The Budapest Computational Neuroscience Forum is a series of informal monthly meetings of Budapest-based computational neuroscientists and computational cognitive scientists with the aim of facilitating discussion and cooperation among researchers working in different institutes and giving an opportunity to students to present their work and get to know the community. Originally started in 2007, restarted in 2017 and then again in 2023 the Forum is now regularly hosted by Central European University, and followed by a social event, both open to anyone interested.
Events of the Forum are advertised on a mailing list. If you wish to be on this list or have any inquiries about the series, contact Mihály Bányai.
Upcoming meeting:
Time: 17:00, November 15., 2023.
Location: CEU, 1051 Bp. Nádor u. 15., Room 203.
Speaker: András Ecker, EPFL
Title: Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model
Abstract: Synaptic plasticity underlies the brain's ability to learn and adapt. This process is often studied in small groups of neurons in vitro or indirectly through its effects on behavior in vivo. Due to the limitations of available experimental techniques, investigating synaptic plasticity at the microcircuit level relies on simulation-based approaches. Although modeling studies provide valuable insights, they are usually limited in scale and generality. To overcome these limitations, we extended a previously published and validated large-scale cortical network model with a recently developed calcium-based model of functional plasticity between excitatory cells. We calibrated the network to mimic an in vivo state characterized by low synaptic release probability and low-rate asynchronous firing, and exposed it to 10 different stimuli. We found that synaptic plasticity sparsely and specifically strengthened synapses forming spatial clusters on postsynaptic dendrites and those between populations of co-firing neurons, also known as cell assemblies: among 312 million synapses, only 5% experienced noticeable plasticity and cross-assembly synapses underwent three times more changes than average. Furthermore, as occasional large-amplitude potentiation was counteracted by more frequent synaptic depression, the network remained stable without explicitly modeling homeostatic plasticity. When comparing the network's responses to the different stimuli before and after plasticity, we found that it became more stimulus-specific after plasticity, manifesting in prolonged activity after selected stimuli and more unique groups of neurons responding exclusively to a single pattern. Taken together, we present a plasticity rule that leads to sparse change and analyze the rules governing those changes.