ESI SyNC 2023: September 14 - 15
Linking hypotheses: where neuroscience, computation, and cognition meet
We are happy to announce that the Ernst Strüngmann Institute Systems Neuroscience Conference (ESI SyNC) 2023 will take place in-person this year, on September 14 and 15 at the Ernst Strüngmann Institute in Frankfurt, Germany. A hybrid format will be implemented, such that online participation is also possible.
This year’s topic is: Linking hypotheses: where neuroscience, computation, and cognition meet. Eleven speakers will discuss their research and views on the intersection of systems, behavioral, and computational neuroscience from a wide array of viewpoints: from basic network interactions in the brain probed at the electrophysiological and mechanistic levels, to complex animal behavior and cognition. This year’s conference is an outstanding platform bringing together sub-fields in the neurosciences that find themselves, regrettably often, treated in isolation. Young researchers will have the opportunity to exchange ideas and raise questions in formal and informal discussions. There will also be a poster session for which we welcome submissions.
Many nervous systems develop and form functional circuits through a long period of development involving a myriad of mechanisms. Some of these are determined by genes and molecules, while others depend on neural activity patterns. I will present how these diverse mechanisms work together to set up neural circuits shortly after an animal is born, enabling it to gradually acquire its cognitive and behavioral capabilities. I will focus on the visual system, and demonstrate how neural circuits became established and capable of performing different computations. I will focus on some specific mechanisms such as inhibitory synaptic plasticity and the emergence of excitatory and inhibitory balance, and its role in the detection of novel stimuli.
Encoding and storing facts: A joint agenda for neuroscience, psychology and linguistics
How does the brain represent and store facts such as “Jenny painted her bedroom window yesterday” or “Children like sweets”? Across semantic theories, representation of the corresponding meanings is achieved by assuming a lexicon of predicates (like, paint) that take arguments (children, sweets, Jenny, …) as inputs to yield descriptions of events or states. Psychology and neuroscience provide a different type of answer, where the guiding principle is an association or its neural counterpart, a Hebbian synapse. Such an associative view, while bolstered by much neuroscientific evidence, lacks the means to encode different predicates and fails to provide a satisfactory answer as to how different relations between the same arguments can be maintained. In this talk we resolve the tension by emphasizing another type of neural machinery, most prominently demonstrated in (but not restricted to) the literature on rodent spatial navigation. We argue that cell types such as object cells, boundary cells, head-direction cells, etc. are predicates that enable expressing various abstract meanings, and that a (para-)hippocampal navigational system can be viewed as a model for a more complex system present in human cognition.
Memory research is in the midst of an “engram renaissance” (Josselyn, Kohler, & Frankland, 2017), as new tools make possible a more detailed and precise understanding of the neural mechanisms of memory than ever before. These findings have re-invigorated interest in multi-level, multi-field exploration of the capacity to remember. This revival also provides an opportunity to develop a richer conception of the engram — one that is required, I argue, to continue moving memory research forward. In this talk, I explore how a range of theoretical assumptions — about the nature of cognition, constraints on computation, and features of neural systems — influence how researchers think about the engram. I will attempt to show how making these assumptions explicit can yield not only more productive debates about this fundamental concept, but also lead to more fruitful lines of inquiry.
Model-based and model-free reinforcement learning mechanisms in brains and robot
The reinforcement learning (RL) theory constitutes a framework for an artificial agent to learn actions that maximize rewards in the environment. It has been successfully applied to Neuroscience to account for animal neural and behavioral processes in simple laboratory tasks, such as Pavlovian and instrumental conditioning, and single-step economic decision-making tasks. It moreover became very popular due to its account for dopamine reward prediction error signals. However, more complex multi-step tasks, such as navigation and social interaction tasks, illustrate their computational limitations. In parallel, researches in engineering, in robotics in particular, have emphasized the complementarity between different learning strategies when facing complex tasks, and explored solutions to combine these different strategies. One central distinction is between model-based and model-free reinforcement learning strategies: In the former case, an agent learns a statistical model of the effects of its actions in the environment, and then use this model to plan sequences of actions towards desired goals. In contrast, model-free strategies are relevant when the environment statistics are too noisy to learn a good internal model. In this case, RL agents can rather learn local action values and adapt reactively in each state of the environment. In this presentation, I will show a series of work where we used a coordination of model-based and model-free reinforcement learning to account for a diversity of behavioral and neural observations in humans, non-human primates and rodents in different paradigms: Navigation, instrumental and Pavlovian conditioning. I will moreover present recent robotics results where the same algorithm with the same parameters produces optimal performance in simple navigation and social interaction tasks, with a drastically reduced computational cost compared to classical methods. Finally, I will show how the patterns of mental simulation within such internal models can mimic experimentally observed reactivations of the rodent hippocampus in spatial cognition tasks, and raise new predictions for future experiments.
Bats are extreme aviators and amazing navigators. Many bat species nightly commute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has always been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have developed novel miniature sensors allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track bat pups over months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based navigation. Using miniature microphones placed on the bats, we have also inferred and studied their foraging success and social behaviour. This novel technology thus allows us to document and model foraging decision making in real-life large scale over long time periods.
With the internet, society has been exposed to puzzling and exquisite animal behavior, while neuroscience has vastly concentrated on a few inbred animal models studied in trained unnatural settings. Changing this trend is imperative. The onset of wireless recording technologies has allowed the development of more naturalistic inquiries into the brain and behavior of animals. By developing a novel behavioral paradigm in neuroscience, playing 'Hide and Seek' with rats, we were able to study mammalian play and its neural bases. We played 'Hide and Seek' with rats in a 30 square meter room, and found that they acquired the game easily and played by the rules. Rats played strategically and, without being conditioned, developed remarkable game specific vocalizations patterns. The freedom afforded to our subject animals allowed us to probe new forms of linking neural and behavioral phenomena. We reversed the arrow of inquiry by doing unsupervised clustering of neural population data allowing us to blindly refer back to the behavior of the rat during the game and uncover neural-behavioral associations unsuspected by the experimenters. My work in large scale wireless rat play laid the foundation for expanding neuroscience in two future directions, 1) the use of non-traditional rodent models (Deer mice) to understand the evolution of how play contributes to cognition and 2) taking novel neurotechnologies out of the lab to study the brains and behavior of animals in the wild.
I will depict the neural dynamics underlying music perception and speech comprehension, focusing on time scales and adaptive processes. First, I will present an account of why humans spontaneously dance to music. I will present behavioral and neuroimaging evidence that motor dynamics reflect predictive timing during music listening. While auditory regions track the rhythm of melodies, intrinsic neural dynamics at delta (1.4 Hz) and beta (20-30 Hz) rates in the dorsal auditory pathways encode the wanting-to-move experience (groove). Critically, neural dynamics are organized along this pathway in a spectral gradient, with the left sensorimotor cortex coordinating groove-related delta and beta activity. Combined with predictions of a neurodynamic model, this suggests that spontaneous motor engagement during music listening is a manifestation of predictive timing effected by interaction of neural dynamics along the dorsal auditory pathway. Second, to investigate speech comprehension, we developed a framework capitalizing on the concept of channel capacity. We behavioral examined the respective influence of seven acoustic and linguistic features on the comprehension of compressed speech. We show that comprehension is independently impacted by all these features, but at varying degrees and with a clear dominance of the syllabic rate. Complementing this framework, we integrate human intracranial recordings to study how neural dynamics in the auditory cortex adapt to different acoustic features, allowing for parallel sampling of speech at both syllabic and phonemic time scales. These findings underscore the dynamic adaptation of neural processes to temporal characteristics in speech and music, enhancing our understanding of language and music perception.
A role for beta oscillations in flexible ensemble formation
We recently proposed that the beta rhythm (15-30 Hz) provides a key aspect of routing of information through the brain, namely, the formation of flexible, transient neural ensembles. We tested this hypothesis using spike and LFP recordings in monkeys and MEG/EEG in healthy human participants performing a categorical decision-making task. We found that distinct beta-band frequencies are consistently associated with categorical decisions, with activity in these bands predicting the behavioral response. We characterized beta at these frequencies as transient bursts, and show cortical connectivity via distinct beta-frequency channels, suggestive of a multiplexing mechanism. These results further substantiate the idea that beta provides the scaffolding for the formation of neural ensembles, linking cells that encode currently relevant information, and show that such ensembles synchronize at different beta frequencies.
From synchronous to asynchronous: multiscale exploration of cortical state transitions
The cerebral cortex spontaneously elicits different types of activity that changes over time according to the brain state. Brain state transitions from synchronous to asynchronous, or from unconscious to conscious states, are accompanied by changes in parameters such as cortical complexity, functional connectivity, synchronization, and by a modulation of the excitatory-inhibitory balance. The phenomenological correlates of such transitions have been observed in the cerebral cortex at multiple scales, i.e., at microscale in cortical slices in vitro, at mesoscale in cortical areas in vivo and at macroscale at whole brain level. In this presentation, we will explore the distinct properties of various brain states, as well as the transitions between them, driven by both internal and external mechanisms. A comprehensive understanding of these brain state transitions is crucial in bridging the gap between experimental findings, theoretical research, and clinical applications.
On sleep, camouflage, and brain evolution
I will describe recent work on two systems and at different levels of investigation. In the first I will describe the unexpected dynamic complexity of sleep activity in the brain of a reptile. In the second, I will describe experiments on camouflage behavior in cuttlefish, an animal that exploits a unique skin display system controlled by the brain to match the texture statistics of visual scenes. Such studies, in animals that diverged over 320 and 550 MYA respectively from our own lineage, force one to reflect on the nature of common principles of brain operations.
All times are given in local, central European summer time (CEST) / GMT+2 / UCT+2.
Registration & Fees
Registration for in-person or online participation is now open: Register here. The fee for attending the conference in person is Euro 100. It can be paid via bank transfer using the bank details that will be provided after registration. The fee includes coffee, snacks, breakfast and lunch, and one dinner that will be provided at the conference venue. Please consider our privacy notice regarding events. There are no fees for online participation in the conference.
You can contact the organizing committee with any queries regarding the conference by sending an email to: esi-sync (at) esi-frankfurt.de or to the individual organizers.