During perceptual decision-making, populations of neurons, arranged in highly interconnected microcircuits, work together to encode sensory stimuli and to transform sensory perception into appropriate behavioral choices. A fundamental gap in our knowledge about perceptual decision-making is understanding how the connectivity in cortical microcircuits shapes dynamics and information codes in populations of neurons. This gap has arisen because anatomical connectivity and activity have generally been studied separately, and because a computational framework to understand structure-function relationships in cortical microcircuits is missing. Here, we will assemble a team of researchers with complementary skills to tackle this problem. We will combine approaches to study population coding and dynamics using two-photon calcium imaging during a novel and complex decision task for mice, with measurements of connectivity in the imaged neurons using electron microscopy (EM)-based connectomics. Furthermore, we will use our activity and connectivity data to develop a data-driven model to explore structure-function relationships across cortical microcircuits. We will apply our new approach to investigate how population codes, microcircuit connectivity, and structure- function relationships differ across cortex to perform distinct computational tasks during perceptual decision- making. Although it is well established that sensory and association cortices perform different functions, little is known about the mechanisms underlying these different roles, including distinctions in microcircuit connectivity and population coding schemes. In a first aim, we will compare population codes and microcircuit connectivity for sensory stimuli and behavioral choices in visual cortex (V1; sensory cortex) and posterior parietal cortex (PPC; association cortex). We will use computational tools to examine how distinct coding schemes provide functional benefits. We will use EM connectomics in V1 and PPC for neurons imaged during a perceptual decision task to probe structure-function relationships for stimulus and choice codes. We will develop a data- driven recurrent neural network model to relate connectivity and population activity. In a second aim, we will investigate how neuronal populations transform sensory information into behavioral choices using microcircuit connectivity. We will develop a new statistical concept ? intersection information ? to identify activity patterns in V1 and PPC that carry sensory information that informs behavioral choices. Using EM connectomics, we will reconstruct the microcircuit connectivity between cells to test hypotheses about sensory-to-choice information flow. Our work will be some of the first to compare population coding and microcircuit connectivity across cortical regions and to explore structure-function relationships for perceptual decision-making.
Many neurological and neuropsychiatric disorders, including Alzheimer?s disease, Autism Spectrum Disorders, schizophrenia, and bipolar disorder, likely result from the disrupted function of neural circuits. The proposed projects will establish novel methods to understand the relationship between connectivity and function in neural circuits to reveal basic principles of neural computation in the mammalian cortex. We expect our projects will help inform how neural circuits are disrupted in a wide range of neurological and neuropsychiatric disorders.
