An in-silico framework to study the cholinergic modulation of the neocortex
Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereological techniques. The data obtained are integrated into a biologically detailed digital reconstruction of the rodent neocortex (Markram et al, 2015) in order to model the influence of modulatory systems on the activity of the somatosensory cortex neocortical column. Simulations of ascending modulation of network activity in our model predict the effects of increasing levels of neuromodulators on diverse neuron types and synapses and reveal a spectrum of activity states. Low levels of neuromodulation drive microcircuit activity into slow oscillations and network synchrony, whereas high neuromodulator concentrations govern fast oscillations and network asynchrony. The models and simulations thus provide a unifying in silico framework to study the role of neuromodulators in reconfiguring network activity.
How single neuron dynamics influence network activity and behaviour
To understand how the brain can perform complex tasks such as perception, we have to understand how information enters the brain, how it is transformed and how it is transferred. But, how do we measure information transfer in the brain? This presentation will start with a general introduction of what mutual information is and how to measure it in an experimental setup. Next, the talk will focus on how this can be used to develop brain models at different (spatial) levels, from the microscopic single neuron level to the macroscopic network and behavioural level. How can we incorporate the knowledge about single neurons, that already show complex dynamics, into network activity and link this to behaviour?