STDP in Recurrent Neuronal Networks

Recent results about spike-timing-dependent plasticity (STDP) in recurrently connected neurons are reviewed, with a focus on the relationship between the weight dynamics and the emergence of network structure. In particular, the evolution of synaptic weights in the two cases of incoming connections for a single neuron and recurrent connections are compared and contrasted. A theoretical framework is used that is based upon Poisson neurons with a temporally inhomogeneous firing rate and the asymptotic distribution of weights generated by the learning dynamics. Different network configurations examined in recent studies are discussed and an overview of the current understanding of STDP in recurrently connected neuronal networks is presented

Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma

Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing

Entropy production of Multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to fMRI brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics

A comparative study between a power and a connectivity sEEG biomarker for seizure-onset zone identification in temporal lobe epilepsy

Background: Ictal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated. New method: This work aimed to compare and optimize the performance of a power and a connectivity-based biomarker to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score. Results: The analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the lower- and higher-gamma range for connectivity, both using a 20 or 30 s period after seizure onset. Conclusions: The results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients’ data for clinical applications.M.V., R.Z. and A.T.C. were funded by the European Regional Development Funds, Grant/Award Number: CECH/001-P-001682. A. T. C. and M.V. were funded by the Spanish Ministry of Science, Innovation, and Universities, and the State Investigation Agency (MCIN/AEI /10.13039/501100011033), Grant Award Number: PID2020-119072RAI00. G.D. was supported by the AGAUR research support grant (ref. 2021 SGR 00917) funded by the Department of Research and Universities of the Generalitat of Catalunya, the NODYN Project PID2022- 136216NB-I00 financed by the MCIN/AEI/10.13039/501100011033/ FEDER, UE., the Ministry of Science and Innovation, the State Research Agency and the European Regional Development Fund and the project NEurological MEchanismS of Injury, and Sleep-like cellular dynamics (NEMESIS) (ref. 101071900) funded by the EU ERC Synergy Horizon Europe. M.V. was supported by grant PTQ2022-012679, funded by Spanish Ministry of Science, Innovation, and Universities, and the State Investigation Agency (MCIN/AEI /10.13039/501100011033).Peer ReviewedPostprint (published version

Distinct modes of functional connectivity induced by movie-watching

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices.Peer ReviewedPostprint (author's final draft

Fundamental movement skills in grassroots soccer:A comparative study of coaches’ perceptions and practices in 9 European countries

Fundamental Movement Skills (FMS) are proven to be beneficial for development across sports domains, including soccer. Grassroots soccer provides a substantial platform to promote and develop FMS. However, coaches often have limited knowledge about FMS. Therefore, this study aimed to explore the perceptions and practices of FMS among grassroots soccer coaches across nine European countries and various coaching profiles. This study surveyed 1055 grassroots coaches from 9 countries based on prior studies to understand their perceptions and practices regarding FMS. Firstly, 14 questions were divided into three components with a Principal Component Analysis to enable clearer analysis: ‘Coaching Effectiveness,’ ‘Influencing Factors,’ and ‘Importance of FMS.’ The second phase involved comparing countries and coaching profiles to see how perceptions and practices varied by coaches’ expertise, experience, and the age group they coach. Kruskal-Wallis group comparisons revealed varied awareness and understanding of FMS among grassroots coaches in nine European countries (p &lt; 0.001). Post-hoc results showed that perceptions and practices were influenced more by coaching experience (p &lt; 0.01) and the age group coached (p &lt; 0.01) rather than qualifications. Coaches with over 10 years of experience and those working in the fundamental phase (U7-U12) recognized the benefits of FMS to a greater extent. While FMS awareness exists, deep understanding and practical implementation remain challenging. Differences between countries suggest a unified approach to FMS in coach education is missing. Strengthening FMS education will ensure that grassroots coaches are better equipped to develop young players, ultimately contributing to more effective long-term player development.</p

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks