Seminars

To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent.

In epilepsy research, a holy grail has been the identification and understanding of the "epileptogenic zone" - operationally defined as the (minimal) area or region of the brain is indispensible for the generation of epileptic seizures. The identification of the epileptogenic zone is particularly important for surgical treatments of focal epilepsy patients, but I will highlight some recent clinical, experimental and theoretical work showing that it is also fundamentally linked with our understanding of epilepsy and seizures. I will conclude with a proposal for an updated understanding of the epileptogenic zone and ictogenesis.

Neural activity in many sensory systems is organized on low-dimensional manifolds by means of convex receptive fields. Neural codes in these areas are constrained by this organization, as not every neural code is compatible with convex receptive fields. The same codes are also constrained by the structure of the underlying neural network. In my talk I will attempt to provide answers to the following natural questions: (i) How do recurrent circuits generate codes that are compatible with the convexity of receptive fields? (ii) How can we utilize the constraints imposed by the convex receptive field to understand the underlying stimulus space. To answer question (i), we describe the combinatorics of the steady states and fixed points of recurrent networks that satisfy the Dale’s law. It turns out the combinatorics of the fixed points are completely determined by two distinct conditions: (a) the connectivity graph of the network and (b) a spectral condition on the synaptic matrix. We give a characterization of exactly which features of connectivity determine the combinatorics of the fixed points. We also find that a generic recurrent network that satisfies Dale's law outputs convex combinatorial codes. To address question (ii), I will describe methods based on ideas from topology and geometry that take advantage of the convex receptive field properties to infer the dimension of (non-linear) neural representations. I will illustrate the first method by inferring basic features of the neural representations in the mouse olfactory bulb.

Temporal predictions are fundamental instruments for facilitating sensory selection, allowing humans to exploit regularities in the world. Recent evidence indicates that the motor system instantiates predictive timing mechanisms, helping to synchronize temporal fluctuations of attention with the timing of events in a task-relevant stream, thus facilitating sensory selection. Accordingly, in the auditory domain auditory-motor interactions are observed during perception of speech and music, two temporally structured sensory streams. I will present a behavioral and neurophysiological account for this theory and will detail the parameters governing the emergence of this auditory-motor coupling, through a set of behavioral and magnetoencephalography (MEG) experiments.

Information is encoded in fine-scale functional topographies that vary from brain to brain. Hyperalignment models information that is shared across brain in a high-dimensional common information space. Hyperalignment transformations project idiosyncratic individual topographies into the common model information space. These transformations contain topographic basis functions, affording estimates of how shared information in the common model space is instantiated in the idiosyncratic functional topographies of individual brains. This new model of the functional organization of cortex – as multiplexed, overlapping basis functions – captures the idiosyncratic conformations of both coarse-scale topographies, such as retinotopy and category-selectivity, and fine-scale topographies. Hyperalignment also makes it possible to investigate how information that is encoded in fine-scale topographies differs across brains. These individual differences in fine-grained cortical function were not accessible with previous methods.

Talk & Tutorial

Talk(1): Fetal and maternal NLRP3 signaling is required for preterm labor and birth. (DOI: 10.1172/jci.insight.158238) Talk(2): Maternal IL-33 critically regulates tissue remodeling and type 2 immune responses in the uterus during early pregnancy in mice (DOI: 10.1073/pnas.2123267119)

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701

We provide protocols for the social transfer of pain and analgesia in mice. We describe the steps to induce pain or analgesia (pain relief) in bystander mice with a 1-h social interaction with a partner injected with CFA (complete Freund’s adjuvant) or CFA and morphine, respectively. We detail behavioral tests to assess pain or analgesia in the untreated bystander mice. This protocol has been validated in mice and rats and can be used for investigating mechanisms of empathy. Highlights • A protocol for the rapid social transfer of pain in rodents • Detailed requirements for handling and housing conditions • Procedures for habituation, social interaction, and pain induction and assessment • Adaptable for social transfer of analgesia and may be used to study empathy in rodents https://doi.org/10.1016/j.xpro.2022.101756

Dominant theories of analogical thinking postulate that making an analogy consists in discovering that two superficially different situations share isomorphic systems of similar relations. According to this perspective, the comparison between the two situations may eventually lead to the construction of a schema, which retains the structural aspects they share and deletes their specific contents. We have developed a new approach to analogical thinking, whose purpose is to explain a particular type of analogies: those in which the analogs are exemplars of a schema-governed category (e.g., two instances of robbery). As compared to standard analogies, these comparisons are noteworthy in that a well-established schema (the schema-governed category) mediates each one of the subprocesses involved in analogical thinking. We argue that the category assignment approach is able to provide a better account of how the analogical subprocesses of retrieval, mapping, re-representation, evaluation and inference generation are carried out during the processing of this specific kind of analogies. The arguments presented are accompanied by brief descriptions of some of the studies that provided support for this approach.

The rules governing behavior often vary with behavioral contexts. As a result, an action rewarded in one context may be discouraged in another. Animals and humans are capable of switching between behavioral strategies under different contexts and acting adaptively according to the variable rules, a flexibility that is thought to be mediated by the prefrontal cortex (PFC). However, how the PFC orchestrates the context-dependent switch of strategies remains unclear. Here we show that pathway-specific projection neurons in the medial PFC (mPFC) differentially contribute to context-instructed strategy selection. In mice trained in a decision-making task in which a previously established rule and a newly learned rule are associated with distinct contexts, the activity of mPFC neurons projecting to the dorsomedial striatum (mPFC-DMS) encodes the contexts and further represents decision strategies conforming to the old and new rules. Moreover, mPFC-DMS neuron activity is required for the context-instructed strategy selection. In contrast, the activity of mPFC neurons projecting to the ventral midline thalamus (mPFC-VMT) does not discriminate between the contexts, and represents the old rule even if mice have adopted the new one. Furthermore, these neurons act to prevent the strategy switch under the new rule. Our results suggest that mPFC-DMS neurons promote flexible strategy selection guided by contexts, whereas mPFC-VMT neurons favor fixed strategy selection by preserving old rules.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

In vision, there is, surprisingly, very little evidence of common factors. Most studies have found only weak correlations between performance in different visual tests; meaning that, a participant performing better in one test is not more likely to perform also better in another test. Likewise in ageing, cross-sectional studies have repeatedly shown that older adults show deteriorated performance in most visual tests compared to young adults. However, within the older population, there is no evidence for a common factor underlying visual abilities. To investigate further the decline of visual abilities, we performed a longitudinal study with a battery of nine visual tasks three times, with two re-tests after about 4 and 7 years. Most visual abilities are rather stable across 7 years, but not visual acuity. I will discuss possible causes of these paradoxical outcomes.

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how qualitative aspects of a dendritic tree, such as its branched morphology, its repetition of presynaptic inputs, voltage-gated ion channels, electrical properties and complex synapses, determine neural computation beyond this apparent nonlinearity. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these qualitative biological constraints. Here we simulate multi-layer neural network models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by most of these constraints and may synergistically benefit from all of them combined. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks through the emergent capabilities afforded by their properties.