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Topic: recurrent spiking neural network

ePoster
2 ePosters

In Computational Neuroscience and Mathematical Modeling

ePoster · Computational Neuroscience

Homeostatic regulation through aggregate synaptic dynamics at multiple timescales

Petros Vlachos, Jochen Triesch · Bernstein Conference 2024

Synaptic changes driven by Hebbian plasticity are unstable and require compensatory mechanisms to homeostatically control neuronal activity. Synaptic scaling regulates the synaptic efficacies to bring individual neurons to a set firing regime [1]. However, the timescales of synaptic scaling are too slow to counterbalance the rapid growth due to Hebbian mechanisms [2]. Heterosynaptic plasticity that operates on timescales similar to Hebbian processes can prevent runaway potentiation [3] but its ability to homeostatically control activity is uncertain. Here, we introduce aggregate scaling, a si

ePoster · Computational Neuroscience

Simulating odor representations and responses in the olfactory bulb using eligibility propagation (e-prop)

Fabrizio Musacchio, Monika Müller, Martin Fuhrmann · Bernstein Conference 2024

Understanding the neural mechanisms underlying odor representation and memory in the olfactory bulb (OB) is critical for advancing our knowledge of sensory processing and neural plasticity. Inspired by in vivo experimental findings [1, 2], we applied the eligibility propagation (e-prop) learning rule [3] to simulate the cholinergic and GABAergic responses to distinct odors. [1, 2] found differential activation patterns in glomeruli of the OB in response to different odor exposures. The modulation of activity by cholinergic and GABAergic projections from the horizontal diagonal band (HDB) was s