Biology subjects

Kocillari, L.

Publications and source records attributed to Kocillari, L..

8 recordsLinked to original sources

bioRxiv2026

Redundant information across functionally coupled cortical networks supports rapid perceptual decisions in the ferret

Coordinated activity across cortical areas transforms sensory inputs into perceptual decisions, yet how task-relevant information is distributed across sites and linked to functional interactions and behavior remains unclear. Conventional functional connectivity measures reveal statistical dependencies between neural signals but cannot distinguish information encoded uniquely at individual sites, shared redundantly across sites, or available only from their joint activity. Here, we used Partial Information Decomposition (PID) to characterize stimulus information during fast and slow correct decisions. We analyzed local field potentials (LFPs) extracted from mesoscale electrocorticographic recordings from auditory, visual, and parietal cortices in ferrets performing a visual and audiovisual spatial-detection task. Time- and frequency-resolved analyses of local field potential power and phase showed that stimulus-side information was strongest in the theta and alpha bands and greater during fast than slow responses. PID applied to pairs of recording sites revealed that fast responses were associated with earlier and stronger unique information and a greater relative contribution of redundancy, whereas synergistic contributions were smaller. During fast responses, redundancy was selectively associated with stronger LFP power-envelope coupling. These findings indicate that faster perceptual decisions involve a frequency-specific reorganization of cortical information, characterized by early local encoding and enhanced redundant information across functionally interacting sites.

neuroscience↗

bioRxiv2024

MINT: a toolbox for the analysis of multivariate neural information coding and transmission

Information theory has deeply influenced the conceptualization of brain information processing and is a mainstream framework for analyzing how neural networks in the brain process information to generate behavior. Information theory tools have been initially conceived and used to study how information about sensory variables is encoded by the activity of small neural populations. However, recent multivariate information theoretic advances have enabled addressing how information is exchanged across areas and used to inform behavior. Moreover, its integration with dimensionality-reduction techniques has enabled addressing information encoding and communication by the activity of large neural populations or many brain areas, as recorded by multichannel activity measurements in functional imaging and electrophysiology. Here, we provide a Multivariate Information in Neuroscience Toolbox (MINT) that combines these new methods with statistical tools for robust estimation from limited-size empirical datasets. We demonstrate the capabilities of MINT by applying it to both simulated and real neural data recorded with electrophysiology or calcium imaging, but all MINT functions are equally applicable to other brain-activity measurement modalities. We highlight the synergistic opportunities that combining its methods afford for reverse engineering of specific information processing and flow between neural populations or areas, and for discovering how information processing functions emerge from interactions between neurons or areas. MINT works on Linux, Windows and macOS operating systems, is written in MATLAB (requires MATLAB version 2018b or newer) and depends on 5 native MATLAB toolboxes. The calculation of one possible way to compute information redundancy requires the installation and compilation of C files (made available by us also as pre-compiled files). MINT is freely available at https://github.com/panzerilab/MINT with DOI 10.5281/zenodo.13998526 and operates under a GNU GPLv3 license.

neuroscience↗

bioRxiv2024

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

neuroscience↗

bioRxiv2023

Sperm length evolution in relation to body mass is shaped by multiple trade-offs in tetrapods

Sperm size is highly variable across species and is influenced by various factors including fertilization mode, female reproductive traits and sperm competition. Despite considerable efforts, many questions about sperm size variation remain open. Variation in body size may affect sperm size evolution through its influence on these factors, but the extent to which sperm size variation is linked to body mass remains elusive. In this study, we use the general theory of Pareto Optimality to investigate the relationship between sperm size and body mass across tetrapods. We find that tetrapods fall within a triangular-shaped Pareto front in the trait space of body mass and sperm length suggesting that the evolution of sperm size in relation to body size is shaped by trade-offs. We then explore the three main factors predicted to influence sperm size evolution, namely sperm competition, clutch size and genome size. Our results demonstrate that body mass optimally shapes sperm size evolution in tetrapods mainly through its association with sperm competition and clutch size. Finally, we show that the triangular-shaped Pareto front is maintained when tested separately within mammals, birds, endothermic species and internal fertilizers, suggesting that similar evolutionary trade-offs characterize the evolution of sperm size in relation to body size within taxonomic/phylogenetic and functional subgroups of tetrapods. This study provides insights into the evolutionary mechanisms driving interspecific sperm size variation and highlights the importance of considering multiple trade-offs in optimizing reproductive traits.

evolutionary biology↗

bioRxiv2023

Measuring stimulus-related redundant and synergistic functional connectivity with single cell resolution in auditory cortex

Measures of functional connectivity have played a central role in advancing our understanding of how information is communicated within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites. However, recent research has highlighted the potential importance of also identifying synergistic connectivity--that is, connectivity that gives rise to information not contained in either site alone. Here, we measured redundant and synergistic functional connectivity with individual-neuron resolution in the primary auditory cortex of the mouse during a perceptual task. Specifically, we identified pairs of neurons that exhibited directed functional connectivity between them, as measured using Granger Causality. We then used Partial Information Decomposition to quantify the amount of redundant and synergystic information carried by these neurons about auditory stimuli. Our findings revealed that functionally connected pairs carry proportionally more redundancy and less synergy than unconnected pairs, suggesting that their functional connectivity is primarily redundant in nature. Furthermore, we observe that the proportion of redundancy is higher for correct than for incorrect behavioral choices, supporting the notion that redundant connectivity is beneficial for behavior.

neuroscience↗

bioRxiv2021

Sequential Transmission of Task-Relevant Information in Cortical Neuronal Networks

During auditory task performance, cortical processing of task-relevant information enables mammals to recognize sensory input and flexibly select behavioral responses. In mouse auditory cortex, small neuronal networks encode behavioral choice during a pure-tone detection task, but it is poorly understood how neuronal networks encode behavioral choice during a pure-tone discrimination task where tones have to be categorized into targets and non-targets. While the interactions between networked neurons are thought to encode behavioral choice, it remains unclear how patterns of neuronal network activity indicate the transmission of task-relevant information within the network. To this end, we trained mice to behaviorally discriminate target vs. non-target pure-tones while we used in vivo 2-photon imaging to record neuronal population activity in primary auditory cortex layer 2/3. We found that during task performance, a specialized subset of neurons transiently encoded intersection information, i.e., sensory information that was used to inform behavioral choice. Granger causality analysis showed that these neurons formed functional networks in which task-relevant information was transmitted sequentially between neurons. Differences in network structure between target and non-target sounds encoded behavioral choice. Correct behavioral choices were associated with shorter timescale communication between neurons. In summary, we find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both sensory input and behavioral choice.

neuroscience↗

bioRxiv2020

Recovery of neural dynamics criticality in personalized whole brain models of stroke

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.

neuroscience↗

bioRxiv2018

Archetypes in human behavior and their brain correlates: An evolutionary trade-off approach

Organisms perform multiple tasks and in doing so face critical trade-offs. According to Pareto optimality theory, such trade-offs lead to the evolution of phenotypes that are distributed in a portion of the trait-space resembling a polytope, whose vertices represent the specialists at one of the traits (archetypes).\n\nWe applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible 300 combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting task. This task requires choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward identified three archetypes, which accounted for variability on many cognitive, personality, and socio-economic status scores, differences in brain structure, as well as in functional connectivity between prefrontal cortex, basal ganglia, and amygdala, regions associated with reward and their regulation. There was only a weak association with genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

neuroscience↗