Publications

Inhibitory neurons for learning

  • Yuqing Zhu, Chadbourne M.B. Smith, Tarek Jabri, Mufeng Tang, Franz Scherr, Jason MacLean. (2023). Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition. bioRxiv. https://doi.org/10.1101/2023.08.29.555334.

neurons in the brain change their synaptic connections during learning. These changes lead to new patterns of neural activity that support new behaviors. can we point to a specific connectivity change that supports a specific new behavior? why yes! we train spiking neural network (snn) models of the brain to complete a binary task, and we find that inhibitory neurons strengthen and weaken their connections in specific ways. if we disrupt these changes, the network does not learn the task. if we do not ask the network to learn the task, these changes do not occur. excitatory neurons typically get most of the credit, but inhibition is also key for learning.


Triangles of activity for stability

  • Kyle Bojanek*, Yuqing Zhu*, Jason MacLean. (2020). Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1007409. *co-first-authors.  

the brain needs to maintain overall stable neuronal activity over an animal’s lifespan. this is not trivial, because many circumstances change over time. in this paper, we show that stability in snn models of the brain relies upon specific three-neuron patterns (triangles!) of activity that repeat over time.