Learning inhibition-controlled sequences through unsupervised plasticity in spiking neural networks
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2022-11-28
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en
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Abstract
The brain can learn the temporal order of events through exposure to sequential information.
This can lead to the emergence of feedforward structures within cortical circuits. Understanding
the neurobiological mechanisms that support the generation of such structures
can deepen the knowledge of the learning and memory processes. Previous computational
approaches have addressed sequence learning in spiking neural networks through plasticity
between excitatory neurons. However, to do so, they rely on handcrafted connectivity or nonbiological
assumptions. Recent studies have indicated that circuits of inhibitory interneurons
might control several functions by regulating the activity of excitatory cells. Here, I propose a
model for learning inhibition-controlled sequences through the interaction of excitatory and
inhibitory spike-timing dependent plasticity (STDP). The proposed network consists of one
excitatory, E, and two inhibitory populations, I1 and I2, that are responsible for the formation
of excitatory assemblies and their arrangement in sequential order, respectively. In contrast
to other models of sequence learning, here, learning is realized through unsupervised plasticity
principles alone. I show that plasticity on the I1-to-E, I2-to-E, and E-to-I2 synapses are
essential for inhibition-controlled sequence learning. Adjusting the timescales and learning
rate of inhibitory STDP and regulating the activity of I2 neurons contribute to the reliable
replay of sequences. Plasticity between E and I2 neurons during spontaneous background
activity after learning degrades the learned connectivity and can lead to sequence memory
loss. The model shows that interneuron circuits can have a functional role in memory by
learning to control the propagation of activity between excitatory cells. Moreover, it suggests
that including multiple populations of interneurons with distinct functions increases
neurobiological realism compared to previous models of sequence learning.
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Faculteit der Sociale Wetenschappen