Multiple sclerosis (MS), a chronic, immune-mediated disease affecting the central nervous system (CNS), impacts an estimated 2.5 million people worldwide and nearly 1 million in the USA, primarily young adults with a female predilection. It is a major cause of neurological impairment, characterized by relapses that are driven by aberrant peripheral immune-cell activation and trafficking from the circulation into the CNS. Immune cells that then compartmentalize within the CNS are thought to contribute to subsequent progressive disease. Anti-CD20 (aCD20) therapy, that selectively depletes B cells, is highly effective at limiting relapsing MS, a benefit now understood to reflect removal of abnormally pro-inflammatory B cells that, when present, contribute to aberrant responses of T cells and myeloid cells involved in disease relapse. While now a mainstay treatment in MS, long-term continuous aCD20 treatment is associated with increasing risks. Interestingly, if B cells are allowed to return, both the B cells and non-B cells appear to no longer harbor the same abnormal profiles. The proposed randomized, placebo controlled-discontinuation trial of aCD20 in relapsing MS (AMS05) will test the hypothesis that at least some MS patients who discontinue aCD20 treatment will experience durable remission of relapsing disease, even after B cell reconstitution. Comprehensive mechanistic studies will define cellular mechanisms underlying, and potentially predictive of, durable remission and breakthrough disease. AMS05 will recruit patients with very early active MS, treat them with open-label anti-CD20 (ocrelizumab; OCR) for 24 months, then randomize them to placebo (Arm 1) or continued standard OCR treatment (Arm 2). Frequent brain MRIs will closely monitor for any disease activity and frequent biosampling will enable us to address: (1) Whether prolonged disease quiescence can be achieved after aCD20 discontinuation and B cell reconstitution? (2) What biological mechanisms underlie durable remission vs. disease breakthrough? (3) Can early biomarkers predict which patients will experience these outcomes? The study's two main Aims are: Aim 1: (a) Assess the rate of durable relapsing disease remission following 24 months of initial aCD20 treatment, using serial clinical assessments and high frequency acquisition of co-registered brain MRIs; (b) define biological profiles associated with durable disease remission or breakthrough disease, Aim 2: Evaluate predictors of durable treatment effects following OCR discontinuation by (a) assessing whether outcomes (durable remission or not; breakthrough disease) can be predicted by particular immune cell subset responses, in earlier samples; and (b) explore predictive modeling (accounting for age, sex, race, biological measures) and their interactions, that may better predict outcomes. The AMS05 clinical trial will explore the potential for aCD20 therapy to induce long-term remission in MS, potentially providing a breakthrough treatment paradigm, while also gaining novel insights into roles of B cells in autoimmunity and of cellular mechanisms of human immune tolerance.8. Proj

PROJECT SUMMARY A particularly aggressive and challenging kidney cancer subtype is translocation renal cell carcinoma (tRCC). tRCC predominantly affects children and adolescents and is incurable when metastatic. Importantly, tRCC is an orphan disease without specific therapies. tRCC is characterized by chromosomal translocations of the Microphtalmia (MiT) transcription factors, which are key regulators of lysosome biogenesis and autophagy. The most common translocated MiT gene is TFE3 and the most common partner is ASPSCR1. Our current understanding for how these chimeric fusion proteins promote tumorigenesis points to aberrant constitutive overexpression of transcriptionally active MiT transcription factors, which are characterized by a N’ transactivation domain (N-TAD), a basic helix-loop-helix (bHLH) domain implicated in DNA binding, and a leucine zipper dimerization domain. However, these studies are largely based on in vitro assays, and the contribution of TFE3 to tRCC tumorigenesis remains unknown. Addressing these questions has been hampered by a lack of tRCC animal models reproducing the human disease. To overcome this barrier, we have generated a mouse model based on the most common translocation (Prakasam et al., JCI 2024). In preliminary data we show that conditional ASPSCR1-TFE3 overexpression using a Sglt2-Cre driver leads to tRCC undistinguishable from human tRCC. Interestingly, when driven by Pax8-Cre, which we previously showed drives clear cell RCC (which is believed to arise from the same cells as tRCC), ASPSCR1-TFE3 induces cellular morphological changes typical of tRCC, but without increasing proliferation. Furthermore, constitutive ASPSCR1-TFE3 expression in the Pax8 lineage abolishes glomeruli, which are essential for kidney function, resulting in perinatal death. These data show that cell fate and proliferative functions of ASPSCR1-TFE3 are not necessarily linked and provide two experimental contexts in which to dissect ASPSCR1-TFE3 function. Here, we propose to leverage this innovative tRCC mouse model, the first animal model to faithfully recapitulate human tRCC, to investigate the molecular mechanism of tRCC tumorigenesis. Building upon preliminary data that implicates for the first time TFE3 DNA-binding for tumorigenesis, we propose to dissect the function of the N-TAD in tumorigenesis and cell fate programs. In addition, we will explore how autophagy, which is induced in tRCC, contributes to tRCC development. Finally, we will use the ASPSCR1-TFE3 model to develop new therapeutic opportunities. If successful, these studies leveraging an innovative mouse model will provide fundamental understanding about tRCC pathogenesis and present opportunities for therapeutic intervention addressing a significant unmet medical need.

SUMMARY Clostridioides difficile infection (CDI) is a leading cause of healthcare-associated diarrhea, with rising incidence in community settings and a growing burden of asymptomatic colonization. Asymptomatic car- riers, particularly among the elderly and individuals consuming high-sugar diets, represent a critical but underexplored reservoir for transmission and disease progression. This proposal introduces novel, anti- biotic-independent mouse models demonstrating that both dietary sugar and aging independently pro- mote asymptomatic C. difficile colonization. We hypothesize that these factors disrupt colonization re- sistance (CR) through distinct but overlapping microbial, metabolic, and immune pathways. In Aim 1, we will define how traditional and emerging dietary sugars alter the gut environment to permit C. difficile colonization using in vitro bioreactors and in vivo models. Aim 2 will identify age-associated changes in microbiota and mucosal immunity that impair CR, using longitudinal studies and fecal micro- biota transfer. Aim 3 will functionally validate C. difficile genes upregulated during asymptomatic carriage using CRISPR-Cas9 mutants in both sugar- and age-induced models. This integrative, multi-omics approach will uncover the mechanisms enabling asymptomatic colonization and identify microbial and host targets for intervention. The findings will inform microbiome-based strat- egies to prevent CDI in vulnerable populations and shift current paradigms in CDI risk assessment and prevention.

PROJECT SUMMARY T cell development must select a mature T cell repertoire that is tolerant to self pMHC yet responds vigorously to foreign peptides presented by the same MHC alleles. This balance is achieved through thymic checkpoints that enforce MHC restriction, eliminate or divert highly self-reactive clones, and fine-tune T cell receptor (TCR) signaling machinery in positively selected T cells based on their self-reactivity. TCR tuning – mediated in part by upregulation of the inhibitory co-receptors CD5 and CD6 - is essential to selectively suppress responses to low affinity peptides to optimize affinity discrimination. Yet the molecular mechanisms that link self-reactivity to these outputs are incompletely understood. Our published data has identified a kinetic proofreading module operated by the T cell signaling adaptor LAT in thymocytes that imposes a selective calcium signaling delay via slow phosphorylation of Y136, and thereby enables T cells to distinguish weak, brief pMHC contacts from high affinity pMHC interactions of long duration. Our preliminary studies suggest that this module links weak pMHC interactions exclusively to Nr4a1 induction, while only strong and long duration TCR stimuli are sufficient to induce expression of the completely NFAT-dependent family member Nr4a3. We recently showed that the transcriptional regulators Nr4a1 and Nr4a3 are redundantly required for Treg diversion and clonal deletion, and together orchestrate a broad antigen-responsive transcriptional program in thymocytes. However, their distinct expression pattern and sensitivity to pMHC quality suggest unique roles and lead us to hypothesize that kinetic proof-reading by a Lat–Nr4a signaling axis coordinates TCR tuning to refine self/non-self discrimination and to enforce central tolerance. We will test this by: Defining how self-reactivity is translated into TCR signal tuning through Lat–Nr4a pathways. Our data suggests a role for the Nr4as in scaling CD5/CD6 expression with self-reactivity. Here we will test how genetic manipulation of either self-reactivity or LAT kinetic proofreading in vivo impacts Nr4as and TCR tuning. We will manipulate Nr4a1 and Nr4a3 dosage to identify roles in TCR tuning under graded negative selection pressure. Determining how Lat–Nr4a signaling directs T cell fate decisions in the thymus. We will test the hypothesis that Nr4a1 and Nr4a3 - via the LAT proofreading module - translate TCR signal strength into distinct thymocyte fates, including deletion or diversion into regulatory lineages. We will exploit both polyclonal and retrogenic TCR systems, along with TCR repertoire analysis to do so. Finally, we will use genetic epistasis to test if Nr4a1 and/or Nr4a3 operate downstream of fast kinetic LAT mutant G135D. Defining the transcriptional and epigenetic programs directed by Nr4as during thymic selection. Using RNAseq, we will identify Nr4a- and NFAT-dependent tuning regulators. We will also identify Nr4a-bound regulatory elements to define how individual Nr4a factors cooperate with NFAT and other transcriptional partners at specific thymic checkpoints to shape the gene regulatory landscape.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that is largely unresponsive to chemotherapy and current immune checkpoint blockade drugs, highlighting a critical need for the development of innovative therapeutic strategies. This R01 proposal targets vasoactive intestinal peptide (VIP), an immunosuppressive neuropeptide overexpressed in PDAC, which signals through VIP receptors (VPAC) on cancer cells, T cells, and myeloid cells within the tumor microenvironment. Based on our recent success in developing selective and potent VPAC receptor antagonists, we hypothesize that blocking VPAC signaling will reverse immunosuppression in the PDAC TME by reducing immune checkpoint expression, enhancing chemokine-driven infiltration of cytotoxic T cells, and disrupting immunosuppressive interactions between T cells and myeloid cells, ultimately leading to durable anti-cancer immunity. We propose three specific aims to explore the immunosuppressive roles of VPAC signaling in PDAC. Aim 1 will identify the primary sources of VIP in PDAC tumors and characterize the effects of VPAC signaling on immune cell function and phenotype within the tumor microenvironment. Aim 2 will investigate how VPAC signaling influences immune cell migration into tumors by modulating chemokine receptors and directional signaling. Aim 3 will determine how VPAC signaling regulates interactions between T cells and immunosuppressive myeloid cells, particularly tumor-associated macrophages, and the resulting impact on anti-cancer immune responses and immunological memory. Our preliminary findings indicate that combined inhibition of VPAC signaling and PD-1 significantly enhances the regression of PDAC tumors in multiple mouse models, generating lasting protective immunity in cured mice without triggering autoimmune responses. We will use novel methods to pursue our aims, including inducible genetically engineered mouse models (GEMM) of PDAC, long-acting VPAC antagonists engineered with immunoglobulin Fc domains to improve their plasma half-life, and advanced microfluidics technologies to analyze immune cell movement within tumors. Animal experiments will be used to validate the translational potential of observations from in vitro organoids and microfluidic experiments. The GEMM and orthotopic mouse models of PDAC are necessary to provide critical insights into the 3-D structure of the TME and tumor regression in response to our novel immunotherapy. This research will be conducted by a multidisciplinary team with complementary expertise that will clarify the therapeutic potential of VPAC signaling inhibition in PDAC using sophisticated experimental tools and single-cell RNA sequencing. Ultimately, these findings could significantly improve the development of immunotherapeutic strategies for PDAC, potentially enhancing patient outcomes in pancreatic cancer and other malignancies expressing high VIP levels.

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains the leading infectious cause of death worldwide. Interruption of transmission is the most effective strategy to reduce incident infections, yet current approaches often fail to reach individuals for timely testing and treatment. This study addresses that gap by leveraging social networks to identify individuals at highest risk of transmitting TB, specifically, people who use drugs (PWUD). We will evaluate respondent-driven sampling (RDS), a peer7 based community recruitment strategy, to identify TB cases among PWUD and the household contacts (HHCs) of those with TB disease (RDS-TB) in Kampala, Uganda. Conducting this work in a high-prevalence setting such as Kampala where our team has established expertise allows us to overcome recruitment challenges common in settings in the United States while generating findings that are directly translatable. This is particularly relevant given that higher TB prevalence and larger outbreaks in the United States have been associated with the use of methamphetamine, heroin, and crack/cocaine, drugs that we will study. In Aim 1, we will compare the effectiveness and reach of RDS-TB with a traditional clinic-based index case HHC approach for TB case finding. We will screen 2,000 PWUD and their HHCs, estimate the number needed to screen to identify one case of TB disease, and compare the demographic and network characteristics of RDS-TB recruits with clinic-based HHCs. Whole genome sequencing will be used to characterize transmission dynamics. In Aim 2, we will compare the yield of individual and combined TB diagnostic strategies for community-based active case finding. Participants will undergo chest radiography with computer-aided detection, tongue swab testing for TB nucleic acid amplification tests (NAAT), and sputum testing for NAAT and mycobacterial culture. We will identify the minimal combination of tests needed to meet World Health Organization target product profile thresholds for screening. In Aim 3, we will define the conditions under which RDS-based screening can effectively interrupt TB transmission. We will develop an agent-based model informed by social network data from individuals with and without TB, incorporating drug use patterns and demographic characteristics. This project will generate a practical, scalable roadmap for social network–based TB active case finding in high28 risk communities. The approach will be readily adaptable to settings in the United States and will inform strategies to interrupt transmission and advance progress toward TB elimination, in alignment with the NIH Strategic Plan for TB Research.

Abstract Drawing upon the principles of social identity theory, existing literature, and our initial findings from family caregiver (FCG) online support groups (OSGs), our objective is to identify fundamental facilitator communication strategies that promote safe communication engage participants, and strengthen mechanisms of action (MOAs) within OSGs, ultimately enhancing health outcomes for hospice FCGs. Our pioneering initiative, Communication and Hospice Online with Optimal Support and Engagement (CHOOSE) is backed by compelling evidence highlighting the critical role of facilitator communication in reinforcing MOAs (a shared identity, social support, and social networks) in OSGs. Preliminary research underscores the transformative power of these MOAs in improving health outcomes for FCGs, yet current studies lack generalizability and statistical robustness. CHOOSE represents the first major, multisite, rigorously designed, and theoretically informed OSG intervention explicitly tailored for hospice FCGs of cancer patients. We aim to strengthen MOAs to enhance FCG well-being, reduce depression and anxiety, improve quality of life, and diminish loneliness. By advancing this critical research, we seek to provide a well-founded, evidence-based solution to the urgent needs of FCGs, making a significant impact on their health and well-being. We have outlined the following study aims: Aim 1. Determine the effect of the CHOOSE intervention on FCGs’ health outcomes compared to usual OSGs and usual hospice care. Aim 2. Examine direct and mediational relationships between CHOOSE participation, MOAs, and health outcomes. Aim 3. Explore the relationship between facilitator communication strategies and the FCG experience of the MOA to allow for future calibration of the intervention 1

One woman in seven will develop breast cancer in her lifetime increasing chances of occurrence with age. Breast cancer is thus the most frequently diagnosed cancer (excluding skin cancer) accounting for 29% of all cancers diagnosed each year. More importantly, breast cancer remains the second most lethal in women with 20% of new invasive breast cancer patients dying from the disease each year. In the case of malignant tumors, treatment depends on the stage of disease progression. In non-metastatic breast tumors, surgical intervention followed by radiation therapy is typically recommended. In locally advanced breast cancer, patients are treated with neo-adjuvant chemotherapy (NACT) re-surgically. Successful NACT and FUS thermal ablation may lead to softening and stiffening of the breast tumor, respectively. If stiffness changes are monitored, treatment response can be reliably and timely detected. To this end, we have developed two noninvasive, time- and cost-efficient theranostic methodologies for characterization and treatment of female breast cancer: 1) NACT monitoring of locally advanced malignant tumors with Harmonic Motion Imaging (HMI) and 2) FUS treatment of benign and non-metastatic tumors with HMI-guided Focused Ultrasound (HMIgFUS). HMI employs the FUS beam for vibration and mapping of tumors based on their distinct relative stiffness compared to the non-cancerous surrounding tissue. The use of animals is justified in order to be able to determine the effect of ablation and chemotherapy with both the safety and efficacy of the technique before clinical translation. HMIgFUS simultaneously monitors and induces thermal ablation without treatment interruption. In both methodologies, the same ultrasound beam simultaneously mechanically vibrates (with amplitude modulation (AM) at low power for diagnostic/localization purposes) and/or ablates (at high power for treatment purposes) the detected tumor. The team assembled encompasses expertise in ultrasound engineering, imaging, surgery, oncology, mouse tumor models and histopathological analysis. The techniques proposed herein stand to timely inform the current but also new therapies for breast cancer and ultimately lead to outpatient and efficient procedures for women with early-stage breast cancer.

PROJECT SUMMARY As oncological treatments improve, people with advanced cancer are living longer but are increasingly burdened by physical and psychological symptoms. Anxiety affects 1 in 2 people with advanced cancer and is linked to worse health outcomes and poor quality of life. Despite existing treatments, anxiety is under-treated in 40% of this population and is one of the highest unmet needs. Music therapy (MT) is a non-pharmacological intervention in which therapists guide patients through music experiences, ranging from receptive (music listening) to active (songwriting). Most MT studies for anxiety used receptive techniques, which produce short- term anxiety reduction, but not long-term reduction. Research in non-cancer populations suggests collaborative songwriting can foster social connection and provide outlets for processing difficult experiences, both of which are critical for long-term anxiety reduction. However, studies had key methodological limitations that make it impossible to determine the specific efficacy of collaborative songwriting. While songwriting has historically required extensive time, effort, equipment, and expertise, the growth of song creation platforms, powered by artificial intelligence (AI), has democratized songwriting. Despite their widespread availability, these AI-powered platforms have not been integrated into MT interventions or rigorously evaluated in clinical trials. We conducted a pilot RCT in which N=30 were randomized to 8 weekly Zoom sessions of an AI-assisted collaborative songwriting (ARTIST) intervention or a therapist attention-music listening (TAME) control. ARTIST participants reported clinically meaningful, durable anxiety reduction at week 16, whereas TAME did not. Moreover, in AI- powered facial expression analyses, ARTIST participants demonstrated significantly higher engagement, an objective marker of emotional expressivity, compared to TAME. Our work challenges the conventional MT paradigm of using receptive approaches to treat anxiety and suggests collaborative songwriting may produce greater long-term improvement by providing a safe outlet for expressing emotions and processing cancer- related experiences. However, its specific efficacy needs to be confirmed in a rigorous trial. We bring together a multidisciplinary team to propose the Virtual AI-Assisted Music Therapy for Anxiety Symptoms in People with Advanced Cancer (VIRTUOSO) study. We will conduct an RCT randomizing N=240 equally to the ARTIST intervention or a TAME control. Interventions will be delivered via Zoom over 8 weeks. Anxiety and social- cognitive processes will be assessed for up to 26 weeks. We will use AI-powered facial expression analyses as objective indices of emotional engagement. The specific aims are: 1) to determine the long-term efficacy of ARTIST on anxiety, 2) to assess the social-cognitive mechanisms of ARTIST, and 3) to explore associations between AI-powered facial expression analyses and anxiety outcomes in the context of MT. The VIRTUOSO study has the potential to expand treatment options for anxiety, deepen mechanistic understanding of MT, and integrate AI technologies into MT practice and research to benefit millions of people impacted by cancer.

Project Summary Systemic fungal infections, particularly fungal meningitis caused by Cryptococcus species, are a major cause of death for CD4+ T cell-deficient patients. These infections are difficult to treat due to patients’ immunocompromised state and the high frequency in which the disseminated fungal meningitis infection, which is the driver of mortality, is the presenting illness. Developing therapies to block dissemination and treat the disseminated infection is important for improving outcomes and preventing these severe cases of fungal meningitis. Cryptococcus species (spp.) are environmental organisms that are cultured from soil and pigeon guano. In order to establish infection and disseminate within the mammalian host, C. neoformans must adjust to and survive first in soil and/or pigeon guano, the mammalian lung and bloodstream, and then enter and proliferate in the brain. C. neoformans forms a highly heterogenous population at its initial site of infection in the lungs, varying in total (cell + capsule) diameter from 1 to 50 µm or more. We recently identified a Cryptococcus neoformans subpopulation, or morphotype, that forms in the middle of disease progression and is important for dissemination. This cell type – named “seed” cells because of their enhanced ability to enter extrapulmonary organs compared to large fungal cells – exhibits enhanced mannose exposure on its cell surface to facilitate organ entry. We also developed an in vitro system for inducing seed cells, which results in seed cells with the same phenotypes as seed cells extracted directly from mouse lungs termed “ex vivo”). Seed cell induction involves exposure to any one of a variety of phosphate sources at concentrations found within the infected mammalian host and acts through the transcription factor PHO4 at alkaline but not acidic pHs. The goal of this work will be to 1) elucidate the molecular mechanism underlying seed cell formation and identify drivers and 2) determine the mechanics of seed cell production and identify small molecule blockers. At the end of the proposed work period, we will have identified drug targets to prevent seed cell formation and candidate molecules for further development into seed cell blocking drugs. These could serve as antifungal treatments or prophylactic treatment for patients at risk of cryptococcal meningitis.

Europe’s leading neuroscience conference, bringing together researchers, clinicians, and innovators across molecular, cellular, systems, cognitive, and clinical neuroscience.

Arterial spin labeling (ASL) MRI has become a vital tool in clinical neuroimaging, enabling noninvasive assessment of cerebral perfusion across a range of conditions including stroke, vascular malformations, and brain tumors. With broader clinical adoption, its practical strengths — as well as important limitations — have become increasingly clear.

Although stress can be considered as an ongoing process that helps an organism to cope with present and future challenges, when it is too intense or uncontrollable, it can lead to adverse consequences for physical and mental health. Social stress specifically, is a highly prevalent traumatic experience, present in multiple contexts, such as war, bullying and interpersonal violence, and it has been linked with increased risk for major depression and anxiety disorders. Nevertheless, not all individuals exposed to strong stressful events develop psychopathology, with the mechanisms of resilience and vulnerability being still under investigation. During this talk, I will identify key gaps in our knowledge about stress vulnerability and I will present our recent data from our contextual fear learning protocol based on social defeat stress in mice.

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher processing stages. Instead, it suppresses the most predictable spikes and transmits a compressed representation of the input. Using only biologically plausible spike-timing based learning rules, PCL reproduces a wealth of findings on information processing in visual cortex and permits strong performance in downstream classification tasks. Overall, PCL offers a new approach to predictive coding and its implementation in natural and artificial spiking neural networks

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand movements when in contact with a surface, we predicted poorer performance when the participants traced with their bare finger compared to when their tactile sensation was dampened by a smooth, rigid finger splint. The results supported this prediction. EEG source analyses revealed smaller current in the source-localized somatosensory cortex during sensory conflict when the finger directly touched the surface. This finding supports the hypothesis that, in response to mirror-reversed visual feedback, the central nervous system selectively gated task-irrelevant somatosensory inputs, thereby mitigating, though not entirely resolving, the visuo-somatosensory conflict. Together, our results emphasize touch’s involvement in movement control over a surface, challenging the notion that vision predominantly governs goal-directed hand or finger movements.

Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

The Nature versus Nurture debate has generally been considered from the lens of genome versus experience dichotomy and has dominated our thinking about behavioral individuality and personality traits. In contrast, the role of nonheritable noise during brain development in behavioral variation is understudied. Using the Drosophila melanogaster visual system, I will discuss our efforts to dissect how individuality in circuit wiring emerges during development, and how that helps generate individual behavioral variation.

Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

The COSYNE 2025 conference was held in Montreal with post-conference workshops in Mont-Tremblant, continuing to provide a premier forum for computational and systems neuroscience. Attendees exchanged cutting-edge research in a single-track main meeting and in-depth specialized workshops, reflecting Cosyne’s mission to understand how neural systems function.

Each year the Bernstein Network invites the international computational neuroscience community to the annual Bernstein Conference for intensive scientific exchange. Bernstein Conference 2024, held in Frankfurt am Main, featured discussions, keynote lectures, and poster sessions, and has established itself as one of the most renowned conferences worldwide in this field.

Organised by FENS in partnership with the Austrian Neuroscience Association and the Hungarian Neuroscience Society, the FENS Forum 2024 will take place on 25–29 June 2024 in Vienna, Austria. The FENS Forum is Europe’s largest neuroscience congress, covering all areas of neuroscience from basic to translational research.

While sleeping, many vertebrate groups alternate between at least two sleep stages: rapid eye movement (REM) and slow wave sleep (SWS), in part characterized by wake-like and synchronous brain activity respectively. Sleep stage alternation has been implicated in learning and memory function experimentally1, and has motivated several techniques in training artificial neural networks2. If the functions ascribed to 2-stage sleep are truly general, one might expect to find similar phenomena outside the vertebrate lineage. Here we delineate neural and behavioral correlates of 2-stage sleep in octopuses, marine invertebrates which evolutionarily diverged from vertebrates ~550 MYA and have independently evolved large brains and behavioral sophistication. Octopus sleep is rhythmically interrupted by ~60 second bouts of pronounced body movements and rapid changes in their neurally controlled skin patterns. We show that this constitutes a distinct ‘active’ sleep stage, being homeostatically regulated, rapidly reversible, and coming with increased arousal threshold. Neuropixels recordings from the octopus central brain reveal that local field potential (LFP) activity during active sleep resembles that of waking. LFP activity differs across brain regions, with the strongest activity during active sleep seen in the Superior Frontal and Vertical lobes, anatomically connected regions associated with learning and memory function. During ‘quiet’ sleep, these regions are relatively silent but generate LFP oscillations resembling mammalian sleep spindles in frequency and duration. Computational analysis reveals the rich skin pattern dynamics of active sleep, which move through states strongly resembling waking skin patterns. The range of similarities with vertebrates implies that aspects of 2-stage sleep in octopuses may represent convergent features of complex cognition.

Stimuli trigger a pattern of activity across neurons in cortex, whose firing rates define a stimulus's representation in a high-dimensional vector space. Learning a visuomotor task can affect the responses of visual cortical neurons, but how and why training modifies population-level representations is unclear. One hypothesis is that representational plasticity in visual cortex facilitates visuomotor associations by downstream motor systems. Learning systems exhibit "inductive biases", meaning they form some stimulus-motor associations more easily than others. An animal's inductive biases presumably reflect its neuronal representations; its ability to form distinct motor associations for different stimuli depends on the representational similarity of the stimuli. Thus, the plasticity of sensory cortical representations may change inductive bias: for an animal to make different associations to two stimuli, the cortical representations of the stimuli must differentiate, such as if the evoked firing vectors were orthogonalized. A second hypothesis is that task training increases the fidelity of stimulus coding in sensory cortex, which improves decoding accuracy by downstream regions. However, this hypothesis presupposes that the population code in naive cortex suffers from low fidelity, which recent recordings of large cortical populations have questioned. We used two-photon calcium imaging to study how the tuning of V1 populations changes after mice learn to associate opposing actions with differently oriented gratings. Training did not improve the fidelity of stimulus coding, as it was already perfect in naive animals thanks to a subpopulation of highly reliable neurons. Instead, training caused the population's responses to motor-associated stimuli to become more orthogonal. The basis of this training-evoked orthogonalization was the sparsening of stimulus representations, an effect which could be summarized by a simple nonlinear transformation of naive neuronal firing rates and whose convexity was largest for motor-associated stimuli.

Distinguishing near and far visual cues is an essential computation that animals must carry out to guide behavior using vision. When animals move, self-motion creates motion parallax — an important but poorly understood source of depth information — whereby the speed of optic flow generated by self-motion depends on the depth of visual cues. This enables animals to estimate depth by comparing visual motion and self-motion speeds. As neurons in the mouse primary visual cortex (V1) are broadly modulated by locomotion, we hypothesized that they may integrate visual- and locomotion-related signals to estimate depth from motion parallax. To test this hypothesis, we designed a virtual reality (VR) environment for mice, where visual cues were presented at different virtual distances from the mouse and motion parallax was the only cue for depth, and recorded neuronal activity in V1 using two-photon calcium imaging. We found that the majority of excitatory neurons in layer 2/3 of V1 were selective for virtual depth. Neurons with different depth preferences were spatially intermingled, with nearby cells often tuned for disparate depths. Moreover, depth tuning could not be fully accounted for by either running speed or optic flow speed tuning in isolation, but arose from the integration of both signals. Specifically, depth selectivity of V1 neurons was explained by the ratio of preferred running and optic flow speeds. Finally, many neurons responded selectively to visual stimuli presented at a specific retinotopic location and virtual depth, demonstrating that during active locomotion V1 neuronal responses can be characterized by three-dimensional receptive fields. These results challenge the traditional view of V1 as a feed-forward filter bank, and suggest that the widespread modulation of V1 neurons by locomotion and other movements plays an essential role in estimation of depth from motion parallax.

Birdsong is an important model for vocal learning and sequential motor behavior. Similarly to human language, songs, notably those of Bengalese finches and canaries, exhibit higher-order sequence structure, meaning that the statistics of one syllable may depend on a number of previous syllables. However, this number (the context depth) varies in a manner that has challenged previous formal approaches. Here we used a hierarchical non-parametric Bayesian sequence model (based on Teh, 2006; Elteto et al., 2022) that seamlessly combines predictive information from shorter and longer contexts of previous syllables, weighing them proportionally to their predictive power. We fit our model to songs of 8 different Bengalese finches, each with > 300 song bouts (Veit et al., 2021). The model inferred the context depth, showing that it varied substantially, with some syllables depending just on one deterministic predecessor, but others depending on $>10$ previous syllables. Underlying this variability was syllables forming alternating and repeating chunks, i.e. strings of fixed subsequences. When fitted at the chunk-level, our model revealed different chunk-motifs that characterize how bouts typically start, unfold, and end. The model was also able to predict the flexibility with which birds can learn to switch between syllable transitions based on external cues.

When foraging in dynamic and uncertain environments, animals can benefit from basing their decisions on smart inferences about hidden properties of the world. Typical theoretical approaches for understanding the strategies that animals use in such settings combine Bayesian inference and value iteration to derive optimal behavioral policies that maximize total reward given changing beliefs about the environment. However, specifying these beliefs requires infinite numerical precision; with limited resources, this problem can no longer be decomposed into the separate steps of optimizing inference and optimizing action selection. To understand the space of behavioral policies in this constrained setting, we enumerate and evaluate all possible behavioral programs that can be constructed from just a handful of states. We show that only a small fraction of the top-performing programs can be constructed by approximating Bayesian inference; the remaining programs are structurally or even functionally distinct from Bayesian. To assess structural and functional relationships among all programs, we developed novel tree-embedding algorithms; these embeddings, which are capable of extracting different relational structures within the program space, reveal that nearly all good programs are closely connected through single algorithmic “mutations”. We demonstrate how one can use such relational structures to efficiently search for good solutions via an evolutionary algorithm. Moreover, these embeddings reveal that the diversity of non-Bayesian behaviors originates from a handful of key mutations that broaden the functional repertoire within the space of good programs. The fact that this diversity of non-optimal behavior does not significantly compromise performance suggests that these same strategies might generalize across tasks.

Parkinson's disease (PD), characterized by the absence of dopamine in the striatum[1], is caused by the death of the substantia nigra pars compacta dopamine (SNcDA) neurons in the mid-brain. The cause of this cell loss is attributed to irreparable damage due to a dysregulation cascade originating from excess cytosolic dopamine[2]. However, it is unresolved if dopamine dysregulation in SNcDA neurons themselves is the cause of PD or if it is a mere symptom. Here, we introduce a theory of specialized non-causal action potentials that serve metabolic homeostasis called `metabolic spikes' which can account for spontaneous activity observed in many neuron types including SNcDA. We propose that loss of these metabolic spikes in SNcDA can account for both, the cause of PD and the subsequent dopamine dysregulation. Neurons, presumably in anticipation of synaptic inputs, keep their ATP levels at a maximum such that they are ATP-surplus/ADP-scarce during synaptic quiescence. With ADP availability as the rate-limiting step, ATP production stalls in their mitochondria when energy consumption is low, leading to the formation of toxic Reactive Oxygen Species(ROS). Under these circumstances, `metabolic spikes’ serve to restore ATP production and relieve ROS toxicity. In a metabolism-coupled model of SNcDA that senses ROS and initiates spikes, we identified three categories of deficits that could decrease metabolic spikes and consequently deplete the dopamine tone seen in PD. Importantly in PD, such lowered extracellular dopamine level is misread by D2-autoreceptors and dopamine synthesis is increased. With dopamine vesicles being already full, excess dopamine produces disruptive aldehyde (DOPAL) leading to dysregulation and ultimately cell death. Metabolic spikes, though relevant for cellular health, may thus be an integrated neuronal mechanism that operates in synergy with synaptic integration and forms a basic principle of network dynamics and behaviour, as exemplified in PD.

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry of spike-coding networks, we consider a population of spiking neurons with low-rank connectivity, allowing each neuron’s threshold to be cast as a boundary in a space of population modes, or latent variables. Each neuron’s boundary divides this latent space into subthreshold and suprathreshold areas, which determines its contribution to the input-output function of the network. Then, incorporating Dale’s law as a connectivity constraint, we demonstrate how a network of inhibitory (I) neurons forms a convex, stable boundary in the latent coding space, and a network of excitatory (E) neurons forms a concave, unstable boundary. Finally, we show how the combination of the two yields stable dynamics at the crossing of the E and I boundaries. The resultant E/I networks are balanced, inhibition-stabilized, and exhibit asynchronous irregular activity, thereby closely resembling cortical dynamics. Moreover, the latent variables can be mapped onto a constrained optimization problem, and are capable of universal function approximation. The combination of these dynamical and functional properties leads to unique insights, including specified computational roles for E/I balance and Dale’s law. Finally, the intuitive geometry of the representations, plus the link to constrained optimization, makes our framework a promising candidate for scalable and interpretable computation in biologically-plausible spiking networks.

Understanding the basis of shared, across trial fluctuations in neural activity in mammalian cortex is critical to uncovering the nature of information processing in the brain. This correlated variability has often been related to the structure of cortical connectivity since variability not accounted for by signal changes likely arises from local circuit inputs. However, recent recordings from segregated networks of excitatory neurons in mouse primary visual cortex (V1) complicate this relationship. These results found that despite weak cross-network connection probability, noise correlations were significantly larger than one would expect. We aim to explore possible circuit mechanisms responsible for these enhanced positive correlations through biologically motivated cortical network models, with the hypothesis that they arise from unobserved inhibitory neurons. In particular, we consider networks with weakly interconnected excitatory populations, but either global or subpopulation-specific inhibitory populations. We then ask how correlations can be enhanced or marred via the strength of outgoing and incoming connections to these inhibitory populations. By performing a pathway expansion of the covariance matrix, we find that a single inhibitory population with sufficiently strong I to E connections can lead to stronger than expected positive correlations across excitatory populations. However, this result is highly parameter dependent. When considering an inhibition-stabilized network (ISN) the viable parameter regime shrinks dramatically into a narrow band close to the edge of stability. We find that both non-ISN and ISN regimes can recover the ability to robustly explain the experimental results by allowing for two tuned inhibitory populations, meaning that each inhibitory population preferentially connects to one of the two excitatory populations. Our results therefore imply that complexity in excitation should be mirrored by complexity in the structure of inhibition.

Cortical activity is characterized by state-specific dynamics arising from the interplay between connectivity, cellular diversity, and intrinsic properties. During non-Rapid Eye Movement (NREM) sleep, cortical population activity alternates between periods of neuronal firing (“UP” states) and neuronal silence (“DOWN” states). Patterns of neuronal activity at DOWN-to-UP (DU) transitions have functional relevance beyond sleep: they are related to sensory coding during wakefulness and support homeostatic processes and memory consolidation. Despite this functional importance, the factors that organize these spiking patterns remain unknown but mechanisms that rely on network connectivity or intrinsic excitability have been proposed. In order to elucidate the mechanisms that organize spontaneous activity, we recorded populations of neurons in the head-direction cortex (HDC, i.e., post-subiculum), where the behavioral correlates of most neurons are well accounted for. Neuronal tuning to HD was independent of anatomical position. However, while UP-DOWN (UD) transitions were synchronous along the dorsoventral (DV) axis, we observed sequential activation of neurons at DU transitions. To understand the mechanisms underlying these traveling waves at UP state onset, we built a computational model with a linear array of recurrently connected adapting units and compared the effects of different biophysical gradients. We found that, unlike gradients in local connectivity, excitability/input, and adaptive current, a gradient in rectifying current (Ih) was able to uniquely reproduce the experimental observations, and predict a yet-unobserved relationship between UP onset and post-DOWN rebound activity. Subsequent ex vivo intracellular recordings confirmed the predicted DV gradient of Ih in HDC. In conclusion, precisely organized spontaneous population activity patterns may be independent of circuit features and sensory coding but instead may only reflect intrinsic neuronal properties. Yet, the resulting traveling waves have the potential to anatomically segment computation in output structures like the medial entorhinal cortex (MEC) and indirectly, the hippocampus.

Exploring novel approaches to auditory rehabilitation, we aim to demonstrate, in mice, the efficiency of an optogenetic cortical implant. Several studies have shown that mice can use patterned optogenetic stimulations of the sensory cortex to drive their behaviour. It was however never tested if it is possible to provide a detailed representation of sensory inputs through such stimulation patterns. To explore this key question for cortical implant devices, we developed a novel sensory encoding model based on a convolutional autoencoder, which is able to temporally compress and denoise 500ms sounds into a 10x10 array of stimulation sites while preserving latent space continuity and detailed sound information. To minimize spatial crosstalk between stimulation sites, we actually limit the latent representations to the 10 largest activations and impose spatial sparseness constraints during model training. We could then demonstrate that mice can discriminate these activity patterns when applied onto their auditory cortex using a video-projector setup for mesoscopic patterned optogenetic stimulation. After mastery of the discrimination task, we presented in catch trials various new patterns from the model and observed that several mice elicit similar behavioural categorization responses across patterns. This demonstrates that the artificial patterns imposed to auditory cortex produce a robust representation structure that can be used to solve a task. These results indicate that constrained autoencoder model can be used for generating artificial auditory perception via an array of cortical stimulators. We aim to further benchmark these artificial perceptions against already acquired auditory discrimination performances of normally-hearing mice.

The COSYNE 2023 conference provided an inclusive forum for exchanging experimental and theoretical approaches to problems in systems neuroscience, continuing the tradition of bringing together the computational neuroscience community. The main meeting was held in Montreal followed by post-conference workshops in Mont-Tremblant, fostering intensive discussions and collaboration.

Neuromatch 5 (Neuromatch Conference 2022) was a fully virtual conference focused on computational neuroscience broadly construed, including machine learning work with explicit biological links. After four successful Neuromatch conferences, the fifth edition consolidated proven innovations from past events, featuring a series of talks hosted on Crowdcast and flash talk sessions (pre-recorded videos) with dedicated discussion times on Reddit.

The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. The main meeting is single-track, with invited talks selected by the Executive Committee and additional talks and posters selected by the Program Committee based on submitted abstracts. The workshops feature in-depth discussion of current topics of interest in a small group setting.