Topic: synaptic strength

Which nodes in a brain network causally influence one another, and how do such interactions utilize the underlying structural connectivity? One of the fundamental goals of neuroscience is to pinpoint such causal relations. Conventionally, these relationships are established by manipulating a node while tracking changes in another node. A causal role is then assigned to the first node if this intervention led to a significant change in the state of the tracked node. In this presentation, I use a series of intuitive thought experiments to demonstrate the methodological shortcomings of the curren

Behavioral experience shapes neural circuits, adding and subtracting connections between neurons that will ultimately control sensation and perception. We are using natural sensory experience to uncover basic principles of information processing in the cerebral cortex, with a focus on how sensory learning can selectively alter synaptic strength. I will discuss recent findings that differentiate reinforcement learning from sensory experience, showing rapid and selective plasticity of thalamic and inhibitory synapses within primary sensory cortex.

Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for compari

The ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic s

Memory and learning are significantly influenced by long-term changes in synaptic strength, though the exact molecular mechanisms remain unclear. The postsynaptic density (PSD) is a critical component for storing and organizing synaptic long-term changes. The PSD itself is organized into complex nano-clusters, which are modulated during synaptic plasticity. However, the underlying organizational principles yielding such changes and maintaining stability otherwise are still to a large degree unknown. Recent research assumes that liquid-liquid phase separation (LLPS) might be crucial for PSD for

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4

AMPA receptors (AMPARs) mediate fast excitatory neurotransmission and participate in memory formation. Synaptic strength is proportional to the number of AMPARs in the postsynaptic membrane. Previous models of AMPA receptor trafficking can explain their fast, brief incorporation in dendritic spines after LTP induction but offer limited insights into long-lasting changes in AMPAR content at potentiated spines[1,2]. Hence, a unified modeling framework is necessary to understand persistent synaptic changes in AMPAR numbers. Molecular trafficking, such as diffusion, active transport, endo/exocytos