Uncovering biophysical and functional mechanisms of information processing in neural systems

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The prodigious capacity of our brain to process information relies on efficient information-processing mechanisms. Understanding these mechanisms enables the development of bio-inspired intelligent systems and facilitates the development of implantable devices that can help people with neurological disorders to experience a healthy life again. Inferring these mechanisms from recorded brain signals is extremely challenging because the neural systems' activities are complex, nonlinear, and multi-scale. Furthermore, the recorded brain signals are noisy, sparse, and incomplete. We refer to these obstacles toward understanding the mechanisms of neuronal information processing as the lack of observability in neural systems. Relying solely on experimental recordings of the brain is not sufficient to overcome these obstacles - mathematical models and engineering methods are required. My research program is devoted to creating mathematical models and developing engineering algorithms that can link the models with experimental recordings. In this way, we aim to conquer this lack of observability in neural systems, understand neuronal information processing, and enhance the controllability of the brain's functions. Nearly all efforts to uncover neuronal information processing have focused on understanding how neurons respond to external stimuli. However, it is well-known from experimental studies that the communication between neurons is NOT static. Neurons communicate with each other through synapses (the structure that permits a neuron to transmit information to another neuron). The strength of connectivity between neurons varies with the ever-changing rate of activities in the pre- and post-synaptic neurons. The dynamic interactions between neurons are referred to as synaptic plasticity. Inferring mechanisms of neuronal information processing becomes more challenging if an additional set of parameters reflecting synaptic plasticity is to be taken into account. However, neurostimulation devices - by delivering electrical pulses to sets of neurons and affecting synaptic plasticity - presents a way to examine the modulated neural activities in a controlled fashion. My general approach is to benefit from neurostimulation to stimulate a neural system, create computational models to predict the underlying neural activities, and use experimental recordings provided by my collaborators to observe those activities. Moreover, I will develop engineering algorithms to infer the model parameters by minimizing the error between the observed and the predicted neural activities. My long-term goal is to uncover the mechanisms of information processing in neural systems by examining how neurostimulation impacts synaptic plasticity and modulates neuronal responses. This research program will provide a framework to gain a mechanistic understanding of neural systems' information processing, which in turn will advance our ability to control the brain's functions.