Modelling sensory map plasticity in barrel cortex

An important feature of the brain is its ability to retain plasticity not only during development, but also later in life in response to experience. This experience dependent plasticity (EPD) modulates connections between neurons and is also present during sensory map reorganization. In barrel cortex, whisker deprivation (plucking a whisker) diminishes the input to its corresponding barrel, after which reorganization of the sensory maps takes place. This causes the representation of the deprived whisker to shrink, while the representation of surrounding whiskers expands. Koen Kole investigated how such sensory experience affects supragranular cross-columnar synaptic communication. Linda Wouters showed with a computational model which connectivity changes could explain these experimental results. Tousif Jamal is currently analyzing these data. Tea Tompos is investigating how sparsity and wieight scaling affect network dynamics differentially

Biophysically realistc models from whisker to cortex

We continually change the way we perceive the world by actively changing the position and physical properties of our sensory organs, for instance by moving and focussing our eyes to pursue a moving object, yet we perceive objects in the world as stable entities. How the brain processes this changing sensory information, what computations it performs to create unvarying percepts and how the interaction between perception and active control of our sensory organs (sensorimotor integration) arises, is still largely unknown. I research these computations: How do neurons extract information about the world and represent it? How does the information sensory neurons extract influence active control over our sensory organs and vice versa?

Perception and sensorimotor integration are often studied in the rodent whisker system, for several reasons. Firstly, rodents use active sensorimotor integration to solve real-world problems like navigation, object localization and texture discrimination. Secondly, whisker motion can easily be tracked in millisecond resolution and manipulated experimentally, giving direct access to the sensory information that animals collect and to the motor plan they generate during touch sensing. Finally, the availability of molecular tools in the mouse makes it a good candidate to address neural circuit mechanisms. Even though the rodent whisker system has been experimentally well studied and lends itself to a mechanistic explanation of neural computation, there are not many computational models of it. Moreover, the models that have been proposed only describe certain modules in the whisker system, such as biomechanical whisker models, mechanoreceptor models, cortical models or behavioural models.

Chao Huang and me developed an end-to-end model to allow the integration of and cross-validation between structural and functional aspects of sensory phenomena. The model is based on anatomical reconstructions and includes known electrophysiological cell properties. The connectivity is probabilistic, so every time the model is run, a new realisation of the connectivity is established. The newest version can be found in the GitHub link below. Matteo Cantarelli of MetaCell LLC Ltd, and Salvador Dura-Bernal and Eugenio Urdapilleta of the Dura-Bernal Lab, SUNY Downstate have helped us to convert the model to NetPyNE.

Tea Tompos made a comparable model with a smaller number of neurons, but more complex neuron models. Nicolas Rault is developed models of brain stem and thalamus. These two were coupled to the Whiskit model of a whisker, to create an end-to-end model from whisker to cortex.