Topic: cortical areas

Understanding how macroscale brain dynamics are shaped by microscale mechanisms is crucial in neuroscience. We investigate this relationship in animal models by directly manipulating cellular properties and measuring whole-brain responses using resting-state fMRI. Specifically, we explore the impact of chemogenetically neuromodulating D1 medium spiny neurons in the dorsomedial caudate putamen (CPdm) on BOLD dynamics within a striato-thalamo-cortical circuit in mice. Our findings indicate that CPdm neuromodulation alters BOLD dynamics in thalamic subregions projecting to the dorsomedial striatu

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with diffe

Many everyday decisions are based on both external cues and internal hunches. How does the brain put these together? We addressed this question in mice trained to make decisions based on sensory stimuli and on past events. While mice made these decisions, we causally probed the roles of cortical areas and recorded from thousands of neurons throughout the brain, with an emphasis on frontal cortex. The results are not what we thought based on textbook notions of how the brain works. This talk is based on work led by Nick Steinmetz, Peter Zatka-Haas, Armin Lak, and Pip Coen, in the laboratory I s

The ability to simultaneously record the activity from tens to thousands to tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures most of the

The ability to simultaneously record the activity from tens to thousands and maybe even tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures m

Background: Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods: We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics

n natural vision, active vision refers to the changes in visual input resulting from self-initiated eye movements. In this talk, I will present studies that show that the stimulus-related activity during active vision differs substantially from that occurring during classical flashed-stimuli paradigms. Our results uncover novel and efficient mechanisms that improve visual perception. In a general way, the nervous system appears to engage in sensory modulation mechanisms, precisely timed to self-initiated stimulus changes, thus coordinating neural activity across different cortical areas and se

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. We investigated how the brain coordinates its activity across areas to inform complex, top-down control behaviors. Animals were trained to perform a novel brain machine interface task to guide a visual cursor to a reward zone, using activity recorded with widefield calcium imaging. This allowed us to screen for cortical areas

Spontaneous activity in young ferrets (p22) is organised into modular, low-dimensional patterns across multiple cortical areas [1]. In the following two weeks (p22-43), these activity patterns undergo a transition similar in all areas, and show a gradual increase in dimensionality in most areas, suggesting an expansion of representational capacity [2]. However, it is unclear what the functional role of having low-dimensional activity patterns at the initial stage of development is, since one would expect the limited dimensionality to restrict representational learning. In this work, we investi

Unlike conventional deep neural networks, the human brain has a myriad of direct connections from subcortical nuclei to all cortical areas [1,2]. These enable fast information transfer and facilitate hierarchical compositionality but have not yet been explored in artificial systems [3]. In this work, we present the Shallow Hierarchical Artificial Neural Network (SHANN), a novel brain-inspired architecture with shallow connections from the input to all the hierarchical processing layers. Like conventional deep networks, SHANNs can achieve high levels of abstraction in more hierarchical layers.