Biology subjects

Pipa, G.

Publications and source records attributed to Pipa, G..

10 recordsLinked to original sources

bioRxiv2025

When Firing Rate Falls Short: Spike Synchrony to Efficiently Disentangle Stimulus Saliency and Familiarity

Whether neural computation relies on firing rate or spike timing has been debated for decades, with no definitive resolution. Here, using a recurrent spiking network simulation and realistically varying stimulus saliency, we demonstrate that the two mechanisms are complementary: rate reliably detects stimulus class, spike synchrony reliably detects stimulus familiarity. This division of labor is necessary: rate coding fails to distinguish novel high-saliency from familiar low-saliency stimuli, while synchrony succeeds. We validate this complementary coding across a biologically realistic V1 model and an abstract associative memory network, demonstrating robustness across connectivity regimes and memory loads. Crucially, when inputs are already temporally coordinated through familiarity in a previous network layer, spike synchrony alone can encode both stimulus identity and familiarity. Our findings reconcile the rate-timing debate: familiar stimuli exploit efficient synchrony-based codes reinforced by recurrent connectivity across the cortical hierarchy, while novel stimuli depend on rate codes.

neuroscience↗

bioRxiv2025

Beyond the first glance: How human presence enhances visual entropy and promotes spatial learning

Spatial learning emerges not only from static environmental cues but also from the social and semantic context embedded in our surroundings. This study investigates how human agents influence visual exploration and spatial knowledge acquisition in a controlled Virtual Reality (VR) environment, focusing on the role of contextual congruency. Participants freely explored a 1 km2 virtual city while their eye movements were recorded. Agents were visually identical across conditions but placed in locations that were either congruent, incongruent, or neutral with respect to the surrounding environment. Using Bayesian hierarchical modeling, we found that incongruent agents elicited longer fixations and significantly higher gaze transition entropy (GTE), a measure of scanning variability. Crucially, GTE emerged as the strongest predictor of spatial recall accuracy. These findings suggest that human-contextual incongruence promotes more flexible and distributed visual exploration, thereby enhancing spatial learning. By showing that human agents shape not only where we look but how we explore and encode space, this study contributes to a growing understanding of how social meaning guides attention and supports navigation. Author summaryWhen people explore a new environment, such as an unfamiliar city, they rely on what they see to understand and remember the space. Traditionally, research has focused on stable features like buildings or landmarks. However, real-world environments also include people, whose presence can shape how we explore and learn. In this study, participants explored a virtual city while their eye movements were tracked. Some human figures matched their surroundings, while others appeared out of place. We found that people looked longer at those unexpected figures and that their gaze patterns became more flexible and varied afterward. This broader visual exploration helped them remember the layout of the city more accurately. Our results suggest that human presence, especially when it disrupts expectations, can promote more effective learning by encouraging more complex visual engagement. These findings can provide insight into how strategically placed social cues enhance attention and memory, and more importantly, how they influence our patterns of visual exploration.

neuroscience↗

bioRxiv2025

Continual familiarity decoding from recurrent connections in spiking networks

Familiarity memory enables recognition of previously encountered inputs as familiar without recalling detailed stimuli information, which supports adaptive behavior across various timescales. We present a spiking neural network model with lateral connectivity shaped by unsupervised spike-timing-dependent plasticity (STDP) that encodes familiarity via local plasticity events. We show that familiarity can be decoded from network activity using both frequency (spike count) and temporal (spike synchrony) characteristics of spike trains. Temporal coding demonstrates enhanced performance under sparse input conditions, consistent with the principles of sparse coding observed in the brain. We also show how connectivity structure supports each decoding strategy, revealing different plasticity regimes. Our approach outperforms LSTM in temporal generalizability on the continual familiarity detection task, with input stimuli being naturally encoded in the recurrent connectivity without a separate training stage.

neuroscience↗

bioRxiv2024

The impact of human agents on spatial navigation and knowledge acquisition in a Virtual Environment

Concepts of spatial navigation rest on the idea of landmarks, which are immobile features or objects in the environment. However, behaviorally relevant objects or fellow humans are often mobile. This raises the question of how the presence of human agents influences spatial exploration and knowledge acquisition. Here, we investigate exploration and performance in subsequent spatial tasks within a virtual environment containing numerous human avatars. In the exploration phase, agents had a locally limited effect on navigation. They prompted participants to revisit locations with agents during their initial exploration without significantly altering overall exploration patterns or the extent of the area covered. However, agents and buildings competed for visual attention. When spatial recall was tested, pointing accuracy toward buildings improved when participants directed their attention to the buildings and nearby agents. In contrast, pointing accuracy for agents showed weaker performance and did not benefit from visual attention directed toward the adjacent building. Active agents and incongruent agent-environment pairings further enhanced pointing accuracy, revealing that violations of expectations by agents can significantly shape navigational knowledge acquisition. Overall, agents influenced spatial exploration by directing attention locally, with the interaction between agent salience and environmental features playing a key role in shaping navigational knowledge acquisition.

neuroscience↗

bioRxiv2023

Spike synchrony as a measure of Gestalt structure

The function of spike synchrony has long been debated in the neuroscience community. Some researchers view synchrony as a byproduct of brain activity, while others argue that it serves as a mechanism for binding perceptual features. We, however, argue for an alternative view on the computational role of synchrony: it can serve as a mechanism for estimating the prior probability of incoming stimuli. In V1, this probability can be estimated by comparing the input stimulus with previously acquired visual experience. Such experience is encoded in horizontal intracortical connections, as they are subject to change, learn, and adapt to incoming visual stimuli throughout the course of life. Thus, the V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on the synchrony of neuronal firings in V1. In our work, we used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found significant differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation. Author SummaryNeurons in the brain fire synchronously in different contexts, in response to different stimuli. However, the function of synchrony is very debatable in the neuroscience community. We suggest that synchrony plays an important role in brain computations: it can estimate how well a network is familiar with the incoming stimulus. We tested our hypothesis on the simulation of neuronal activity in a model of primary visual cortex - a brain region which is processing simple visual features, such as lines of different angle orientation. The connections between neurons in this brain area store the acquired visual experience in the form of its statistics, such as edge cooccurrences. We built the connectivity in our model in accordance with principles of connectivity in primary visual cortex, assuming that it reflects natural images statistics. We tested our model on artificial stimuli, which represented more and less experienced visual features. Our results showed that spike synchrony is a global phenomenon which emerges across the large parts of the network, and it reflects the familiarity of the network with the stimulus. Thus, it can serve as a form of recognition memory and has a potential for usage in various applications which require estimation of input familiarity.

neuroscience↗

bioRxiv2021

Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets. This is particularly challenging if there are systematic differences between them, as is often the case. Accounting for these differences requires a larger number of parameters and thus increases the risk of overfitting. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to explicitly incorporate information about the dataset structure and general domain knowledge. To this end, we look at a typical application in remote sensing: the estimation of leaf area index (of white winter wheat), an important indicator for agronomical modeling, from measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach lead us to recommend Bayesian hierarchical models as a versatile tool for environmental sciences and beyond, particularly in scenarios where the available data sources are heterogeneous.

ecology↗

bioRxiv2019

Event-based pattern detection in active dendrites

AO_SCPLOWBSTRACTC_SCPLOWThe brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brains own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.

neuroscience↗

bioRxiv2019

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.\n\nAuthor summaryWhy was this study done?\n\nO_LIStatistical modeling is invaluable to public-health policy as it helps understand and anticipate the dynamics of the spread of infectious diseases. The available training data is often limited and reported with a low spatial and temporal resolution. This poses a challenge and makes it particularly important to incorporate domain knowledge and prior assumptions to guide the modeling process.\nC_LIO_LIIn order to evaluate the trustworthiness and reliability of a models predictions, it is crucial to be able to interpret the model and quantify the model uncertainty.\nC_LIO_LITo address this, we develop an interpretable model that uses Bayesian inference (rather than commonly used maximum likelihood estimation) and provides a probability distribution over inferred parameters.\nC_LI\n\nWhat did the researchers do and find?\n\nO_LIWe develop and test a single probabilistic model that learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviral enteritis and Lyme borreliosis) at the county level one week ahead of time.\nC_LIO_LIWe employ a Bayesian Monte Carlo regression approach that provides an estimate of the full probability distribution over inferred parameters as well as model predictions.\nC_LIO_LIThe model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.\nC_LIO_LIThe predictive performance of our model compares favorably with a contemporary reference model for all diseases tested.\nC_LI\n\nWhat do these findings mean?\n\nO_LIInterpretable predictive models can be applied to surveillance data to gain insights into the dynamics of infectious diseases.\nC_LIO_LIProbabilistic modeling approaches provide a suitable framework for many challenges of working with epidemiological data.\nC_LI

epidemiology↗

bioRxiv2017

2D:4D and spatial abilities: From rats to humans

Variance in spatial abilities are thought to be determined by in utero levels of testosterone and oestrogen, measurable in adults by the length ratio of the 2nd and 4th digit (2D:4D). We confirmed the relationship between 2D:4D and spatial performance using rats in two different tasks (paired-associate task and watermaze) and replicated this in humans. We further clarified anatomical and functional brain correlates of the association between 2D:4D and spatial performance in humans.

neuroscience↗

bioRxiv2017

Encoding And Decoding Dynamic Sensory Signals With Recurrent Neural Networks: An Application Of Conceptors To Birdsongs

In a constantly changing environment the brain has to make sense of dynamic patterns of sensory input. These patterns can refer to stimuli with a complex and hierarchical structure which has to be inferred from the neural activity of sensory areas in the brain. Such areas were found to be locally recurrently structured as well as hierarchically organized within a given sensory domain. While there is a great body of work identifying neural representations of various sensory stimuli at different hierarchical levels, less is known about the nature of these representations. In this work, we propose a model that describes a way to encode and decode sensory stimuli based on the activity patterns of multiple, recurrently connected neural populations with different receptive fields. We demonstrate the ability of our model to learn and recognize complex, dynamic stimuli using birdsongs as exemplary data. These birdsongs can be described by a 2-level hierarchical structure, i.e. as sequences of syllables. Our model matches this hierarchy by learning single syllables on a first level and sequences of these syllables on a top level. Model performance on recognition tasks is investigated for an increasing number of syllables or songs to recognize and compared to state-of-the-art machine learning approaches. Finally, we discuss the implications of our model for the understanding of sensory pattern processing in the brain. We conclude that the employed encoding and decoding mechanisms might capture general computational principles of how the brain extracts relevant information from the activity of recurrently connected neural populations.

neuroscience↗