The Fries Lab at the Ernst Strüngmann Institute (ESI) in Frankfurt is looking for an enthusiastic post-doctoral fellow, who is interested in studying the functional roles of brain rhythms and their synchronization in awake behaving marmosets. Behavior and the underlying cognitive functions rely on the dynamic communication between brain areas, a central topic of the lab (e.g. Bastos et al., Neuron, 2015; Rohenkohl et al., Neuron, 2018; Vezoli et al., Neuron, 2021). The postdoc will study this using electrophysiological recordings, potentially combined with functional Ultrasound (fUSI) and/or optogenetic stimulation. The postdoc will be able to use an outstanding infrastructure, and will join an existing international team with expertise in non-human primate research, including marmosets (Jendritza et al., eNeuro, 2021; Jendritza et al., bioRxiv, 2021). For details on the institute and the lab, see: https://www.esi-frankfurt.de/people/pascalfries/ The position is for three years initially, with the possibility of extension, and can start at any time in 2022 or 2023. This call will remain open until 15 September 2022.

A postdoctoral position in computational neuroscience is available in the lab of Christopher Rozell at the Georgia Institute of Technology (Atlanta, GA). This BRAIN Initiative research project seeks to advance the field of closed-loop computational neuroscience by pioneering the use of real-time feedback stimulation during experiments to decouple recurrently connected circuit elements and make stronger causal inferences about circuit interactions. This position will have a broad opportunity to develop models and algorithms that are implemented in novel experiments using closed-loop optogenetic stimulation. We aim to provide both new scientific insight about computation in neural circuits (especially sensory coding in the thalamo-cortical circuit) as well as new approaches and algorithmic tools for the community to use in novel electrophysiology experiments. This position will work as part of a team and in close collaboration with the experimental lab of Garrett Stanley (also at Georgia Tech), and it is expected that the computational and algorithmic approaches will be implemented experimentally through close partnership with experimentalists in the Stanley Laboratory. Applicants should hold a PhD in a related discipline with a strong record of research impact, quantitative thinking and collaborative work. Experience in computational neuroscience, machine learning, feedback control, and causal inference is all advantageous. The lab is committed to providing a diverse and inclusive environment for all scholars, and applications are especially encouraged from all underrepresented groups. Additionally, the lab is committed to the professional development of the members, making it valuable preparation for people who are interested in academic, industrial or entrepreneurial careers. The position has no mandatory teaching or administrative duties. Excellent (written and oral) communication skills in English are required. This particular project is part of the Collaborative Research in Computational Neuroscience program (CRCNS), providing access to a community of researchers across the country who are focused on similar types of collaborations between computational and experimental labs. Georgia Tech's campus in the heart of midtown Atlanta, which has a thriving and collaborative neuroscience community that has a particular emphasis on computational and systems neuroscience. Atlanta is also one of the fastest-growing metropolitan areas in the United States, boasting a wide range of opportunities for recreation and culture. Georgia Tech has competitive benefits (including comprehensive medical insurance) and is an equal opportunity employer. The position would ideally start as soon as possible (spring 2021). The appointment is initially for 12 months with the expectation of renewal. Compensation will be commensurate with relevant experience. Candidates should send a CV, a statement of research experience and interests, expected date of availability, and the contact information for three references to crozell@gatech.edu with the subject line "CRCNS postdoc". Application review will proceed until the position is filled and should be received by December 1 for full consideration.

A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.

Animals exhibit remarkable learning capabilities with little direct supervision. Likewise, self-supervised learning is an emergent paradigm in artificial intelligence, closing the performance gap to supervised learning. In the context of biology, self-supervised learning corresponds to a setting where one sense or specific stimulus may serve as a supervisory signal for another. After learning, the latter can be used to predict the former. On the implementation level, it has been demonstrated that such predictive learning can occur at the single neuron level, in compartmentalized neurons that separate and associate information from different streams. We demonstrate the power such self-supervised learning over unsupervised (Hebb-like) learning rules, which depend heavily on stimulus statistics, in two examples: First, in the context of animal navigation where predictive learning can associate internal self-motion information always available to the animal with external visual landmark information, leading to accurate path-integration in the dark. We focus on the well-characterized fly head direction system and show that our setting learns a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Second, we show that incorporating global gating by reward prediction errors allows the same setting to learn conditioning at the neuronal level with mixed selectivity. At its core, conditioning entails associating a neural activity pattern induced by an unconditioned stimulus (US) with the pattern arising in response to a conditioned stimulus (CS). Solving the generic problem of pattern-to-pattern associations naturally leads to emergent cognitive phenomena like blocking, overshadowing, saliency effects, extinction, interstimulus interval effects etc. Surprisingly, we find that the same network offers a reductionist mechanism for causal inference by resolving the post hoc, ergo propter hoc fallacy.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Recent advances in optogenetics allow for stimulation of neurons with sub-millisecond spike jitter and single neuron selectivity. Already this precision has revealed new levels of cortical sensitivity: stimulating tens of neurons can yield changes in the mean firing rate of thousands of similarly tuned neurons. This extreme sensitivity suggests that cortical dynamics are near criticality. Criticality is often studied in neural systems as a non-equilibrium thermodynamic process in which scale-free patterns of activity, called avalanches, emerge between distinct states of spontaneous activity. While criticality is well studied, it is still unclear what these distinct states of spontaneous activity are and what responses we expect from stimulation of this activity. By answering these questions, optogenetic stimulation will become a new avenue for approaching criticality and understanding cortical dynamics. Here, for the first time, we study the effects of optogenetic-like stimulation on a model near criticality. We study a model of Inhibitory/Excitatory (I/E) Leaky Integrate and Fire (LIF) spiking neurons which display a region of high sensitivity as seen in experiments. We find that this region of sensitivity is, indeed, near criticality. We derive the Dynamic Mean Field Theory of this model and find that the distinct states of activity are asynchrony and synchrony. We use our theory to characterize response to various types and strengths of optogenetic stimulation. Our model and theory predict that asynchronous, near-critical dynamics can have two qualitatively different responses to stimulation: one characterized by high sensitivity, discrete event responses, and high trial-to-trial variability, and another characterized by low sensitivity, continuous responses with characteristic frequencies, and low trial-to-trial variability. While both response types may be considered near-critical in model space, networks which are closest to criticality show a hybrid of these response effects.

Ever since Bliss and Lømo discovered the phenomenon of long-term potentiation (LTP) in rabbit dentate gyrus in the 1960s, Hebb’s rule—neurons that fire together wire together—gained popularity to explain learning and memory. Accumulating evidence, however, suggests that neural activity is homeostatically regulated. Homeostatic mechanisms are mostly interpreted to stabilize network dynamics. However, recent theoretical work has shown that linking the activity of a neuron to its connectivity within the network provides a robust alternative implementation of Hebb’s rule, although entirely based on negative feedback. In this setting, both natural and artificial stimulation of neurons can robustly trigger network rewiring. We used computational models of plastic networks to simulate the complex temporal dynamics of network rewiring in response to external stimuli. In parallel, we performed optogenetic stimulation experiments in the mouse anterior cingulate cortex (ACC) and subsequently analyzed the temporal profile of morphological changes in the stimulated tissue. Our results suggest that the new theoretical framework combining neural activity homeostasis and structural plasticity provides a consistent explanation of our experimental observations.

The Miniscope Project -- an open-source collaborative effort—was created to accelerate innovation of miniature microscope technology and to increase global access to this technology. Currently, we are working on advancements ranging from optogenetic stimulation and wire-free operation to simultaneous optical and electrophysiological recording. Using these systems, we have uncovered mechanisms underlying temporal memory linking and investigated causes of cognitive deficits in temporal lobe epilepsy. Through innovation and optimization, this work aims to extend the reach of neuroscience research and create new avenues of scientific inquiry.

Team work makes sensory streams work: our senses work together, learn from each other, and stand in for one another, the result of which is perception and understanding. Learned associations between stimuli in different sensory modalities can shape the way we perceive these stimuli (Mcgurk and Macdonald, 1976). During audio-visual associative learning, auditory cortex is thought to underlie multi-modal plasticity in visual cortex (McIntosh et al., 1998; Mishra et al., 2007; Zangenehpour and Zatorre, 2010). However, it is not well understood how processing in visual cortex is altered by an auditory stimulus that is predictive of a visual stimulus and what the mechanisms are that mediate such experience-dependent, audio-visual associations in sensory cortex. Here we describe a neural mechanism by which an auditory input can shape visual representations of behaviorally relevant stimuli through direct interactions between auditory and visual cortices. We show that the association of an auditory stimulus with a visual stimulus in a behaviorally relevant context leads to an experience-dependent suppression of visual responses in primary visual cortex (V1). Auditory cortex axons carry a mixture of auditory and retinotopically-matched visual input to V1, and optogenetic stimulation of these axons selectively suppresses V1 neurons responsive to the associated visual stimulus after, but not before, learning. Our results suggest that cross-modal associations can be stored in long-range cortical connections and that with learning these cross-modal connections function to suppress the responses to predictable input.

Seizure onset is a critically important brain state transition that has proved very difficult to predict accurately from recordings of brain activity. I will present new data acquired using a range of optogenetic and imaging tools to characterize exactly how cortical networks change in the build-up to a seizure. I will show how intermittent optogenetic stimulation ("active probing") reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. This data relates back to old work from the 1980s suggesting a critical role in epileptic pathophysiology for dendritic plateau potentials. Our data show how the precipitous nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms.

Seizures can emerge from multiple or large foci in temporal lobe epilepsy (TLE), complicating focally targeted strategies such as surgical resection or the modulation of the activity of specific hippocampal neuronal populations through genetic or optogenetic techniques. Here, we evaluate a strategy in which optogenetic activation of medial septal GABAergic neurons (MSGNs), which provide extensive projections throughout the hippocampus, is used to control seizures. We found that MSGNs were structurally and functionally resilient in the chronic intrahippocampal kainate mouse model of TLE, which as is often the case in human TLE patients, presents with hippocampal sclerosis. Optogenetic stimulation of MSGNs modulated oscillations across the rostral to caudal extent of the hippocampus in epileptic conditions. Chronic wireless optogenetic stimulation of MSGNs, upon electrographic detection of spontaneous hippocampal seizures, resulted in reduced seizure durations. We propose MSGNs as a novel target for optogenetic control of seizures in TLE.

Understanding what drives foraging decisions in animals requires careful manipulation of the value of available options while monitoring animal choices. Value-based decision-making tasks, in combination with formal learning models, have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is even though fruit flies have served as a model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision-making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate a reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of value-based decisions.