Synaptic plasticity

GoEMMI (GOettingen EMergent Minds) is a three-week winter school on learning and computation in brains and machines, held from 15 February to 6 March 2027 at the Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, Göttingen. The programme is organised around three blocks: the theory of emergence and computation, covering conceptual and mathematical frameworks for emergent macrovariables, dynamical autonomy, compositionality and information-theoretic approaches to computation and causality; learning in brains and machines, covering learning objectives, representation formati

…It connects engram formation and synaptic plasticity with encoding, consolidation, persistence and flexibility. Sessions examine transcriptional, translational, epigenetic and RNA regulation, together with neuromodulation, before considering circuit vulnerability and memory disruption in neurodegenerative and neuropsychiatric disorders. Novel methods and unresolved questions connect the different biological scales. A satellite symposium on synapses and c

Research in Taro Toyoizumi’s laboratory connects synaptic plasticity, information processing and adaptive neural circuits. This role leads computational work on sleep within a multiscale CREST BioDX collaboration with Kaoru Inokuchi and Tatsuya Haga. Analytical approaches draw on statistical physics and information theory, alongside mathematical models of learning and memory. The postdoctoral category is for researchers within five years of their doctorate; the research-scientist category is for more experienced doctorate holders. The appointment is at RIKEN’s Wako campus, with annual renewal

…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

Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning

Behavioral time scale synaptic plasticity underlies CA1 place fields

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM)

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. This study by Marios Abatis et al. demonstrates how fear conditioning strengthens synaptic connections between engram cells in the lateral amygdala, revealed through optogenetic identification of neuronal ensembles and electrophysiological measurements. The work provides crucial insights into memory formation mechanisms at the synaptic level, with implications for understanding anxiety disorders and developing targeted interventions. Presented by Dr. Kenneth Hayworth, this journal club will explore

…Due to the locality constraint of synaptic plasticity, synaptic weights should only be updated using signals available at presynaptic and postsynaptic neurons, without accessing information from unconnected neurons. How a biological neuron encodes and transmits feedforward predictions and er

Major efforts in neuroscience are dedicated to uncovering synaptic plasticity rules which underlie learning-related changes of neural activity in vivo. Plasticity rules measured in vitro are highly heterogeneous, suggesting that learned behaviors could be supported by particular combinations of plasticity rules. We studied the emergence of preparatory neural activity in the anterior lateral motor (ALM) cortex during a delayed-discrimination task, a paradigmatic example of learned neural-dynamics supporting flexible goal-directed behaviors. Specifically, we constrained an attractor network mode

…Dynamic connectivity relies on plasticity rules to adjust synapses, enabling learning and neural circuit formation. Networks with only excitatory synaptic plasticity can become unstable due to runaway excitation, compromising a circuit's ability to store memories. To counter this, homeostatic synaptic plasticity of inhibitory connections provide a matched negative feedback that stabilizes neural activity and any associated learning. In this study, we outline the conditions under which recurrent

A long-standing theory proposes memories are stored in the synaptic wiring of strongly connected neural populations, known as neural assemblies. However, experimental evidence for assembly formation has been limited to inferring changes in synaptic connectivity from changes in neural activity following behavioral learning. Using tagged c-fos activity combined with channelrhodopsin expression, we directly measured the synaptic strength between neurons in the Piriform cortex (PCx) following mice learning an odor discrimination task. Our results suggest that an assembly forms after learning, but

Our ability to form and retrieve memories relies on a network of connected brain regions. In particular, neuronal ensembles across regions form distributed memory engrams that have structural and functional connectivity. However, the computational principles underlying the distributed nature of memory remain unclear. Here, we show that distributed engrams enable memory generalization and discrimination in parallel across brain regions. Orthogonal and complementary, generalization and discrimination are computations that must be balanced for adaptive, memory-guided behavior. For instance, while

…and that learning relies on changes in synaptic strength. Here we aim to extend this tenet into the temporal domain by proposing that synapses not only learn to strengthen or weaken, but can learn when to be weak or strong on the subsecond scale. It has been known for more than half a century that synapses exhibit short-term synaptic plasticity (STP): a form of presynaptic, use-dependent, plasticity in which consecutive spikes can elicit progressively depressing (short-term depression/

Synaptic plasticity is key to the brain’s life-long learning capabilities, but the link between changes at individual synapses and emerging network properties remains elusive. Recent meta-learning approaches have started automating the search through the space of possible plasticity rules for plausible candidates. However, these approaches were either limited to narrow sets of rules, or did not investigate any network function beyond stability. Here, we identify thousands of co-active excitatory(E)-to-E, E-to-inhibitory(I), I-to-E and I-to-I Hebbian rules that elicit learning in large recurren

…amplification for prefrontal synaptic plasticity and is the key to facilitate a low-rank structure in prefrontal comput

…connectivity, mediated by both synaptic plasticity and neuromodulation. Traditional methods for extracting connectivity from activity focus on modeling static connectivity. On the other hand, recent transformer-based models are able to capture nonstationary interactions, but their deep layered structures and nonlinear attention mechanisms make them challenging to interpret, particularly in terms of

How does the brain gain insight from limited novel experience? Humans and animals have a striking ability to learn relationships between items of experience, enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational learning is order learning, which enables transitive inference: if A>B and B>C, then A>C. Remarkably, both humans and animals can quickly and globally reorganize separate learned orderings upon learning a single new item ("list-linking"; learn A>B>C and D>E>F, then C>D, and infer B>E), an instance of fast "reassembly" of exi

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon hypothesized to be critical for understanding synaptic plasticity and neural network stability. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. The model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then train an artificial neural network in which synaptic strengths fluctuate according to our model. T

…is faster than can be explained by synaptic plasticity, making neuromodulation a prime suspect. Neuromodulation is typically modeled as gain changes at the level of somatic activation function. As a result, gain control is limited to the number of neurons N and often proves to be too coarse for multi-muscle coordination. Moreover, somatic gain modulation cannot easily accommodate the simultaneous impact of dif

Plasticity rules governing synaptic changes vary widely across species, brain regions, and brain states. Despite this variability, these rules often give rise to neural circuits with similar structural motifs and functional properties, a phenomenon known as degeneracy. While functional degeneracy---where different plasticity rules result in similar network functions---has been widely studied, the structural aspects of degeneracy remain underexplored. Structural degeneracy refers to cases where distinct plasticity rules produce neural circuits with similar connectivity patterns. To investigate

…of neural activity and changes in synaptic weights, yet the precise rules governing synaptic plasticity in the motor cortex and how they give rise to the observed changes in activity are still unknown. Brain computer interfaces (BCIs) allow for task improvement to be directly linked to changes in neural activity. Recent experiments that pair BCIs with all-optical circuit mapping allow for the simultaneous tracking of in vivo changes in connectivity while task-relevant activity learning occurs. In silico models of learning allow for th

Functional connectivity has been the focus of many research groups aiming to study the interaction between cells and brain regions. A standard method for analyzing connectivity is to statistically compare pairwise interactions between cells or brain regions across behavioral states or conditions. This methodology ignores the intrinsic properties of functional connectivity as a multivariate and dynamic signal, expressing the correlational configuration of the network. In this talk, I will present a geometric approach, combining Graph Theory and Riemannian Geometry to build "a graph of graphs" a

Recent progress in artificial intelligence (AI), particularly through the development of artificial neural networks (ANNs), has significantly benefited from insights gained from neuroscience. This progress has enhanced the replication of complex cognitive tasks such as vision and natural language processing (1,2). However, ANNs still struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly (3–8). Neuromodulators, the chemical messengers that target neurons and synapses in specific brain regions,