Dario L. Ringach - US grants

Our objective in this research is to reveal the neural circuitry generating novel response properties in the primate primary visual cortex (area V1). The experiments are designed to uncover the role cortical dynamics, suppressive signals, receptive field non-linearities, and functional intra- cortical connectivity play in the generation of neural response selectivity to visual stimulus attributes: orientation, direction of motion, spatial frequency, length, and width. Dynamics of Tuning: We will measure the dynamics of tuning selectivity in the different V1 layers using reverse correlation techniques. A recent improvement to this method (the inclusion of "blank" images) provides the means to determine the dynamics of excitation and inhibition, and to establish their contribution to the cell's response. Using this methodology, we will characterize the dynamics of tuning to orientation, direction of motion, spatial frequency, length, and width. The hypothesis that sharp tuning selectivity is present from the very beginning of the response will be tested. Role of Suppression: We will test the hypothesis that suppressive signals are involved in shaping the tuning selectivity of neural responses. To facilitate the detection of suppressive signals in extracellular recordings, we will measure tuning curves on top of a conditioning stimulus (two-dimensional noise) whose purpose is to elevate the activity of the cortex above the usually low spontaneous rates. We will seek evidence of inhibition: regions in the tuning curve where responses are depressed below the baseline activity. Spatio-temporal Linearity: We will test the hypothesis that simple cells behave as a linear spatio-temporal filter followed by a static non-Linearity. We will test the hypothesis that simple method that avoids the problems associated with the contribution of the output non-Linearity to the response. A goal of this experiment is to determine if quasi-linear mechanisms are sufficient to account for the tuning properties of simple cells. Functional connectivity: We will begin to explore the pattern of cortical interactions in the dimensions of space, orientation and spatial frequency. To achieve this goal, we will record simultaneously from pairs of neurons when their responses are elevated by the use of a conditioning stimulus. These experiments are designed to study, from multiple conceptual angles, the neural mechanisms generating cortical selectivity.. Taken together, these findings will advance our understanding of the computations and strategies used by V1 to process thalamic input.

Proposal No: 0745685
PI: Dario L. Ringach

Award Abstract:

This award supports the preparation and sharing of computational neuroscience data as part of an exploratory activity aimed at catalyzing rapid and innovative advances in computational neuroscience and related fields. The data to be shared in this project are single- and multi-unit recordings from primary visual cortex, obtained using either standard microelectrodes or micro-machined electrode arrays. Both spontaneous and stimulus driven activity are available in a number of different conditions, including standard receptive field characterizations (e.g., orientation tuning, spatial and temporal frequency tuning) and more specific experiments such as sub-space receptive field mapping and natural image sequences. Data from micro-machined electrode arrays also include local field potentials and surface EEG. It is anticipated that these data will be useful for studies of visual processing, population coding, and retinotopy, and that the large-scale high-dimensional data will be well suited for exploration by novel machine learning and statistical methods.

DESCRIPTION (provided by applicant): A hallmark of primary visual cortex is its organization into maps of visual space, orientation and ocular dominance. Despite remarkable advances in our ability to measure the structure of cortical maps and their mutual relationships, many important questions remain unanswered. How do these maps develop? Why are maps missing in some species? What role do maps play, if any, in cortical computation? The central goal of our research is to seek answers to these fundamental questions of cortical development, organization and function that have eluded us for decades. Our working hypothesis is that the blueprint for the formation of simple-cell receptive fields and orientation maps in primary visual cortex is encoded in the spatial layout of retinal ganglion cell (RGC) mosaics. To test the idea we will analyze the spatial statistics of RGC mosaics, reconstruct the retinal input to given orientation domains, and test the micro-organization of cortical maps. If the these ideas are confirmed, they can offer a definitive account for the origin of orientation maps in primary visua cortex and, more broadly, profoundly influence the way we conceive of cortical maps, their development and function.

Abstract The overarching goal of this proposal is to learn how large groups of neurons interact in a network to perform computations that go beyond the individual ability of each cell. Our working hypothesis is that emergent behavior in neural networks results from their organization into a hierarchy of modular sub-networks or motifs, each performing simpler computations than the network as a whole. This theoretical framework suggests that our understanding of neural networks will advance if we can reliably measure network connectivity, detect recurring motifs, elucidate the computations they perform, and reveal how these smaller modules are combined into larger networks capable of performing increasingly complex computations. To advance the field forward we will: (a) develop novel system identification methods for cortical networks based on dynamical, two- photon imaging data. Our methods will use a Bayesian formulation that incorporates prior constraints on network topology, sparsity of synaptic connections, and cell type, derived from published, experimental data; (b) advance graph theoretic methods to identify patterns of connectivity among subsets of neurons which appear at rates higher than chance; (c) will use extensive in-vivo and in-vitro methods to validate our techniques. The work will deliver transformative software tools for Bayesian inference of network connectivity from functional data; it will yield a catalog of elementary cortical motifs of excitatory and inhibitory cells that will shed light on the wiring of the cortical circuitry; and it will generate the first database combining functional calcium imaging data with ?ground truth? estimates of direct synaptic connectivity. Altogether, the proposed work will make available much needed analytical tools and databases to support a wide range of studies under the BRAIN initiative.

Project Summary / Abstract How do cortical populations represent sensory input and support perceptual decision making? It has long been known that the responses of individual neurons to the repeated presentation of a stimulus are highly variable. Nonetheless, the pattern of activity across a population encodes enough information to support precise perceptual decisions. This implies that hidden in the distribution of population responses there are invariant features, yet to be identified, which robustly encode the sensory stimulus from one trial to the next. Here we propose to mathematically model the conditional distribution of the population response given a stimulus and to uncover the invariant features that support the reliable discrimination of sensory stimuli. Surprisingly, preliminary data reveal that the distribution of population responses to a fixed stimulus is star- shaped ? in any one trial, the population vector can point in one among a finite set of directions. The directions are highly invariant across trials, while the amplitude of the responses is variable. Based on these observations we hypothesize that cortical coding is a one-to-many correspondence. This idea represents a major departure from the prevailing view of cortical coding as a one-to-one map between a stimulus and a population direction. We propose to study star-shaped distributions and their role in the encoding of sensory information with the following three Aims: (a) measure population responses to the repeated presentation of a stimulus and test the hypothesis the structure is well-described by star-shaped distributions, (b) test a mathematical model linking the direction of population responses evoked in indiviudal trials to behavioral choice in a discrimination task, (c) establish if star-shaped distributions are generated in the cortex or inherited from thalamic input. The proposed studies are significant because they challenge the dominant view that cortical coding implements a one-to-one map. We introduce an innovative framework to study and understand the structure of cortical variability that generalizes prior approaches, yielding predictive models that link population activity to behavior. Altogether, the proposed studies will significantly advance our understanding of cortical coding and function.