Biology subjects

Guntupalli, J. S.

Publications and source records attributed to Guntupalli, J. S..

11 recordsLinked to original sources

bioRxiv2020

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

neuroscience↗

bioRxiv2020

The neural basis of intelligence in fine-grained cortical topographies

Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically-organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function, because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies, and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods couldnt resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

neuroscience↗

bioRxiv2020

The role of auxilliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data

In neuroimaging, voxel-wise encoding models are a popular tool to predict brain activity elicited by a stimulus. To evaluate the accuracy of these predictions across multiple voxels, one can choose between multiple quality metrics. However, each quality metric requires specifying auxiliary parameters such as the number and selection criteria of voxels, whose influence on model validation is unknown. In this study, we systematically vary these parameters and observe their effects on three common quality metrics of voxel-wise encoding models in two open datasets of 3- and 7-Tesla BOLD fMRI activity elicited by musical stimuli. We show that such auxiliary parameters not only exert substantial influence on model validation, but also differ in how they affect each quality metric. Finally, we give several recommendations for validating voxel-wise encoding models that may limit variability due to different numbers of voxels, voxel selection criteria, and magnetic field strengths.

neuroscience↗

bioRxiv2019

Learning cognitive maps for vicarious evaluation

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. These maps are critical for flexible behavior as they permit us to navigate vicariously, but their underlying representation learning mechanisms are still unknown. To form these abstract maps, hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization, efficient planning, and handling of uncertainty. Here we introduce a specific higher-order graph structure - clone-structured cognitive graph (CSCG) - which forms different clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a novel probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety cognitive map phenomena such as discovering spatial relations from an aliased sensory stream, transitive inference between disjoint episodes of experiences, formation of transferable structural knowledge, and shortcut-finding in novel environments. By learning different clones for different contexts, CSCGs explain the emergence of splitter cells and route-specific encoding of place cells observed in maze navigation, and event-specific graded representations observed in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for a variety of place cell remapping phenomena. By lifting the aliased observations into a hidden space, CSCGs reveal latent modularity that is then used for hierarchical abstraction and planning. Altogether, learning and inference using a CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

neuroscience↗

bioRxiv2019

Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models

Sequence learning is a vital cognitive function and has been observed in numerous brain areas. Discovering the algorithms underlying sequence learning has been a major endeavour in both neuroscience and machine learning. In earlier work we showed that by constraining the sparsity of the emission matrix of a Hidden Markov Model (HMM) in a biologically-plausible manner we are able to efficiently learn higher-order temporal dependencies and recognize contexts in noisy signals. The central basis of our model, referred to as the Cloned HMM (CHMM), is the observation that cortical neurons sharing the same receptive field properties can learn to represent unique incidences of bottom-up information within different temporal contexts. CHMMs can efficiently learn higher-order temporal dependencies, recognize long-range contexts and, unlike recurrent neural networks, are able to natively handle uncertainty. In this paper we introduce a biologically plausible CHMM learning algorithm, memorize-generalize, that can rapidly memorize sequences as they are encountered, and gradually generalize as more data is accumulated. We demonstrate that CHMMs trained with the memorize-generalize algorithm can model long-range structure in bird songs with only a slight degradation in performance compared to expectation-maximization, while still outperforming other representations.

neuroscience↗

bioRxiv2019

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Hippocampus encodes cognitive maps that support episodic memories, navigation, and planning. Under-standing the commonality among those maps as well as how those maps are structured, learned from experience, and used for inference and planning is an interesting but unsolved problem. We propose higher-order graphs as the general principle and present, as a plausible model, a cloned hidden Markov model (HMM) that can learn these graphs efficiently from experienced sequences. In our experiments, we use the cloned HMM for learning spatial and abstract representations. We show that inference and planning in the learned CHMM encapsulates many of the key properties of hippocampal cells observed in rodents and humans. Cloned HMM thus provides a new frame-work for understanding hippocampal function.

neuroscience↗

bioRxiv2018

Cortical Microcircuits from a Generative Vision Model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation. Based on a recently published generative model for visual inference (George et al., 2017), we derive a family of anatomically instantiated and functional cortical circuit models. In contrast to simplistic models of Bayesian inference, the underlying generative models representational choices are validated with real-world tasks that required efficient inference and strong generalization. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feedforward, feedback and lateral connections observed in different laminae and columns, and assigns a computational role for the path through the thalamus.

neuroscience↗

bioRxiv2018

Explaining Visual Cortex Phenomena using Recursive Cortical Network

The connectivity and information pathways of visual cortex are well studied, as are observed physiological phenomena, yet a cohesive model for explaining visual cortex processes remains an open problem. For a comprehensive understanding, we need to build models of the visual cortex that are capable of robust real-world performance, while also being able to explain psychophysical and physiological observations. To this end, we demonstrate how the Recursive Cortical Network (George et al., 2017) can be used as a computational model to reproduce and explain subjective contours, neon color spreading, occlusion vs. deletion, and the border-ownership competition phenomena observed in the visual cortex.

neuroscience↗

bioRxiv2018

Reliable individual differences in fine-grained cortical functional architecture

Fine-grained functional organization of cortex is not well-conserved across individuals. As a result, individual differences in cortical functional architecture are confounded by topographic idiosyncrasies--i.e., differences in functional-anatomical correspondence. In this study, we used hyperalignment to align information encoded in topographically variable patterns to study individual differences in fine-grained cortical functional architecture in a common representational space. We characterized the structure of individual differences using three common functional indices, and assessed the reliability of this structure across independent samples of data in a natural vision paradigm. Hyperalignment markedly improved the reliability of individual differences across all three indices by resolving topographic idiosyncrasies and accommodating information encoded in spatially fine-grained response patterns. Our results demonstrate that substantial individual differences in cortical functional architecture exist at fine spatial scales, but are inaccessible with anatomical normalization alone.

neuroscience↗

bioRxiv2017

The Neural Representation Of Personally Familiar And Unfamiliar Faces In The Distributed System For Face Perception

Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception.

neuroscience↗

bioRxiv2017

A computational model of shared fine-scale structure in the human connectome

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.\n\nAuthor SummaryResting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory -- shared fine-scale structure, a dominant but mostly unexplored component of the human connectome -- for analysis and study.

neuroscience↗