FENS Regional Meeting held 26-29 May 2027 in Istanbul, Turkey, jointly organised by the Neuroscience Society of Turkey, the Hellenic Society for Neuroscience, the Serbian Neuroscience Society and the Slovenian Neuroscience Association.

This Keystone Symposium examines how nerves and neural activity shape cancer initiation, progression, metastasis, cognition, and treatment, with a focus on actionable therapeutic targets.

Kuan Hong Wang examines how dopaminergic, cannabinoid, and neuroimmune signaling regulate circuit plasticity across development, experience, and disease, and how cross-species work may inform selective therapies.

Funding for mid-career researchers from any discipline who have the potential to become international research leaders and whose work relates to human life, health and wellbeing.

NSF support for translating research discoveries and innovations toward practical use through the TTP Translational and Partnership tracks.

The Society for Neuroscience annual meeting brings the neuroscience community together for a multi-day scientific program, professional development, networking, and presentation of research spanning the field. Registration and housing are open for the Washington, DC meeting.

Funding for early-career researchers from any discipline who are ready to develop their research identity through an innovative project related to human life, health and wellbeing.

An in-person Stanford symposium highlighting recent progress in brain resilience and aging research, followed by a neuroscience poster session featuring work from the Stanford community.

NSF SBIR and STTR funding for startups and small businesses transforming high-risk technologies into products and services with commercial and societal impact.

NSF funding for startups and small businesses developing next-generation scientific instruments, experimental platforms and enabling technologies for research.

Supports exceptional young neuroscientists in the early stages of establishing an independent laboratory and research career. Up to ten scholars receive a total award of $225,000 paid in equal installments of $75,000 in 2027, 2028, and 2029; applications open early August 2026.

Richard L. Huganir discusses research on the molecular mechanisms that regulate glutamate receptors, the brain's major excitatory neurotransmitter receptors, and thereby modulate communication between neurons.

Vinod Menon examines the brain's default mode network as a foundation for autobiographical memory, self-related processing, and consciousness, and considers how these functions compare with current artificial intelligence systems. The virtual session is part of Stanford's Contemplation by Design Summit.

NSF support for interdisciplinary collaborations developing mathematical and theoretical foundations for explainable, reliable, sustainable and trustworthy artificial intelligence.

NSF support for new engineering investigators at eligible non-R1 institutions to initiate research programs, build capacity and advance their careers.

This NIH BRAIN Initiative opportunity supports dissemination of existing resources into neuroscience research practice. Activities may include distribution of tools and reagents, training in new technologies, access to technology platforms or specialized facilities, minor improvements to resource delivery, and adaptations for user communities.

This NIH BRAIN Initiative opportunity supports the development of theories, computational models, and analytical methods for large-scale, complex brain data. Priority is given to novel capabilities for integrating and interpreting physiological, anatomical, connectivity, behavioral, and cell-type-specific data emerging from BRAIN Initiative research.

Picower Institute Colloquium on the Brain and Cognition featuring Christopher Harvey, PhD, of Harvard University, held in Singleton Auditorium (46-3002) at MIT Building 46, 43 Vassar Street.

UCL Department of Neuroinflammation Seminar by Professor Emad Moeendarbary (Professor of Cell Mechanics and Mechanobiology, UCL Mechanical Engineering) on how biophysical factors such as substrate stiffness and axon geometry regulate oligodendrocyte behavior and myelin formation, presenting the AxoMetic in vitro myelination platform for discovery of remyelinating therapies.

Stanford's annual Precision Mental Health Symposium explores how neuroscience, psychiatry, data science, and artificial intelligence can translate brain research into real-world mental-health care. Themes include circuits and cognition, biomarkers and neuroimaging, rapid-acting interventions, neuromodulation, and precision therapeutics.

The MIT Aging Brain Initiative brings together work in molecular imaging, cognition, chemistry, bioengineering, neurodegeneration, and artificial intelligence to address brain aging and Alzheimer's prevention.

Stanford Neurosciences Seminar Series talk by Emery Brown (Massachusetts Institute of Technology, Neuroscience Statistics Research Lab) on deciphering the dynamics of the unconscious brain under general anesthesia, hosted by the Wu Tsai Neurosciences Institute.

Special seminar at MIT's Picower Institute for Learning and Memory with neuroscientist Hee-Sup Shin, MD, PhD, of the Institute for Basic Science (IBS), held in the Picower Seminar Room (46-3310) at 43 Vassar Street.

Two talks present emerging applications of ultrasound technology in neuroscience, spanning brain imaging and human-machine interaction. The in-person seminar is open to MIT researchers, students, and staff and is supported by the MIT School of Science and the Feng Lab at the McGovern Institute.

MIT researchers and students are invited to two talks on emerging applications of ultrasound for non-invasive brain imaging, neural modulation and wearable human-machine interaction.

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

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.

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.

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 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.

Functional connectomics reveals general wiring rule in mouse visual cortex

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.

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.

Life can be easier if one knows the structure of the world, for instance, that a distant roar, a whiff of a heavy musky smell, and black stripes on orange background are caused by a single physical entity. Indeed, the question how such structures can be discovered by the neural networks of the brain has challenged neuroscientists for many decades. A key constraint is that central nervous systems must learn about the structure of the world from observing correlations within continuous streams of spikes that arrive from their sensory peripheries. Recently, a novel family of unsupervised spiking neural network models, self-supervised neural networks, have been shown highly potent in discovering ensembles of recurring spike patterns even when their individual occurrences within background noise were rare and temporally asynchronous. In these models an internal supervisory circuit drives learning within a layer of processing neurons by providing teaching signals computed from the processing layer's past activity. A central limitation of these models is their reliance on the presence of an externally given trial structure: The given end of a sensory episode prompts the supervisory circuit to compute its teaching signals and initiate a learning step within the processing layer. Here we develop a self-timed version of self-supervised networks whose teaching circuit requires neither external clock nor trial-end signals, but rather uses the processing layer's activity to also decide the timing of learning steps. We demonstrate that self-timed self-supervised networks match the performance (convergence times) of the original trial-based learning model, substantially broadening the range of settings to which this approach is applicable. We explore the stability of the learning dynamics arising from the interactions between synaptic plasticity and homeostatic mechanisms. Our work provides a biologically plausible unsupervised neural network model for multi-modal learning of recurring spike patterns within parallel continuous sensory streams.

Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or ‘frame of reference’ relative to which information can be encoded. In line with this hypothesis, there have been many empirical observations of this phase code in the brain. What are the latent dynamics and circuits that support phase coding with neural oscillations? Here, we propose a new computational hypothesis which is derived from analyzing trained recurrent neural networks (RNNs). We train RNNs on a working memory task, while also giving them access to a reference oscillation (either a pure sine wave or rat CA1 local field potentials). The task is to produce an oscillation such that its phase maintains the identity of transient stimuli. Although this task could be solved with static attractors, we find networks converging to oscillatory dynamics that persist in the absence of oscillatory input. Reverse engineering these trained network reveals bistability: in particular, each phase-coded memory corresponds to a separate limit cycle attractor in a toroidal manifold. We characterise the nonlinear dependence of the stability of the attractor dynamics on reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the computations underlying stable phase-coding, we show that trained networks converge to dynamics that can be described as two phase coupled oscillators. Using this insight, we condense our trained networks to a reduced model consisting of two functional modules: one that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. We show how incoming stimuli transiently modify this coupling function. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a novel mechanism by which neural networks can harness reference oscillations for working memory.

An open question in neuroscience is how the brain controls naturally generated movements. An increasing number of studies have shown that high-dimensional neural population dynamics lie within low-dimensional manifolds, which may be integral to neural control of behavior. However, many of these studies have focused on neural activity recorded during experimentally driven movements with little variability, leaving open the question of whether low-dimensional neural manifolds persist during naturalistic movements. In this paper, we investigate neural population dynamics in naturalistic reaching movements across 12 individuals’ electrocorticography (ECoG) data recorded during their 5 day hospital stay. We found that for all 12 individuals the neural dynamics during four types of arm movements did lie within the same neural manifolds. These manifold spaces also did not drift significantly across the multiple days of their hospital stay. We then compared neural dynamics between individuals for the same types of movements, and found that the manifolds were also aligned between individuals. The alignment of these neural manifold spaces during naturalistic movement indicates that neural manifolds may represent a general principle of movement control.

Bayesian models have been successful at accounting for human and animal behavior, yet to what degree they can also explain neural activity is still an open question. While decoding approaches that link neural variability to behavioral uncertainty provide some evidence, stronger tests have tried to link posterior beliefs about specific latent variables in a generative model to neural responses. On one hand, the specificity of the resulting predictions is desirable since it allows us to decide which of the infinitely many parameterizations of the task model (ideal observer) is more closely aligned with the brain's internal model. On the other hand, it is unclear under what conditions we can even expect a match of predictions and data given that current models are drastic simplifications of the rich internal model the brain uses. Furthermore, this approach so far has required strong assumptions about how probabilities are represented in neural responses. Here, we formalize and address both of these problems and derive predictions for when they can be overcome. In particular, we show how to meaningfully differentiate between Bayesian models using neural data with a weak assumption about the neural representation of probabilities, i.e. a kind of linearity that holds for a wide class of probabilistic representations including distributed distributional codes (DDCs) and neural sampling schemes. We demonstrate our method by using simulated V1 neural data to differentiate between two Bayesian models for an orientation discrimination task that are practically indistinguishable based on behavior. The first model contains orientation as an explicit variable to be inferred, while the second model assumes inference over a set of oriented gratings. Our results pave the way for strong and rigorous neural tests of Bayesian models of behavior using neural data, and give us deeper insights into how to correctly interpret neural data.

Accurate perception requires the integration of external (bottom-up) and internally generated (top-down) information. The main recipient of top-down projections in cortex is layer 1, which houses the dendrites of pyramidal cells (PC; Schuman et al., 2021). While layer 1 is devoid of excitatory cell bodies, it contains a variety of interneurons, including neurogliaform cells expressing neuron-derived neurotrophic factor (NDNF; Abs et al., 2018). NDNF interneurons are unique in that they provide slow inhibition, partially via non-synaptic volume release of GABA (Pardi et al. 2020). Yet, their contribution to cortical computations is still unclear. Here, we propose that NDNF interneurons control cortical inhibition in a layer-specific manner. Specifically, we suggest that NDNF-mediated volume release targets presynaptic GABA receptors at the outputs of somatostatin-expressing (SOM) interneurons in layer 1, leaving SOM outputs in lower levels unaffected. We demonstrate in a computational model how this mechanism gradually replaces SOM-mediated inhibition to PC dendrites with NDNF-mediated inhibition, which carries top-down rather than bottom-up information and is slower in time. The competition for dendritic inhibition stems from a mutual inhibition motif between NDNF interneurons and SOM outputs. Notably, it relies on presynaptic inhibition and does not require synaptic connections from NDNF to SOM interneurons. We show that the motif can become bistable such that top-down inputs to NDNF interneurons function as a switch for different circuit dynamics. Finally, we find that the connections of NDNF interneurons within the circuit introduce additional (dis-) inhibitory pathways, changing how the circuit responds to cell type-specific perturbations. Our model elucidates how NDNF interneurons restructure inhibitory circuitry in cortical layer 1. Because NDNF interneurons receive a broad range of top-down inputs, the model suggests a neural mechanism by which top-down information can modulate cortical processing on behaviourally relevant timescales.

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. We expect neural responses to be functionally highly similar within a species (since we expect findings to generalize across animals). What kind of transform will make such similarity most evident? That is, under what kind of transform are conspecifics’ neural responses highly similar to each other? Researchers often default to linear regression as a reasonable transform class for measuring neural response similarity. We propose an improved transform class that uses a generalized linear model (GLM) whose noise matches the approximately Poisson noise in the neural data, and whose non-linear link function is akin to the activation function of a biological neuron. Incorporating these biologically motivated constraints into the inter-animal transform class substantially improves similarity scores compared to linear regression. We then build a DNN model of mouse visual cortex that swaps out ReLU activations for a more biologically plausible softplus activation function, combined with Poisson noise, to produce activations that are more similar to neural responses. We find that a Poisson GLM whose link function exactly matches the model activation function again yields the highest similarity scores between different randomly seeded instances of our softplus models. This result gives mechanistic insight into why the best performing animal transform class has a non-linear link as well as Poisson noise structure. Moreover, we show that our Poisson GLM not only achieves higher similarity scores for the same layer between model instances, but also scores activations in model layers that are physically far apart as highly dissimilar to each other. Finally, we estimate the number of neurons and number of stimuli that would need to be recorded to accurately estimate inter-animal similarity.

Perception is transformed by both stable and time varying (contextual) expectations. This influence is observable in the domain of orientation processing as cardinal bias (repulsion from more common vertical and horizontal orientations) and serial dependance (attraction towards recent stimuli) [1,2]. These biases operate on vastly different time scales and have typically been studied in isolation. Investigating the properties of these two biases in tandem may reveal important insights into general principles of perception (efficient coding, Bayesian inference) that are obscured when studied in isolation. Here, we modeled the responses of human observers in a 2AFC delayed orientation discrimination task. Responses displayed substantial cardinal (6.5±1.2° peak repulsion from cardinal axes, mean±SD) and serial biases (3.6±1.9° peak attraction towards previous stimulus) relative to their overall precision (σ=9.9±0.36°). By leveraging incorrect responses, we found that the center of attraction towards the previous trial was heavily shifted towards the mis-remembered item (shift 16.79±0.79°). To further infer the order that expectations are applied in this hidden process, we fit 6 alternative models and evaluated the resulting cross-validated likelihoods. Responses were best explained when the inducing stimulus included all known biases (including cardinal, its own history bias, and residual misperception not accounted for) and this dynamic history information was integrated at the end of the processing stream (p<.001 compared to all competing models). This suggests that while long term priors are used to make the earliest stages of encoding more efficient, short term expectations are applied at a later stage to support immediate goals. More generally, these results provide a framework for understanding how long term priors (likely arising during development) interact with dynamic contextual factors to jointly drive complex behaviors. [1] Girchick et al.,2011. NatNeuro; [2] Fischer&Whitney,2014. NatNeuro

We used dense (20 m site spacing) local field potential (LFP) and single unit recordings across all layers of the gRSC concurrent with CA1 LFP recordings in behaving mice to investigate circuit activity of high-frequency oscillations (HFOs) in theta and sharp wave-ripples (SWRs). By comparing HFOs occurring at times of SWRs (+/-50 ms) or during theta, we found using current-source density analysis that HFOs in different states are uniformly localized to layer 2/3 (L2/3) with current sources and sinks bridging the L1-L2/3 border. HFOs that occurred outside of SWR times where rhythmically locked to the descending phase of theta and co-occurred with HPC theta locked gamma oscillations. In ensemble recordings of gRSC neurons, subsets of both excitatory and inhibitory neurons had different activity during each type of HFO and findings were consistent with previously reported event triggered neural activity (Nitzan et al., 2020). Additionally, using mice chronically expressing the excitatory rhodopsin Channelrhodopsin in pyramidal cells, we show that a broad light stimulus delivered to specific layers with a LED probe is sufficient to induce HFOs in L2/3 and L5 (Wu, F. et al., 2015). Interestingly, while we don’t see HFOs naturally occurring in L5 the local synaptic network is structured to support HFOs. These findings suggest that gRSC networks support HFOs in the same location at different states, though the natural drivers of these HFO events are different between states. That being the SWR via excitatory subiculum connections, and the highly synchronous HPC-gRSC theta oscillation. HFO events in different states play key roles in hippocampo-cortical dialogue and may allow for information transfer using theta locked gamma-HFO synchrony during locomotion and SWR-HFO synchrony during quiet wakefulness.

Neural decoding is essential for understanding the association between neural activity and behavior. A prerequisite for most decoding methods is spike sorting, the assignment of action potentials (or spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-channel-count probes like Neuropixels (NP) and extracellular analysis pipelines allow for extracting a rich set of spike features to directly decode behavioral correlates using unsorted spiking data. To this end, we propose a density-based decoding algorithm that incorporates our uncertainty about spike assignments in the form of parametric distributions of spike features. Our density-based decoding approach allows for retaining maximum information about the recording and for explicit uncertainty quantification of spike assignments. Our approach can also reduce the computational cost of neural decoding by avoiding spike sorting. With applications to electrophysiological and behavioral data from the International Brain Laboratory (IBL), we demonstrate that our density-based decoding algorithm can outperform decoding algorithms based on thresholding (i.e. multi-unit activity) and algorithms which rely on well-isolated, single-unit activity.