Consciousness at the edge of chaos

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.

Spike train structure of cortical transcriptomic populations in vivo

The cortex comprises many neuronal types, which can be distinguished by their transcriptomes: the sets of genes they express. Little is known about the in vivo activity of these cell types, particularly as regards the structure of their spike trains, which might provide clues to cortical circuit function. To address this question, we used Neuropixels electrodes to record layer 5 excitatory populations in mouse V1, then transcriptomically identified the recorded cell types. To do so, we performed a subsequent recording of the same cells using 2-photon (2p) calcium imaging, identifying neurons between the two recording modalities by fingerprinting their responses to a “zebra noise” stimulus and estimating the path of the electrode through the 2p stack with a probabilistic method. We then cut brain slices and performed in situ transcriptomics to localize ~300 genes using coppaFISH3d, a new open source method, and aligned the transcriptomic data to the 2p stack. Analysis of the data is ongoing, and suggests substantial differences in spike time coordination between ET and IT neurons, as well as between transcriptomic subtypes of both these excitatory types.

Astrocytes release glutamate by regulated exocytosis in health and disease

Astrocytes release glutamate by regulated exocytosis in health and disease Vladimir Parpura, International Translational Neuroscience Research Institute, Zhejiang Chinese Medical University, Hangzhou, P.R. China Parpura will present you with the evidence that astrocytes, a subtype of glial cells in the brain, can exocytotically release the neurotransmitter glutamate and how this release is regulated. Spatiotemporal characteristic of vesicular fusion that underlie glutamate release in astrocytes will be discussed. He will also present data on a translational project in which this release pathway can be targeted for the treatment of glioblastoma, the deadliest brain cancer.

Neural architectures: what are they good for anyway?

The brain has a highly complex structure in terms of cell types and wiring between different regions. What is it for, if anything? I'll start this talk by asking what might an answer to this question even look like given that we can't run an alternative universe where our brains are structured differently. (Preview: we can do this with models!) I'll then talk about some of our work in two areas: (1) does the modular structure of the brain contribute to specialisation of function? (2) how do different cell types and architectures contribute to multimodal sensory processing?

Dimensionality reduction beyond neural subspaces

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new tensor decomposition that captures and demixes these different types of covariability to reveal task-relevant structure in neural activity. Finally, I will close with some thoughts regarding the circuit mechanisms that could generate mixed covariability. Together this work points to a need to consider new possibilities for how neural populations encode sensory, cognitive, and behavioral variables beyond neural subspaces.

Contentopic mapping and object dimensionality - a novel understanding on the organization of object knowledge

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

Pancreatic Opioids Regulate Ingestive and Metabolic Phenotypes

Sensory cognition

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

Understanding the complex behaviors of the ‘simple’ cerebellar circuit

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these studies reveal specific, cerebellum-dependent features of these complex, whole-body behaviors. This provides a key entry point for understanding how neural computations within the highly stereotyped cerebellar circuit support the precise coordination of muscle activity in space and time. Finally, I will present recent unpublished data that provide surprising insights into how cerebellar circuits flexibly coordinate whole-body movements in dynamic environments.

Brain-Wide Compositionality and Learning Dynamics in Biological Agents

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales. Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

Clonal analysis at single cell level helps to understand neural crest development

Recent research on the neural crest has revealed the multipotency and plasticity of nerve-associated Schwann cell precursors, which can differentiate into diverse cell types, including parasympathetic neurons, neuroendocrine cells, and mesenchymal stem cells. These findings challenge the traditional view of peripheral nerves, highlighting their role as niches for migratory progenitor cells that contribute to tissue formation and regeneration.

Unmotivated bias

In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Mitochondrial diversity in the mouse and human brain

The basis of the mind, of mental states, and complex behaviors is the flow of energy through microscopic and macroscopic brain structures. Energy flow through brain circuits is powered by thousands of mitochondria populating the inside of every neuron, glial, and other nucleated cell across the brain-body unit. This seminar will cover emerging approaches to study the mind-mitochondria connection and present early attempts to map the distribution and diversity of mitochondria across brain tissue. In rodents, I will present convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct behaviorally-relevant mitochondrial phenotypes exist across large-scale mouse brain networks. Extending these findings to the human brain, I will present a developing systematic biochemical and molecular map of mitochondrial variation across cortical and subcortical brain structures, representing a foundation to understand the origin of complex energy patterns that give rise to the human mind.

Learning representations of specifics and generalities over time

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

How are the epileptogenesis clocks ticking?

The epileptogenesis process is associated with large-scale changes in gene expression, which contribute to the remodelling of brain networks permanently altering excitability. About 80% of the protein coding genes are under the influence of the circadian rhythms. These are 24-hour endogenous rhythms that determine a large number of daily changes in physiology and behavior in our bodies. In the brain, the master clock regulates a large number of pathways that are important during epileptogenesis and established-epilepsy, such as neurotransmission, synaptic homeostasis, inflammation, blood-brain barrier among others. In-depth mapping of the molecular basis of circadian timing in the brain is key for a complete understanding of the cellular and molecular events connecting genes to phenotypes.

Roles of inhibition in stabilizing and shaping the response of cortical networks

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

Molecular Characterization of Retinal Cell Types: Insights into Evolutionary Origins and Regional Specializations

Of glia and macrophages, signaling hubs in development and homeostasis

We are interested in the biology of macrophages, which represent the first line of defense against pathogens. In Drosophila, the embryonic hemocytes arise from the mesoderm whereas glial cells arise from multipotent precursors in the neurogenic region. These cell types represent, respectively, the macrophages located outside and within the nervous system (similar to vertebrate microglia). Thus, despite their different origin, hemocytes and glia display common functions. In addition, both cell types express the Glide/Gcm transcription factor, which plays an evolutionarily conserved role as an anti-inflammatory factor. Moreover, embryonic hemocytes play an evolutionarily conserved and fundamental role in development. The ability to migrate and to contact different tissues/organs most likely allow macrophages to function as signaling hubs. The function of macrophages beyond the recognition of the non-self calls for revisiting the biology of these heterogeneous and plastic cells in physiological and pathological conditions across evolution.

Memory: types and neuroanatomical basis

Cellular and genetic mechanisms of cerebral cortex folding

One of the most prominent features of the human brain is the fabulous size of the cerebral cortex and its intricate folding, both of which emerge during development. Over the last few years, work from my lab has shown that specific cellular and genetic mechanisms play central roles in cortex folding, particularly linked to neural stem and progenitor cells. Key mechanisms include high rates of neurogenesis, high abundance of basal Radial Glia Cells (bRGCs), and neuron migration, all of which are intertwined during development. We have also shown that primary cortical folds follow highly stereotyped patterns, defined by a spatial-temporal protomap of gene expression within germinal layers of the developing cortex. I will present recent findings from my laboratory revealing novel cellular and genetic mechanisms that regulate cortex expansion and folding. We have uncovered the contribution of epigenetic regulation to the establishment of the cortex folding protomap, modulating the expression levels of key transcription factors that control progenitor cell proliferation and cortex folding. At the single cell level, we have identified an unprecedented diversity of cortical progenitor cell classes in the ferret and human embryonic cortex. These are differentially enriched in gyrus versus sulcus regions and establish parallel cell lineages, not observed in mouse. Our findings show that genetic and epigenetic mechanisms in gyrencephalic species diversify cortical progenitor cell types and implement parallel cell linages, driving the expansion of neurogenesis and patterning cerebral cortex folds.

Bio-realistic multiscale modeling of cortical circuits

A central question in neuroscience is how the structure of brain circuits determines their activity and function. To explore this systematically, we developed a 230,000-neuron model of mouse primary visual cortex (area V1). The model integrates a broad array of experimental data:Distribution and morpho-electric properties of different neuron types in V1.

Movements and engagement during decision-making

When experts are immersed in a task, a natural assumption is that their brains prioritize task-related activity. Accordingly, most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. Surprisingly, we observed that during decision-making, the cortex-wide activity of multiple cell types is dominated by movements, especially “uninstructed movements”, that are spontaneously expressed. These observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. To understand the relationship between these movements and decision-making, we examined the movements more closely. We tested whether the magnitude or the timing of the movements was correlated with decision-making performance. To do this, we partitioned movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This hinted that the timing of spontaneous movements could indicate periods of disengagement. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity. Surprisingly, we found that the same movement impacts neural activity more strongly when animals are disengaged. An intriguing possibility is that these larger movement signals disrupt cognitive computations, leading to poor decision-making performance. Taken together, these observations argue that movements and cognitionare closely intertwined, even during expert decision-making.

The role of CNS microglia in health and disease

Microglia are the resident CNS macrophages of the brain parenchyma. They have many and opposing roles in health and disease, ranging from inflammatory to anti-inflammatory and protective functions, depending on the developmental stage and the disease context. In Multiple Sclerosis, microglia are involved to important hallmarks of the disease, such as inflammation, demyelination, axonal damage and remyelination, however the exact mechanisms controlling their transformation towards a protective or devastating phenotype during the disease progression remains largely unknown until now. We wish to understand how brain microglia respond to demyelinating insults and how their behaviour changes in recovery. To do so we developed a novel histopathological analysis approach in 3D and a cell-based analysis tool that when applied in the cuprizone model of demyelination revealed region- and disease- dependent changes in microglial dynamics in the brain grey matter during demyelination and remyelination. We now use similar approaches with the aim to unravel sensitive changes in microglial dynamics during neuroinflammation in the EAE model. Furthermore, we employ constitutive knockout and tamoxifen-inducible gene-targeting approaches, immunological techniques, genetics and bioinformatics and currently seek to clarify the specific role of the brain resident microglial NF-κB molecular pathway versus other tissue macrophages in EAE.

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

Comparative transcriptomics of retinal cell types

Freeze or flee ? New insights from rodent models of autism

Individuals afflicted with certain types of autism spectrum disorder often exhibit impaired cognitive function alongside enhanced emotional symptoms and mood lability. However, current understanding of the pathogenesis of autism and intellectual disabilities is based primarily on studies in the hippocampus and cortex, brain areas involved in cognitive function. But, these disorders are also associated with strong emotional symptoms, which are likely to involve changes in the amygdala and other brain areas. In this talk I will highlight these issues by presenting analyses in rat models of ASD/ID lacking Nlgn3 and Frm1 (causing Fragile X Syndrome). In addition to identifying new circuit and cellular alterations underlying divergent patterns of fear expression, these findings also suggest novel therapeutic strategies.

Epilepsy genetics 2023: From research to advanced clinical genetic test interpretation

The presentation will provide an overview of the expanding role of genetic factors in epilepsy. It will delve into the fundamentals of this field and elucidate how digital tools and resources can aid in the re-evaluation of genetic test results. In the initial segment of the presentation, Dr. Lal will examine the advancements made over the past two decades regarding the genetic architecture of various epilepsy types. Additionally, he will present research studies in which he has actively participated, offering concrete examples. Subsequently, during the second part of the talk, Dr. Lal will share the ongoing research projects that focus on epilepsy genetics, bioinformatics, and health record data science.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

Immunosuppression for Parkinson's disease - a new therapeutic strategy?

Caroline Williams-Gray is a Principal Research Associate in the Department of Clinical Neurosciences, University of Cambridge, and an honorary consultant neurologist specializing in Parkinson’s disease and movement disorders. She leads a translational research group investigating the clinical and biological heterogeneity of PD, with the ultimate goal of developing more targeted therapies for different Parkinson’s subtypes. Her recent work has focused on the theory that the immune system plays a significant role in mediating the heterogeneity of PD and its progression. Her lab is investigating this using blood and CSF -based immune markers, PET neuroimaging and neuropathology in stratified PD cohorts; and she is leading the first randomized controlled trial repurposing a peripheral immunosuppressive drug (azathioprine) to slow the progression of PD.

Bernstein Student Workshop Series

The Bernstein Student Workshop Series is an initiative of the student members of the Bernstein Network. It provides a unique opportunity to enhance the technical exchange on a peer-to-peer basis. The series is motivated by the idea of bridging the gap between theoretical and experimental neuroscience by bringing together methodological expertise in the network. Unlike conventional workshops, a talented junior scientist will first give a tutorial about a specific theoretical or experimental technique, and then give a talk about their own research to demonstrate how the technique helps to address neuroscience questions. The workshop series is designed to cover a wide range of theoretical and experimental techniques and to elucidate how different techniques can be applied to answer different types of neuroscience questions. Combining the technical tutorial and the research talk, the workshop series aims to promote knowledge sharing in the community and enhance in-depth discussions among students from diverse backgrounds.

A new framework for modeling innate capabilities in network with diverse types of spiking neurons: Probabilistic Skeleton

Bernstein Conference 2024

Knocking out co-active plasticity rules in neural networks reveals synapse type-specific contributions for learning and memory

Bernstein Conference 2024

Migraine mutation of a Na+ channel induces a switch in excitability type and energetically expensive spikes in an experimentally-constrained model of fast-spiking neurons

Bernstein Conference 2024

Top-down modulation shapes timescales via synaptic plasticity in cortical circuits with multiple interneuron types

Bernstein Conference 2024

Normative models of spatio-spectral decorrelation in natural scenes predict experimentally observed ratio of PR types

COSYNE 2022

Normative models of spatio-spectral decorrelation in natural scenes predict experimentally observed ratio of PR types

COSYNE 2022

Task demands drive choice of navigation strategy and distinct types of spatial representations

COSYNE 2022

Task demands drive choice of navigation strategy and distinct types of spatial representations

COSYNE 2022

Time-warped state space models for distinguishing movement type and vigor

COSYNE 2022

Time-warped state space models for distinguishing movement type and vigor

COSYNE 2022

Distinct transformations of perceptual sensitivity by inhibitory neuron subtypes in V1

COSYNE 2023

Functional Subtypes of Synaptic Dynamics Revealed by Model-based Classification

COSYNE 2023

Machine learning of functional network and molecular mechanisms in autism spectrum disorder subtypes

COSYNE 2023

Multi-modal composition of physiological signals to delineate candidate cell types in-vivo

COSYNE 2023

Only two types of attractors support representation of continuous variables, and learning over long time-spans

COSYNE 2023

Synaptic-type-specific clustering optimizes the computational capabilities of balanced recurrent networks

COSYNE 2023

Two types of locus coeruleus norepinephrine neurons drive reinforcement learning

COSYNE 2023

Computational specialization of cortical cell types

COSYNE 2025

Distinct roles of cortical layer 5 subtypes in associative learning

COSYNE 2025

Inhibitory cell-type-specific properties and transformations set up a unique depth-dependent temporal integration scheme in the human neocortical circuit

COSYNE 2025

Mapping functional differences across cell types using a group embedding-enhanced transformer

COSYNE 2025

Non-stereotyped Neural States in Canary HVC indicate song syntax plasticity

COSYNE 2025

State modulation in spatial networks with three interneuron subtypes

COSYNE 2025

Understanding the effects of neural perturbations using cell-type dynamical systems

COSYNE 2025

In vivo cell-type and brain region classification via multimodal contrastive learning

COSYNE 2025

AAV-mediated overexpression of wild-type human alpha-synuclein leads to alterations in gut microbiota in a ‘brain-first’ rat model of prodromal Parkinson’s disease

FENS Forum 2024

Achieving cell-type specific transduction with adeno-associated viral vectors in pigeons (Columba livia)

FENS Forum 2024

An activator of voltage-gated K+ channels Kv1.1 as a therapeutic candidate for episodic ataxia type 1

FENS Forum 2024

Adinazolam, a benzodiazepine-type new psychoactive substance, produces reinforcement and dependence in rodents

FENS Forum 2024

Aggression control by type 2 diabetes risk gene Dusp8

FENS Forum 2024

Alterations in GABA polarity contribute to changes in network activity in a model of focal cortical dysplasia type 2

FENS Forum 2024

Alterations in activity-regulated inhibitor of death gene expression in spinocerebellar ataxia type 2 and type 3

FENS Forum 2024

Antioxidant effect of combined administration of metformin and propionate in a rat model of type 2 diabetes mellitus

FENS Forum 2024

APOE genotype effects on the human gut microbiome

FENS Forum 2024

Assessment of Purkinje neuron degeneration in the flocculus vs. medial cerebellum in a mouse model of spinocerebellar ataxia type 13 (SCA13)

FENS Forum 2024

Atypical astrocytes in the aging brain: An underreported phenotype where downregulated membrane proteins disrupt glial regulated homeostatic capacities

FENS Forum 2024

Behavioural and neural influences of social partner’s motivational type on food preference

FENS Forum 2024

Boosting MFN2 levels in neurons using adeno-associated virus (AAV) vectors as a therapy for Charcot-Marie-Tooth disease type 2A

FENS Forum 2024

Cell- and layer-type specific intracortical effects of pulsed and continuous wave infrared neural stimulation revealed by high-density laminar recordings in the rat neocortex

FENS Forum 2024

Dendritic nonlinearities and synapse type-specific input clustering enable the development of input selectivity in a wide range of settings.

Bernstein Conference 2024