Responsive luminescent lanthanide probes for biological applications

Lanthanide-based complexes play a significant role in biological applications, spanning MRI contrast agents to molecular luminescent tags. Regardless of their application, these complexes should conform to general requirements, such as high stability against decomplexation at physiologically relevant conditions and sufficient solubility in water. Other more specific requirements may also apply, demanding a customised design of the ligand for a specific application. Emissive bio-responsive lanthanide complexes comprise a large and dynamically developing area, which possesses several intrinsic advantages over non-lanthanide analogues. Large Stokes’ shifts, long-lived excited states, ratiometric bands in the emission spectrum, strong cirularly-polarised signals are but a few to be named. These beneficial properties can be employed for efficient measurement of pH or determination of bioactive molecules both in vitro and in cellulo. For instance, europium complexes bearing sulphonamide arms showed reversible pH-response, producing noticeable changes in both the total emission and CPL spectra (Chapter 2). Other europium complexes possessing polarity-sensitive emission intensity were successfully used for detection of human serum albumin and α1-AGP – two the most abundant serum proteins – by following both total emission and CPL spectra, and these results are discussed in Chapter 3. Selective detection of biologically relevant anions needs specific probe design requirements. Even subtle changes in the structure of the ligand may lead to considerable changes in selectivity and affinity towards selected species. Such a correlation between structure and binding properties was exemplified in a series of europium complexes for the detection of nucleotides and zinc and led to the creation of probes spanning 5 orders of affinity constants. Furthermore, a nucleotide-specific induced CPL signal allowed monitoring the ratio between ADP and ATP – a parameter that characterises metabolic rates in mitochondria. These observations are thoroughly analysed in Chapter 4

A normative theory of social conflict

Social hierarchy in animal groups carries a crucial adaptive function by reducing conflict and injury while protecting valuable group resources. Social hierarchy is dynamic and can be altered by social conflict, agonistic interactions, and aggression. Understanding social conflict and aggressive behavior is of profound importance to our society and welfare. In this study, we developed a quantitative theory of social conflict. We modeled individual agonistic interactions as a normal-form game between two agents. We assumed that the agents use Bayesian inference to update their beliefs about their strength or their opponent's strength and to derive optimal actions. We compared the results of our model to behavioral and whole-brain neural activity data obtained for a large (n=116) population of mice engaged in agonistic interactions. We find that both types of data are consistent with the first-level Theory of Mind model (1-ToM) in which mice form both "primary" beliefs about their and their opponent's strengths as well as the "secondary" beliefs about the beliefs of their opponents. Our model helps identify brain regions that carry information about these levels of beliefs. Overall, we both propose a model to describe agonistic interactions and support our quantitative results with behavioral and neural activity data

Spatiotemporal 3D image registration for mesoscale studies of brain development

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we introduce CORGI (Customizable Object Registration for Groups of Images), an algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm's sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align ("synchronize") brain samples in time, accounting for individual development paces. We tested CORGI on 28 samples of whole-mounted perinatal mouse brains (P0-P9) and compared its accuracy with other registration algorithms. Our algorithm offers a runtime of several minutes per brain on a laptop and automates such brain registration tasks as mapping brain data to atlases, comparing experimental groups, and monitoring brain development dynamics

Neural Networks With Motivation.

Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural networks implementing Q-learning with motivational salience can navigate in environment with dynamic rewards without adjustments in synaptic strengths when the needs of an agent shift. In this setting, our networks may display elements of addictive behaviors. Second, we use a similar framework in hierarchical manager-agent system to implement a reinforcement learning algorithm with motivation that both infers motivational states and behaves. Finally, we show that, when trained in the Pavlovian conditioning setting, the responses of the neurons in our model resemble previously published neuronal recordings in the ventral pallidum, a basal ganglia structure involved in motivated behaviors. We conclude that motivation allows Q-learning networks to quickly adapt their behavior to conditions when expected reward is modulated by agent's dynamic needs. Our approach addresses the algorithmic rationale of motivation and makes a step toward better interpretability of behavioral data via inference of motivational dynamics in the brain

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)