In the computational neuroscience Laboratory, we construct quantitative models of biological nervous systems that are firmly based on their neurophysiology, neuroanatomy and behavior, and that are developed in close interaction with experimentalists. Our main interest is neuronal function at the system level, reflecting the interaction of subsystems to generate useful behavior. Modeling is particularly important for understanding this and other system-level functions, since it requires the interaction of several pathways and neural functions. One of the functions we study is selective attention--that is, the capability of higher animals to scan sensory input for the most important information and to discard all other. Models of the neuronal basis of visual selective attention are constructed by simulating them on digital computers and comparing the results with data obtained from the visual and somatosensory systems of primates. We pay particular attention to the mechanisms involving the implementation of neural mechanisms that make use of the temporal structure of neuronal firing, rather than just the average firing rate.

The research in the Kecken Zhang Laboratory is focused on theoretical and computational neuroscience. We use mathematical analysis and computer simulations to study the nervous system at multiple levels, from realistic biophysical models to simplified neuronal networks. Several of our current research projects involve close collaborations with experimental neuroscience laboratories.

Our mission is to reveal the molecular logic of our intelligence in health and disease. We use advanced molecular biological tools and state-of-the-art neuroscience to test the role of synaptic and neuronal molecules in the dynamics of the living brain.

Artificial neural networks have been heavily inspired by the brain’s architecture, guiding our journey to discovering the keys to intelligence. We now find ourselves at a pivotal moment: today's AI systems surpass biological circuits in certain tasks, yet we still lack a fundamental understanding of the mechanisms behind the brain’s superior cognitive flexibility and efficiency. At Ingie Hong’s Quantitative Intelligence Lab, we are dedicated to unraveling the principles that enable the mammalian cortex to achieve remarkable feats of intelligence, including rapid learning, generalization, and inference across vast stores of memory.

A single neuron’s response depends on its synaptic connections and intrinsic properties, which are dictated by the expression of neuronal genes. However, the role of these molecules in brain computations remains largely uncharted territory. Focusing on the mouse visual cortex as a starting point for broader generalization, and using large-scale electrophysiology, advanced microscopy, and machine learning, we have begun to uncover the impact of key synaptic genes on cortical processing and their role in the brain’s “working algorithm” (Hong et al., Nature, 2024). Our molecular tools, including gene therapy vectors and antisense oligonucleotides, show promise as effective therapeutic candidates.

Our research will advance the nascent field of 'neurocomputational therapeutics'—innovative genetic and pharmacological tools that address biases in neural activity. These tools will not only facilitate the development of novel mechanism-based treatments for brain disorders but also inspire the next generation of intelligent artificial neural networks.