A funded PhD position (Univ. Assistant) in the areas Spiking Neural Networks/Machine Learning/Brain Models/Neuromorphic Hardware.

Modeling brain mechanisms is often confined to a given scale, such as single-cell models, network models or whole-brain models, and it is often difficult to relate these models. Here, we show an approach to build models across scales, starting from the level of circuits to the whole brain. The key is the design of accurate population models derived from biophysical models of networks of excitatory and inhibitory neurons, using mean-field techniques. Such population models can be later integrated as units in large-scale networks defining entire brain areas or the whole brain. We illustrate this approach by the simulation of asynchronous and slow-wave states, from circuits to the whole brain. At the mesoscale (millimeters), these models account for travelling activity waves in cortex, and at the macroscale (centimeters), the models reproduce the synchrony of slow waves and their responsiveness to external stimuli. This approach can also be used to evaluate the impact of sub-cellular parameters, such as receptor types or membrane conductances, on the emergent behavior at the whole-brain level. This is illustrated with simulations of the effect of anesthetics. The program codes are open source and run in open-access platforms (such as EBRAINS).

I am interested in developing a neurally mechanistic understanding of how primate brains represent the world through its visual system and how such representations enable a remarkable set of intelligent behaviors. In this talk, I will primarily highlight aspects of my current research that focuses on dissecting the brain circuits that support core object recognition behavior (primates’ ability to categorize objects within hundreds of milliseconds) in non-human primates. On the one hand, my work empirically examines how well computational models of the primate ventral visual pathways embed knowledge of the visual brain function (e.g., Bashivan*, Kar*, DiCarlo, Science, 2019). On the other hand, my work has led to various functional and architectural insights that help improve such brain models. For instance, we have exposed the necessity of recurrent computations in primate core object recognition (Kar et al., Nature Neuroscience, 2019), one that is strikingly missing from most feedforward artificial neural network models. Specifically, we have observed that the primate ventral stream requires fast recurrent processing via ventrolateral PFC for robust core object recognition (Kar and DiCarlo, Neuron, 2021). In addition, I have been currently developing various chemogenetic strategies to causally target specific bidirectional neural circuits in the macaque brain during multiple object recognition tasks to further probe their relevance during this behavior. I plan to transform these data and insights into tangible progress in neuroscience via my collaboration with various computational groups and building improved brain models of object recognition. I hope to end the talk with a brief glimpse of some of my planned future work!

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?