Topic: neural circuits

How can memories remain durable while adapting to new situations? Gisella Vetere examines how the neuronal ensembles encoding a memory change between initial learning and later recall, and how their reorganization across brain networks supports both precise recollection and generalization. Her research combines activity-dependent cell tagging, interventions in neural circuits and analyses across the brain to investigate these transformations. The talk considers how changes in memory representations influence flexible behavior, including circumstances in which that flexibility becomes maladapti

NeuroAlliance brings together cellular and systems neuroscience, biological psychiatry and cognitive neuroscience. The scientific programme examines how brain mechanisms shape cognition, behaviour and psychiatric illness, connecting molecular and cellular approaches with neuroimaging, computational analysis and clinical research. Plenaries address the microbiome–brain relationship across the lifespan, neural mechanisms of blindsight, negative self-beliefs and mental health, navigation circuits in Drosophila, biometals in brain disease, human decision-making, ovarian hormones and mental health,

Cagla Eroglu presents research on how astrocytes regulate synaptic connectivity during development, function and plasticity. The seminar examines molecular and cellular mechanisms that shape neural circuits using imaging, mouse models and complementary experimental approaches.

The goal of the Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The Scientist will use the Institute's large-scale connectivity atlases and brain-wide recordings to understand how the brain's wiring organizes neural dynamics and drives flexible behavior. The position o

The Brain-Body Interaction RIKEN ECL Research Team, led by Tomoe Ishikawa and scheduled to be established on 1 December 2026, seeks one or two research scientists or postdoctoral researchers. Research will use mouse models, neurophysiology, biology and immunology to investigate how social behaviour and neural circuits affect responses to pathogen infection. The programme aims to understand brain–body and neuroimmune regulation of physiological functions and whether neural activity can modulate immune responses. The appointment is based at the Wako campus; employment is planned from 1 April 202

Hokto Kazama’s Laboratory for Circuit Mechanisms of Sensory Perception studies how Drosophila neural circuits process sensory information and generate behavior. The part-time researcher will support genetic crosses, experiments and analysis involving anatomy, in-vivo physiology and behavior. Employment begins 1 April 2027, negotiable. The Wako-based appointment pays JPY 1,600–2,200 per hour, with negotiable weekday hours and renewable one-year contracts. Applications are open until filled. Upload the documents through the official link and notify the laboratory as its instructions require.

Paid doctoral training connecting neural circuits and behaviour through multidisciplinary neuroethology. The programme offers laboratory rotations, methods and transferable-skills training, thesis-committee mentoring and international support. Recruiting faculty for the 2026/2027 selection are Tobias Ackels, Kevin Briggman, Jason Kerr and Tobias Rose. Applications close on 15 October 2026 and require two submitted references. Applicants select two principal investigators and explain their research interests. The source states approximately EUR 2,000 monthly net salary, increasing over time, ba

Michael Brainard's neuroscience group at UCSF is recruiting research technicians to investigate how experience shapes neural circuits and learned behavior. The role combines songbird behavior, neurophysiology, molecular approaches, histology, data analysis, and laboratory-colony support, with opportunities for direct research involvement and mentorship.

Nicholas Weiler talks with Guosong Hong about overcoming the limited penetration of light into brain tissue. Inspired by transparent tissues in glass frogs and the eye, Hong describes approaches involving fluorescent nanomaterials, focused ultrasound and molecules that temporarily reduce optical scattering. The conversation examines experiments with a common food dye and a project to genetically engineer mice with transparent brains from birth. These techniques could provide new ways to observe neural circuits and study how the brain develops and functions.

Computations in neural circuits are often construed as being implemented through the coordinated dynamics of neurons. In this picture, the role of synaptic connectivity is to sculpt neuronal dynamics to implement computations of interest. Of course, synapses are not static but change on a variety of timescales, including fast timescales comparable to those of neurons. Thus, a more accurate view of computation in neural circuits may involve the coupled dynamics of neurons and synapses. This form of computation is closer to what is implemented by Transformers via an equivalence between ongoing s

Modern experimental techniques now reveal the structure and function of neural circuits at unprecedented scale and resolution. How can we use this wealth of data to understand how cells and circuits implement computations underlying behaviour? Achieving this goal requires models that are consistent with biophysical mechanisms and circuit dynamics, yet flexible enough to capture behaviourally relevant computations. We develop simulation-based machine learning methods that address this challenge. I will show how these approaches—in combination with connectomic measurements—make it possible to bu

Lloyd Minor and Karl Deisseroth discuss how tools for mapping and controlling neural circuits are reshaping research on the human mind. Deisseroth recounts the development of optogenetics and its use in investigating the brain activity underlying emotion and behavior. They explore Stanford Medicine's Human Neural Circuitry research program and how advances in circuit-level neuroscience could improve understanding and treatment of mental illness. The conversation also considers scientific discovery and the importance of humility in research.

Understanding how brains learn requires bridging evidence across scales—from behaviour and neural circuits to cells, synapses, and molecules. In our work, we use computational modelling and data analysis to explore how the physical properties of neurons and neural circuits constrain learning. These include limits imposed by brain wiring, energy availability, molecular noise, and the 3D structure of dendritic spines. In this talk I will describe one such project testing if wiring motifs from fly brain connectomes can improve performance of reservoir computers, a type of recurrent neural network

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaot

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphas

Persistent, stimulus-dependent neuronal activity has been observed in numerous brain areas during tasks that require the temporary maintenance of information. Several competing hypotheses for the neuronal mechanisms underlying persistent activity have been proposed. We have employed data-driven models in conjunction with optogenetic disruptions of neural circuits within memory-guided motor tasks. Our findings revealed a mechanism governed by dynamic attractors, pivotal in sustaining neuronal activity. This mechanism, shaped by time-varying inputs reflecting temporal predictions, is instrumenta

Neural computation is inextricably bound to decisions that must be made under time pressure and uncertainty. At the level of neural circuits, single neurons need to decide whether to spike. On longer timescales, the component circuitry needs to decide whether to reconfigure to store memories and adapt to novel situations. In this talk I will focus on two fun ideas in each of these contexts by showing how nonlinearities in neural components naturally form excitable switches that enable reliable decisions to be made in fluctuating environments. I will also issue propaganda that the kind of high

Computation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about the inputs. Past work has detailed how to optimize nonlinearities and circuit weights to maximize information, but we show that selective nonlinearities, acting together with divergent and convergent circuit structure, can improve information transmission over

Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventiona

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons. These neurons exhibit highly complex coordination patterns. Where does this complexity stem from? One candidate is the ubiquitous heterogeneity in connectivity of local neural circuits. Studying neural network dynamics in the linearized regime and using tools from statistical field theory of disordered systems, we derive relations between structure and dynamics that are readily applicable to subsampled recordings of neural circuits: Measuring the statistics of pairwise covarianc

In the central nervous system, neuronal activity is a critical regulator of development and plasticity. Activity-dependent proliferation of healthy glial progenitors, oligodendrocyte precursor cells (OPCs), and the consequent generation of new oligodendrocytes contributes to adaptive myelination. This plasticity of myelin tunes neural circuit function and contributes to healthy cognition. The robust mitogenic effect of neuronal activity on normal oligodendroglial precursor cells, a putative cellular origin for many forms of glioma, suggests that dysregulated or “hijacked” mechanisms of myelin

Learning in neural circuits manifests via slow changes in activity driving behaviour changes. Uncovering how the coordination of plasticity mechanisms found in the brain leads to such changes is a difficult problem, chiefly because we do not have access to the synaptic parameters underlying neural computation. Nevertheless, as neural population recording techniques improve and experiments track changes in neural dynamics over long durations, finer details about plastic changes could be inferred by observing and characterizing changes in neural activity. In this work, we present a step towards

The hypothalamus controls diverse homeostatic functions including fertility. Neural episode generators are required to drive the intermittent pulsatile and surge profiles of reproductive hormone secretion that control gonadal function. Studies in genetic mouse models have been fundamental in defining the neural circuits forming these central pattern generators and the full range of in vitro and in vivo optogenetic and chemogenetic methodologies have enabled investigation into their mechanism of action. The seminar will outline studies defining the hypothalamic “GnRH pulse generator network” an

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they shed light on fundamental functions being optimized or make novel predictions about neural phenomena. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We rigorously examine the claims of deep learning models of grid cells