Biology subjects

Wojcik, D. K.

Publications and source records attributed to Wojcik, D. K..

13 recordsLinked to original sources

bioRxiv2025

Individual and social contributions to learning of mice in Intellicages: the general framework and mean field theory

Reinforcement learning enables the adaptation of behaviors to changes in the environment. Models of reinforcement learning based on using previous experience to guide future choices enable balancing exploitation of opportunities and exploring the options and provide accurate predictions of animal behavior in simple instrumental tasks. However, they fail to account for the critical advantage we and other animals have, namely the ability not only to learn from our own choices but also from observing the outcomes of choices made by others. Here, we propose a new conceptual, analytical, and computational framework combining point processes with reinforcement learning models for the description, analysis and modeling of individual and group effects of mice learning reward placement. We show that marked point processes provide a natural language to describe behavior of group housed mice. We show how different reinforcement models of the behavior, including group effects, can be studied effectively in this framework, using example experiments. With this framework we show that the group effects (peer pressure) are twice stronger than individual learning.

animal behavior and cognition↗

bioRxiv2025

Modeling social influence as a reinforcer reveals varying individual learning strategies and the group's structure

In a social context, knowledge can be gained through observation and imitation, but in a numerous group, social influence can also be contradictory and confusing. The mechanisms of social learning in groups have not been fully characterized, partly due to the lack of adequate mathematical description. Using the known reinforcing property of social influence we introduce a reinforcement learning model which can account for social effects at the group and individual level. We use it to reveal pairwise influence relations and the overall learning strategies in a reversal learning task applied to a cohort of mice housed in an Intellicage. Animals make an efficient use of social influence when the goal of the group agrees with their own, but switch to an individual learning scheme when these goals are misaligned. They also exhibit varied decision rules depending on their own motivational state, and a selectivity in assimilating social information depending on the state of their conspecifics.

animal behavior and cognition↗

bioRxiv2024

Mapping Large-scale Spatiotemporal Dynamics of Synaptic Plasticity and LTP for Memory Encoding in the Hippocampal Network

Understanding memory formation requires elucidating the intricate dynamics of neuronal networks in the hippocampus, where information is encoded and processed through specific activity patterns and synaptic plasticity. Here, we introduce "EVOX," an advanced network electrophysiology platform equipped with high-density microelectrode arrays to capture critical network-level synaptic dynamics integral to learning and memory. This platform surpasses traditional methods by enabling label-free, high-order mapping of neural interactions, providing unprecedented insights into network Long-Term Potentiation (LTP) and evoked synaptic transmission within the hippocampal network. Utilizing EVOX, we demonstrate that high-frequency stimulation induces network-wide LTP, revealing enhanced synaptic efficacy in previously inactive cell assemblies in hippocampal layers. Our platform enables the real-time observation of network synaptic transmission, capturing the intricate patterns of connectivity and plasticity that underpin memory encoding. Advanced computational techniques further elucidate the mesoscale transmembrane generators and the dynamic processes that govern network-level memory encoding mechanisms. These findings uncover the complex dynamics that underlie learning and memory, showcasing EVOXs potential to explore synaptic and cellular phenomena in aging circuits. EVOX not only advances our understanding of hippocampal memory mechanisms but also serves as a powerful tool to investigate the broader scope of neural plasticity and network interactions in healthy and diseased states.

neuroscience↗

bioRxiv2023

Local contribution to the somatosensory evoked potentials in rat's thalamus

Local Field Potential (LFP), despite its name, often reflects remote activity. Depending on the orientation and synchrony of their sources, both oscillations and more complex waves may passively spread in brain tissue over long distances and be falsely interpreted as local activity at such distant recording site. Current Source Density method was proposed to recover locally active currents from multi-site LFP recordings. Here we use a model-based kernel CSD (kCSD) to study the contribution of local and distant currents to LFP recorded with dense multichannel probes from rat thalamic nuclei and barrel cortex, activated by whisker stimulation. We show that the evoked potential wave seen in the thalamic nuclei around 7-15 ms post-stimulus has a substantial negative component reaching from cortex. This component can be analytically removed and truly local thalamic LFP, with purely thalamic contributions, can be recovered reliably using kCSD. In particular, concurrent recordings from the cortex are not essential for reliable thalamic CSD estimation. Proposed framework can be used to analyse LFP from other brain areas and has consequences for general LFP interpretation and analysis. 1 BRIEF SUMMARYWhile recording LFP simultaneously in multiple structures, we often see significant correlations between the observed waves. A natural question is if they are propagated passively from one structure to another or if they are simultaneously generated by different, separated sets of sources. We argue this can be answered reliably using CSD analysis. We focus on the case of thalamic and cortical recordings in the somatosensory system in response to whisker stimulation where we observe significant correlations between early thalamic and cortical responses to whisker deflection.

neuroscience↗

bioRxiv2023

Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease

Parkinsons disease (PD) is characterized by three main motor symptoms: bradykinesia, rigidity and tremor. PD is also associated with diverse nonmotor symptoms that may develop in parallel or precede motor dysfunctions, ranging from autonomic system dysfunctions and impaired sensory perception to cognitive deficits and depression. Here, we examine the role of the progressive loss of dopaminergic transmission in behaviors related to the nonmotor symptoms of PD in a mouse model of the disease (the TIF-IADATCreERT2 strain). We found that in the period from 5 to 12 weeks after the induction of a gradual loss of dopaminergic neurons, mild motor symptoms became detectable, including changes in the distance between paws while standing as well as the step cadence and sequence. Male mutant mice showed no apparent changes in olfactory acuity, no anhedonia-like behaviors, and normal learning in an instrumental task; however, a pronounced increase in the number of operant responses performed was noted. Similarly, female mice with progressive dopaminergic neuron degeneration showed normal learning in the probabilistic reversal learning task and no loss of sweet-taste preference, but again, a robustly higher number of choices were performed in the task. In both males and females, the higher number of instrumental responses did not affect the accuracy or the fraction of rewarded responses. Taken together, these data reveal discrete, dopamine-dependent nonmotor symptoms that emerge in the early stages of dopaminergic neuron degeneration.

neuroscience↗

bioRxiv2021

Social learning about rewards: how information from others helps to adapt to changing environment

Being a part of a social structure is key for survival and reproduction. Living with conspecifics boosts evolutionary fitness, by providing essential information about the environment. Nonetheless, studying neural mechanisms of social learning has not yet been established under laboratory conditions. To examine how socially passed information about the reward affects the behavior of individuals we used Eco-HAB, an automated system for tracing voluntary behavior of group-housed mice living under semi-naturalistic conditions. We show that a scent of a rewarded individual has profound effects on the conspecifics ability to find the reward in both familiar and novel environments. Importantly, the animals display clear and stable individual differences in social behavior. As a result, socially conveyed information has different effects on individual mice. Further, we show that disrupting neuronal plasticity in the prelimbic cortex with nanoparticles gradually releasing TIMP metallopeptidase inhibitor 1, disrupts animals social behavior and results in decreased ability to adapt to environmental changes. The experimental paradigm we developed can be further used to study neuronal mechanisms of social learning.

neuroscience↗

bioRxiv2020

What we can and what we cannot see with extracellular multielectrodes

Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. Author SummaryEvery set of measurements is a window into reality rendering its incomplete or distorted picture. It is often difficult to relate the obtained representation of the world to underlying ground truth. Here we show, for brain electrophysiology, for arbitrary experimental setup (distribution of electrodes), and arbitrary analytical setup (function space of current source densities), that one can identify distributions of current sources which can be recovered precisely, and those which are invisible in the system. This shows what is and what is not observable in the studied system for a given setup, allows to improve the analysis results by modifying analytical setup, and facilitates interpretation of the measured sets of LFP, ECoG and EEG recordings.

neuroscience↗

bioRxiv2020

Histology-Based Average Template of the Marmoset Cortex With Probabilistic Localization of Cytoarchitectural Areas

The rapid adoption of marmosets in neuroscience has created a demand for three dimensional (3D) atlases of the brain of this species to facilitate data integration in a common reference space. We report on a new open access template of the marmoset cortex (the Nencki-Monash, or NM template), representing a morphological average of 20 brains of young adult individuals, obtained by 3D reconstructions generated from Nissl-stained serial sections. The method used to generate the template takes into account morphological features of the individual brains, as well as the borders of clearly defined cytoarchitectural areas. This has resulted in a resource which allows direct estimates of the most likely coordinates of each cortical area, as well as quantification of the margins of error involved in assigning voxels to areas, and preserves quantitative information about the laminar structure of the cortex. We provide spatial transformations between the NM and other available marmoset brain templates, thus enabling integration with magnetic resonance imaging (MRI) and tracer-based connectivity data. The NM template combines some of the main advantages of histology-based atlases (e.g. information about the cytoarchitectural structure) with features more commonly associated with MRI-based templates (isotropic nature of the dataset, and probabilistic analyses). The underlying workflow may be found useful in the future development of brain atlases that incorporate information about the variability of areas in species for which it may be impractical to ensure homogeneity of the sample in terms of age, sex and genetic background. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/036632v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11d61f6org.highwire.dtl.DTLVardef@a9156org.highwire.dtl.DTLVardef@1cbdc84org.highwire.dtl.DTLVardef@fa738c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA 3D template of the marmoset cortex representing the average of 20 individuals. C_LIO_LIThe template is based on Nissl stain and preserves information about cortical layers. C_LIO_LIProbabilistic mapping of areas, cortical thickness, and layer intensity profiles. C_LIO_LIIncludes spatial transformations to other marmoset brain atlases. C_LI AbbreviationsFor a list of areas and their abbreviations see Table S2.

neuroscience↗

bioRxiv2020

Structural attributes and principles of the neocortical connectome in the marmoset monkey

The marmoset monkey has become an important primate model in Neuroscience. Here we characterize salient statistical properties of inter-areal connections of the marmoset cerebral cortex, using data from retrograde tracer injections. We found that the connectivity weights are highly heterogeneous, spanning five orders of magnitude, and are log-normally distributed. The cortico-cortical network is dense, heterogeneous and has high specificity. The reciprocal connections are the most prominent and the probability of connection between two areas decays with their functional dissimilarity. The laminar dependence of connections defines a hierarchical network correlated with microstructural properties of each area. The marmoset connectome reveals parallel streams associated with different sensory systems. Finally, the connectome is spatially embedded with a characteristic length that obeys a power law as a function of brain volume across species. These findings provide a connectomic basis for investigations of multiple interacting areas in a complex large-scale cortical system underlying cognitive processes.

neuroscience↗

bioRxiv2019

kCSD-python, a tool for reliable Current Source Density estimation

Interpretation of the extracellular recordings can be difficult due to the long range of electric field but can be facilitated by estimating the density of current sources (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method, and introduce several new techniques to facilitate CSD analysis of experimental data and interpretation of the results. We investigate the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter notebook tutorial to facilitate uptake by the community.

neuroscience↗

bioRxiv2018

Unidirectional monosynaptic connections from auditory areas to the primary visual cortex in the marmoset monkey

Until the late 20th Century, it was believed that different sensory modalities were processed by largely independent pathways in the primate cortex, with cross-modal integration only occurring in specialized polysensory areas. This model was challenged by the finding that the peripheral representation of the primary visual cortex (V1) receives monosynaptic connections from areas of the auditory cortex in the macaque. However, auditory projections to V1 have not been reported in other primates. We investigated the existence of direct interconnections between V1 and auditory areas in the marmoset, a New World monkey. Labelled neurons in auditory cortex were observed following 4 out of 10 retrograde tracer injections involving V1. These projections to V1 originated in the caudal subdivisions of auditory cortex (primary auditory cortex, caudal belt and parabelt areas), and targeted parts of V1 that represent parafoveal and peripheral vision. Injections near the representation of the vertical meridian of the visual field labelled few or no cells in auditory cortex. We also placed 8 retrograde tracer injections involving core, belt and parabelt auditory areas, none of which revealed direct projections from V1. These results confirm the existence of a direct, nonreciprocal projection from auditory areas to V1 in a different primate species, which has evolved separately from the macaque for over 30 million years. The essential similarity of these observations between marmoset and macaque indicate that early-stage audiovisual integration is a shared characteristic of primate sensory processing.

neuroscience↗

bioRxiv2017

Analysis of behaviour in the Active Allothetic Place Avoidance task based on cluster analysis of the rat movement motifs

The Active Allothetic Place Avoidance test (AAPA) is a useful tool to study spatial memory in a dynamic world. In this task a rat, freely moving on a rotating circular arena, has to avoid a sector where shocks are presented. The standard analysis of memory performance in the AAPA task relies on evaluating individual performance measures. Here we present a new method of analysis for the AAPA test that focuses on the movement paths of the animals and utilizes a clustering algorithm to automatically extract the stereotypical types of behaviour as reflected in the recorded paths. We apply the method to experiments that study the effect of silver nanoparticles (AgNPs) on the reference memory and identify six major classes of movement motifs not previously described in AAPA tests. The method allows us to analyse the data with no prior expectations about the motion to be seen in the experiments.

neuroscience↗

bioRxiv2017

Four-Sphere Head Model For EEG Signals Revisited

Electric potential recorded at the scalp (EEG) is dominated by contributions from current dipoles set by active neurons in the cortex. Estimation of these currents, called inverse modeling, requires a forward model, which gives the potential when the positions, sizes, and directions of the current dipoles are known. Di[ff]erent models of varying complexity and realism are used in the field. An important analytical example is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. This model has been used extensively in the analysis of EEG recordings. Since it is analytical, it can also serve as a benchmark against which numerical schemes, such as the Finite Element Method (FEM), can be tested. While conceptually clear, the mathematical expression for the scalp potentials in the four-sphere model is quite cumbersome, and we observed the formulas presented in the literature to contain errors. We here derive and present the correct analytical formulas for future reference. They are compared with the results of FEM simulations of four-sphere model. We also provide scripts for computing EEG potentials in this model with the correct analytical formula and using FEM.

neuroscience↗