I completed building the experimental rig within the first month to carry out the experiments with NCI to establish mutual inhibition circuit between two real-life neurons. I also organized ordering animals (mouse). Then, I started to carry out the experiments. I succeeded to evoke bi-stable activity in the pair of neurons when electric currents were injected to both neurons.
WP1
I investigated how the dynamics of the bi-stability are influenced by the activity levels of neurons. I found the pair of neurons exhibits dynamics strikingly similar to the known properties of bi-stable visual perception. When the current injection to one of the two neurons was increased, the neuron showed an increase of its dominance and an increase of dominance durations. When the currents to both neurons increased, the reversal rate of the bi-stability increased. However, the change of reversal rate varied: some pairs showed a decrease of reversal rate when the difference between the currents injected in the two neurons was increased, corresponding to the response of bi-stable perception to the change of strength of the input signals. However, the other pairs did not show the decrease. The mixed results suggest that there may be multiple factors involved in determining the final outcome such as properties of neural adaptation, firing patterns, and normalization mechanisms of input signals. We have published the data in a preprint archive (Kogo, N. et al., bioRxiv (2020), 2020.05.26.113324). The paper is ready to be submitted to eLife.
WP2
I investigated the effect of noise to bi-stable activity. Modelled synaptic noise was implemented in NCI system and was introduced to the neurons while they show bi-stability. First, the level of the noise was systematically changed which showed the increase of reversal rate. Second, I applied antagonists of neurotransmitter receptors to block intrinsic noise (“without noise” condition) and then added the modelled noise (“with noise” condition) and recorded bi-stable activity for 10 minutes under the two conditions. While in “without noise” condition the histogram of dominance durations showed Gaussian-like symmetric distributions, it showed Gamma-like skewed distributions that is typically found in dominance durations of bi-stable perception. The data collection is mostly complete and I have started writing a paper based on this result. The data have also been presented at conferences (Perception Day, 2018, International Congress on Cognitive Neurodynamics 2019, Society for Neuroscience, 2019, Donders-NIN meeting, 2019).
WP3
Because of the delay of the progress due to the regulations during covid-19 pandemic, I decided not to start this work package during the period of the fellowship and decided to invest the time for the work package 4 because it requires less expansion of the equipment.
WP4
“Stabilization effect” is observed when an image that evokes bi-stable perception is presented intermittently interleaved with blank periods: a percept that was dominant in the previous cycle of presentation tends to reappear at the next cycle. This striking effect is related to memory mechanisms of neural system. Inspired by research on working memory, I applied acetylcholine receptor agonist, carbachol (CCh), that evokes persistent activity of a neuron. With the presence of CCh, currents were injected to both neurons intermittently to mimic the intermittent presentation. The responses varied a pair showed the stabilization effect only in some cases. When two neurons complete for dominance, the one that quickly reach to generation of action potential (due to the shorter time constant of its membrane) tends to be dominant at first. This is similar to “onset bias” in bi-stable perception where a certain perception tends to appear first irrelevant to the bias of the dominance during the whole presentation, called “sustained bias”. I now investigate the correlation between the stabilization and the onset and sustained bias of the bi-stable activity.
