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SFB 1080/3 C05 Homeostatic stabilization of neural function in a dynamic network

Laufzeit: 01.01.2021 - 31.12.2024

Kurzfassung


The human brain is composed of approximately 1011 neurons, connected by approximately 1014 synapses. The
behavior that it generates is the outcome of the collective activity of a very large number of neurons. Therefore, it is
tempting to think that random heterogeneities in the connectivity are averaged out and do not substantially affect
behavior. Indeed, this is the case in many theoretical models of brain function. However, in the previous CRC funding
period we discovered that surprisingly,...
The human brain is composed of approximately 1011 neurons, connected by approximately 1014 synapses. The
behavior that it generates is the outcome of the collective activity of a very large number of neurons. Therefore, it is
tempting to think that random heterogeneities in the connectivity are averaged out and do not substantially affect
behavior. Indeed, this is the case in many theoretical models of brain function. However, in the previous CRC funding
period we discovered that surprisingly, this intuition is wrong and that “idiosyncratic choice bias”, a common idiosyncratic
tendency to choose one alternative over others in the absence of an identified reason, naturally emerges from these
random fluctuations in connectivity and activity. Our aim in the next funding period is to study the role of neural smallscale
heterogeneities in cortical representation and behavior. To that goal, (1) we will study the long-term dynamics of
idiosyncratic choice bias in humans and probe its homeostatic stability using feedback; (2) we will study how the targeted
ablation of several to dozens of neurons in the auditory cortex affect auditory representation and how homeostatic
mechanisms compensate for the loss of these neurons; (3) we will develop an experimental paradigm that will allow us to
mechanistically probe idiosyncratic choice bias in mice and compare it to idiosyncratic choice bias in humans. Together,
our results will lay the foundation for mechanistic studies that link the heterogeneity of individual neurons and the
dynamics of local networks to behavior.
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