Integrated Information Theory is a theoretical framework proposing that consciousness is a fundamental property of systems capable of integrating information. To bridge the gap between the theoretical concept and the practical use in actual neurobiological systems, we have applied the Integrated Information Theory approach to a simulated network of integrate and fire neurons (IAF). The primary contribution of this study is several empirical findings. Our analysis shows that such a network can possess a non-zero Φ value under certain conditions and parameter settings. Additionally, our research indicates that the complexity of the network's dynamics doesn't necessarily correlate with its Φ value. On the other hand, the quantity of integrated information within the network appears to grow with the IAF neurons' time constant, which reflects their integrative capacity. Furthermore, our examination of the integrate and fire network with internal random fluctuations demonstrates that the integrated information measure, as defined in IIT version 3.0, is not resilient to noise.

Neural circuits often alternate between tonic and burst firing, two distinct activity regimes that reflect changes in excitability and neuromodulatory state. While tonic firing produces asynchronous spiking driven by diverse external inputs, collective burst firing consists of rapid clusters of spikes followed by a period of silence, happening synchronously within the network. Synaptic plasticity has typically been studied only in either one of these regimes, leaving unclear how their distinct plasticity dynamics can be combined when circuits alternate between regimes. Here, we use a conductance-based network model endowed with calcium-based or spike-timing-based plasticity rules to examine how synaptic weights evolve across tonic and burst firing regimes. During tonic firing, synaptic weights are driven by the statistics of external inputs, producing a broad distribution across the network. In contrast, during collective burst firing, weights converge to a narrow region in weight space: a burst-induced attractor. We derive the location of this attractor analytically in terms of plasticity parameters and activity statistics, and confirm its emergence across diverse plasticity rules. The attractor reflects the synchronization of plasticity-driving signals during bursts, which homogenizes synaptic dynamics and forces convergence toward shared fixed points. We further show that neuromodulation and synaptic tagging can shift or split the burst-induced attractor, stabilizing selected synapses while weakening others. Together, these results identify burst-induced attractors as a robust emergent property of collective bursting. Alternation between tonic and burst firing provides a biologically plausible context in which heterogeneous, input-driven synaptic configurations formed during tonic activity can be selectively consolidated or down-selected by the burst-induced attractor during subsequent bursts. By showing how they can be analytically predicted and experimentally modulated, our work provides a general computational framework linking firing state transitions, synaptic plasticity, and memory organization.

Although the cortico-basal ganglia-thalamic (CBGT) network is identified as a central circuit for decision-making, the dynamic interplay of multiple control pathways within this network in shaping decision trajectories remains poorly understood. Here we develop and apply a novel computational framework-CLAW (Circuit Logic Assessed via Walks)-for tracing the instantaneous flow of neural activity as it progresses through CBGT networks engaged in a virtual decision-making task. Our CLAW analysis reveals that the complex dynamics of network activity is functionally dissectible into two critical phases: deliberation and commitment. These two phases are governed by distinct contributions of underlying CBGT pathways, with indirect and pallidostriatal pathways influencing deliberation, while the direct pathway drives action commitment. We translate CBGT dynamics into the evolution of decision-related policies, based on three previously identified control ensembles (responsiveness, pliancy, and choice) that encapsulate the relationship between CBGT activity and the evidence accumulation process. Our results demonstrate two contrasting strategies for decision-making. Fast decisions, with direct pathway dominance, feature an early response in both boundary height and drift rate, leading to a rapid collapse of decision boundaries and a clear directional bias. In contrast, slow decisions, driven by indirect and pallidostriatal pathway dominance, involve delayed changes in both decision policy parameters, allowing for an extended period of deliberation before commitment to an action. These analyses provide important insights into how the CBGT circuitry can be tuned to adopt various decision strategies and how the decision-making process unfolds within each regime.

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. Although these phenomena have been extensively investigated experimentally and theoretically, the principles governing how E and I inputs are integrated remain unclear. Here, probabilistic rules are derived to describe how neurons in feedforward inhibitory circuits combine these inputs to generate stimulus-evoked responses. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models, and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns.

Identifying the key order parameters that connect a protein's native structure to its dynamical and evolutionary behavior remains a central challenge. We introduce topological and geometrical metrics-specifically, writhe and Local Topological Energy (LTE)-to investigate these connections. Applying these tools to both present-day and ancestral forms of thioredoxin and β-lactamase, we show that LTE strongly correlates with established dynamical measures such as the Dynamical Flexibility Index (DFI). Remarkably, LTE distributions also track the evolutionary trajectories of these proteins, suggesting that the topological geometry of the native state encodes key aspects of both dynamics and evolution. Through molecular dynamics simulations, we further reveal critical shifts in the topological landscape of proteins, providing a molecular mechanism by which functional evolution proceeds via alterations in conformational dynamics. Extending our analysis to over 100 proteins, we provide the first compelling evidence that topological descriptors derived from static structures can reliably predict dynamical behavior. In general, our findings demonstrate that simple geometrical metrics capture essential features of protein conformational landscapes, offering a powerful new approach to bridging protein structure, dynamics, and evolution.

SARS-CoV-2 infection rates displayed strikingly organized patterns of temporal and spatial spread as new variants were introduced and subsequently transmitted within the United States. While thesespatio-temporal"waves" of infection have been described previously, attempts to quantify the speed and extent of these waves have been limited. Here, we estimate and compare the wavefront speed and spatial expansion of the first two major infection waves in the United States, illustrating these dynamics through detailed visualizations. Our findings reveal that the origins of these waves coincide with large gatherings and the relaxation of masking mandates. Notably, we found that the second wave spread more rapidly than the first, possibly driven by multiple introduction events. These analyses highlight regional heterogeneity in epidemic dynamics and underscore the importance of localized public health measures in mitigating ongoing outbreaks.

Flying insects demonstrate exceptional proficiency in detecting and pursuing conspecifics and prey within a cluttered environment, inspiring the development of computational models for small object detection. While existing bioinspired models are dedicated to resolving small moving instead of stationary object detection, few studies have systematically explored the role of visual motion in detection. Here, we developed a fly-inspired model on the basis of the hypothesis that combining visual motion features and luminance features is critical for small moving object detection. We thoroughly investigated the effect of feature combination under diverse stimulus conditions. Simulations indicated that the model exhibited hyperacute object detection, a capability not generally believed to emerge on the basis of motion detection. When tested with a moving background in realistic scenarios, the model demonstrated enhanced efficiency and robustness relative to models relying solely on luminance features. This enhancement was independent of whether visual motion was extracted by two- or three-arm motion detectors. The results suggested that small object detectors within the visual systems of flying insects could be optimally tuned to utilize the limited features inherent to tiny objects.

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression, given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, coefficient of variance (CV), CV2, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is independent of dataset size. In contrast, the Gini index displayed paradoxical behavior in that it increases as dispersion decreases, and Shannon entropy was not scale-invariant. Next, we applied the variance-to-mean ratio (Fano factor) to measure transcriptional variability in single-cell datasets representing different complex systems and cross-platform measurements. Our data show that many genes display transcriptional variability within the same cell type, and that while variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age, variable genes are often correlated with specific biological processes. Notably, most genes and pathways with highest transcriptional variability as identified by the Fano factor were largely independent of differentially expressed genes and have also been implicated in biological processes related to the system. Thus, our data highlight that choice and application of appropriate models for measuring transcriptional variation in scRNA-seq data can reveal biologically relevant information beyond what is observed from mean expression alone.

Processing of electroencephalography (EEG) data requires multiple steps to remove noise and artifacts and select good-quality data. While powerful open-source toolboxes like MNE-Python exist, their command-line nature can pose a barrier for researchers without programming experience. Here, we present EEG-Pype, an open-source (Apache-2.0 licensed) graphical user interface application using MNE-Python functions. EEG-Pype provides an intuitive workflow tailored for preprocessing of resting-state EEG data, including frequency band filtering, independent component analysis and atlas-based beamforming for source-level analysis. The application supports several common raw EEG input file formats and guides users through a comprehensive pipeline focused on manual bad channel and epoch selection. Manual steps are streamlined using MNE-Python's interactive plots, resulting in a user-friendly experience. Configuration saving and loading allows for batch (re)runs, while a separate log is also saved, improving reproducibility and documentation. Output can be saved after filtering in canonical frequency bands, ready for further analysis. EEG-Pype includes a module for calculating quantitative EEG measures on preprocessed data, including spectral, functional connectivity and network analysis metrics. It aims to lower the entry barrier for standardized EEG preprocessing, promoting reproducible research practices among neuroscientists and clinicians without requiring programming knowledge. EEG-Pype can be downloaded from: https://github.com/yorbenlodema/EEG-Pype and is not dependent on a specific operating system.

The distribution of fitness effects (DFE) of new beneficial mutations is a key quantity that dictates the dynamics of adaptation. The barcode lineage tracking (BLT) approach is an important advance toward measuring DFEs. BLT experiments enable researchers to track the frequencies of ~105 barcoded lineages in large microbial populations and detect up to thousands of nascent beneficial mutations in a single experiment. However, reliably identifying adapted lineages and estimating the fitness effects of driver mutations remains a challenge because lineage dynamics are subject to demographic and measurement noise and competition with other lineages. We show that the commonly used Levy-Blundell method for analyzing BLT data and its improved version FitMut2 can produce biased fitness estimates, particularly if selection is strong. To address this problem, we develop a new method called BASIL (BAyesian Selection Inference for Lineage tracking data), which dynamically updates the belief distribution of each lineage's fitness and size based on the number of barcode reads. We calibrate BASIL's model of noise with new experimental data and find that noise variance scales non-linearly with lineage abundance. We test how BASIL and FitMut2 perform on simulated data and on down-sampled data from the original BLT data by Levy et al and find that BASIL is both more robust and more accurate than FitMut2. Our work paves the way for a systematic inference of the distribution of fitness effects of new beneficial mutations from BLT experiments in a variety of scenarios.