Winter&Spring 2023

20^th January 2023

Claudia Clopath

1:00 PM online: https://bristol-ac-uk.zoom.us/j/94138286231?pwd=MlRURE1SWjR6OTZCR1Fnak9QbGxhUT09

Meeting ID: 941 3828 6231 Passcode: 277162

Theory of neural perturbome

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of n euronal networks in future studies.

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13^th January 2023

Kyle Wedgewood 

1:00 PM BIOMED BLDG C44 and online: https://bristol-ac-uk.zoom.us/j/94138286231?pwd=MlRURE1SWjR6OTZCR1Fnak9QbGxhUT09

Meeting ID: 941 3828 6231 Passcode: 277162

Closed-Loop Interrogation of the Dynamics of Neuroendocrine Cells

This talk will discuss how mathematical modelling can be embedded within experiment protocols to study electrical behaviour in neurons and neuroendocrine cells in which delays play an important role. We discuss three examples, the first of which explores the capability of a neuron that is synaptically coupled to itself, to store and repeat patterns of precisely timed spikes, which we regard as single cell ‘memories’. Drawing on analogies from semiconductor lasers, we append a delayed self-coupling term to the oft studied Morris-Lecar model of neuronal excitability and use bifurcation analysis to predict the number and type of memories the neuron can store. These results highlight the delay period as an important period parameter controlling the storage capacity of the cell. We then use the dynamic clamp protocol to introduce self-coupling to a mammalian cell and confirm the existence of the spiking patterns predicted by the model analysis. The second example covers preliminary work of investigating the origin of pulsatile secretion in corticotrophs in the pituitary gland. Such pulsatility has previously been conjectured to be strongly coupled to the delay period between secretion from the corticotrophs and feedback from the adrenal glands. Here, we combine Ca2+ imaging, mathematical modelling and dynamic perfusion to explore how delays influence behaviour of this combined system. The final example will explore how techniques combining control theory and bifurcation analysis with dynamic clamp can be used to probe single cell electrical excitability.

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