, particularly in the parahippocampal place area (PPA). However, it is unclear whether the good exemplar advantages observed depend on or are even confounded by selective attention. Here, we ask whether the observed neural advantage of the good scene exemplars requires full attention. We used a dual-task paradigm to manipulate attention and exemplar representativeness while recording neural responses with functional magnetic resonance imaging (fMRI). Both univariate analysis and MVPA were adopted to examine the effect of representativeness. In the attend-to-scenes condition, our results replicated an earlier study showing that good exemplars evoke less activity but a clearer category representation than bad exemplars. Importantly, similar advantages of the good exemplars were also observed when participants were distracted by a serial visual search task demanding a high attention load. In addition, cross-decoding between attended and distracted representations revealed that attention resulted in a quantitative (increased activation) rather than qualitative (altered activity patterns) improvement of the category representation, particularly for good exemplars. We, therefore, conclude that the effect of category representativeness on neural representations does not require full attention.
Shared input and recurrency in neural networks for metabolically efficient information transmission Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.
The role of gap junctions and clustered connectivity in emergent synchronisation patterns of inhibitory neuronal networks Inhibitory interneurons, ubiquitous in the central nervous system, form networks connected through both chemical synapses and gap junctions. These networks are essential for regulating the activity of principal neurons, especially by inducing temporally patterned dynamic states. We aim to understand the dynamic mechanisms for synchronisation in networks of electrically and chemically coupled interneurons. We use the exact mean-field reduction to derive a neural mass model for both homogeneous and clustered networks. We first analyse a single population of neurons to understand how the two couplings interact with one another. We demonstrate that the network transitions from an asynchronous to a synchronous regime either by increasing the strength of the gap junction connectivity or the strength of the background input current. Conversely, the strength of inhibitory synapses affects the population firing rate, suggesting that electrical and chemical coupling strengths act as complementary mechanisms by which networks can tune synchronous oscillatory behavior. In line with previous work, we confirm that the depolarizing spikelet is crucial for the emergence of synchrony. Furthermore, find that the fast frequency component of the spikelet ensures robustness to heterogeneity. Next, inspired by the existence of multiple interconnected interneuron subtypes in the cerebellum, we analyse networks consisting of two clusters of cell types defined by differing chemical versus electrical coupling strengths. We show that breaking the electrical and chemical coupling symmetry between these clusters induces bistability, so that a transient external input can switch the network between synchronous and asynchronous firing. Together, our results shows the variety of cell-intrinsic and network properties that contribute to synchronisation of interneuronal networks with multiple types of coupling.
A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org)
Differential contribution of THIK-1 K+ channels and P2X7 receptors to ATP-mediated neuroinflammation by human microglia Neuroinflammation is highly influenced by microglia, particularly through activation of the NLRP3 inflammasome and subsequent release of IL-1β. Extracellular ATP is a strong activator of NLRP3 by inducing K+ efflux as a key signaling event, suggesting that K+-permeable ion channels could have high therapeutic potential. In microglia, these include ATP-gated THIK-1 K+ channels and P2X7 receptors, but their interactions and potential therapeutic role in the human brain are unknown. Using a novel specific inhibitor of THIK-1 in combination with patch-clamp electrophysiology in slices of human neocortex, we found that THIK-1 generated the main tonic K+ conductance in microglia that sets the resting membrane potential. Extracellular ATP stimulated K+ efflux in a concentration-dependent manner only via P2X7 and metabotropic potentiation of THIK-1. We further demonstrated that activation of P2X7 was mandatory for ATP-evoked IL-1β release, which was strongly suppressed by blocking THIK-1. Surprisingly, THIK-1 contributed only marginally to the total K+ conductance in the presence of ATP, which was dominated by P2X7. This suggests a previously unknown, K+-independent mechanism of THIK-1 for NLRP3 activation. Nuclear sequencing revealed almost selective expression of THIK-1 in human brain microglia, while P2X7 had a much broader expression. Thus, inhibition of THIK-1 could be an effective and, in contrast to P2X7, microglia-specific therapeutic strategy to contain neuroinflammation.