Synaptic Plasticity
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Related Articles from SNS
Synaptic Plasticity as a Function of the Temporal Derivative
A major outstanding question in neuroscience is whether the neocortex uses the same powerful learning algorithm as current AI models: error backpropagation. One way this could be accomplished is as a function of the temporal derivative (i.e., differences in neural activity states over time), which can closely approximate the backpropagated error gradient. We tested the hypothesis that the direction of synaptic plasticity is a function of the temporal derivative in synaptic activity over the...
Short-Term Synaptic Plasticity Stabilizes Goal-Conditioned Dynamics in a PFC-Inspired Reservoir Model for Multistep Goal-Directed Action Planning
arXiv:2606.03481v1 Announce Type: cross Abstract: The prefrontal cortex (PFC) maintains goal information for action planning, but how recurrent circuits preserve it in an action-usable form over behavioral timescales remains unclear. Here we ask whether short-term synaptic plasticity (STP) can stabilize goal information as action-usable, goal-conditioned dynamics. We incorporated STP into a PFC-inspired reservoir computing model with basal-ganglia-inspired temporal-difference readout...
The ASD Risk Gene D5Ertd579e Regulates Synaptic Plasticity and Selective Autism-Related Behaviors
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition shaped by contributions from hundreds of genes, many of which remain poorly characterized. This largely uncharacterized genomic landscape may therefore hold critical insight into how diverse molecular disruptions converge on shared social phenotypes. Here, we investigated KIAA0232 (mouse orthologue D5Ertd579e), an uncharacterized locus lacking known functional domains, using a global null knockout mouse model.
Hybrid-plasticity Photonic Synapses Enabling Hardware-Level Neural Reuse
arXiv:2605.25712v2 Announce Type: replace Abstract: Biological intelligence is distinguished by neural reuse, the capacity to preserve established learning memory while repurposing it for new tasks and dynamic environments. Bringing this capability to photonic hardware requires hybrid plasticity, namely the coexistence of long-term synaptic plasticity for persistent weight storage and short-term synaptic plasticity for rapid, reversible adaptation within a single synaptic element; however,...
A prognostic human brain network for diffuse midline glioma
Abstract Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3,4,5,6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3,4,5,6,7,8,9 and other glial malignancies10,11,12 through paracrine mechanisms and direct neuron-to-glioma synapses.
Ubiquity of Emergent Hebbian Dynamics in Regularized Learning
Announce Type: replace Abstract: Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized by homeostatic constraints. This raises an identifiability question: does observing Hebbian/anti-Hebbian structure in synaptic updates uniquely imply an underlying Hebbian computation? We identify an alternative, emergent route.
Olfactory learning potentiates long-range cortical GABAergic inputs onto adult-born neurons.
Adult neurogenesis in the olfactory bulb (OB) contributes to structural and functional plasticity, influencing olfactory perception, learning, and memory. Adult-born granule cells (abGCs) exhibit unique morphological, electrophysiological, and synaptic properties compared to their neonatally born counterparts, suggesting a specialized role in olfactory processing. In the OB, such processing relies both on sensory inputs from the olfactory epithelium as well as top-down cortical feedback,...
What can a neuron compute
Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic...
ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training
Announce Type: new Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across a wide range of applications. However, the large number of synaptic connections in SNNs leads to intensive weight-update computation by on-chip learning algorithms during training, resulting in substantial hardware resource utilization and energy consumption. Among existing SNN learning algorithms,...
A thalamus–brainstem attractor network drives history-biased decisions
Abstract Natural environments often change gradually, making it adaptive to bias decisions on the basis of the recent past — a phenomenon known as serial dependence1,2,3. Large-scale recordings during behaviour have identified that serial dependence is a common motif for decision-making, with neural representations of past experiences found throughout the brain4,5,6,7,8,9,10,11. However, it remains unclear whether this bias arises from dedicated neural circuits with history-specific...