TY - JOUR
T1 - Inhibition stabilization and paradoxical effects in recurrent neural networks with short-term plasticity
AU - Wu, Yue Kris
AU - Gjorgjieva, Julijana
N1 - Publisher Copyright:
© 2023 authors. Published by the American Physical Society.
PY - 2023/7
Y1 - 2023/7
N2 - This article is part of the Physical Review Research collection titled Physics of Neuroscience. Inhibition stabilization is considered a ubiquitous property of cortical networks, whereby inhibition controls network activity in the presence of strong recurrent excitation. In networks with fixed connectivity, an identifying characteristic of inhibition stabilization is that increasing (decreasing) excitatory input to the inhibitory population leads to a decrease (increase) in inhibitory firing, known as the paradoxical effect. However, population responses to stimulation are highly nonlinear, and drastic changes in synaptic strengths induced by short-term plasticity (STP) can occur on the timescale of perception. How neuronal nonlinearities and STP affect inhibition stabilization and the paradoxical effect is unclear. Using analytical calculations, we demonstrate that in networks with STP the paradoxical effect implies inhibition stabilization, but inhibition stabilization does not imply the paradoxical effect. Interestingly, networks with neuronal nonlinearities and STP can transition nonmonotonically between inhibition-stabilization and noninhibition-stabilization, and between paradoxically- and nonparadoxically-responding regimes with increasing excitatory activity. Furthermore, we generalize our results to more complex scenarios including networks with multiple interneuron subtypes and any monotonically increasing neuronal nonlinearities. In summary, our work reveals the relationship between inhibition stabilization and the paradoxical effect in the presence of neuronal nonlinearity and STP, yielding several testable predictions.
AB - This article is part of the Physical Review Research collection titled Physics of Neuroscience. Inhibition stabilization is considered a ubiquitous property of cortical networks, whereby inhibition controls network activity in the presence of strong recurrent excitation. In networks with fixed connectivity, an identifying characteristic of inhibition stabilization is that increasing (decreasing) excitatory input to the inhibitory population leads to a decrease (increase) in inhibitory firing, known as the paradoxical effect. However, population responses to stimulation are highly nonlinear, and drastic changes in synaptic strengths induced by short-term plasticity (STP) can occur on the timescale of perception. How neuronal nonlinearities and STP affect inhibition stabilization and the paradoxical effect is unclear. Using analytical calculations, we demonstrate that in networks with STP the paradoxical effect implies inhibition stabilization, but inhibition stabilization does not imply the paradoxical effect. Interestingly, networks with neuronal nonlinearities and STP can transition nonmonotonically between inhibition-stabilization and noninhibition-stabilization, and between paradoxically- and nonparadoxically-responding regimes with increasing excitatory activity. Furthermore, we generalize our results to more complex scenarios including networks with multiple interneuron subtypes and any monotonically increasing neuronal nonlinearities. In summary, our work reveals the relationship between inhibition stabilization and the paradoxical effect in the presence of neuronal nonlinearity and STP, yielding several testable predictions.
UR - http://www.scopus.com/inward/record.url?scp=85166062897&partnerID=8YFLogxK
U2 - 10.1103/PhysRevResearch.5.033023
DO - 10.1103/PhysRevResearch.5.033023
M3 - Article
AN - SCOPUS:85166062897
SN - 2643-1564
VL - 5
JO - Physical Review Research
JF - Physical Review Research
IS - 3
M1 - 033023
ER -