The brain evolved complex recurrent networks to enable flexible behavior in a dynamic and uncertain world, but its computational strategies and underlying mechanisms remain poorly understood. We propose to uncover the network basis of neural computations in foraging, an ethologically relevant behavioral task that involves sensory integration, spatial navigation, memory, and complex decision-making. We will use large-scale electrical recordings from six relevant interconnected areas (visual cortical area V4, Area 7A, Entorhinal Cortex, Hippocampus, Parahippocampal gyrus, and Prefrontal Cortex) of freely behaving macaques. To track the neural network computations used in these ethologically relevant, natural tasks, we will exploit recent advances in both statistical data analysis and theories of neural computation. First, to characterize behavior, we will model relationships between task-relevant sensory, motor, and internal variables using graphical modeling. Animal behavior will be modeled in the framework of Partially Observable Markov Decision Processes (POMDP) and these models will provide predictions about which variables the animals use and how they interact. Second, once we have modeled the behaviorally relevant variables, we will use modern data analysis techniques to identify these variables from the patterns of neuronal responses, extracting the low- dimensional, task-relevant signals from the high-dimensional population activity. The time series of these low- dimensional neural representations will be used to analyze the transformation and flow of signals between different brain areas, using such measures as Directed Information. Finally, we will compare these neural analyses to predictions from the normative models of the foraging task. We hypothesize that neural representations of sensory and internal variables will exhibit the same causal and temporal relationships manifested in the behavioral model. By combining - for the first time - normative modeling, selective dimensionality reduction of neural population signals, and quantification of directed information flow, we will be able to identify the transformations within and between key brain areas that enact neural computations on complex natural tasks. The team project aims to produce a transformative view of distributed neural population coding, unifying ethologically crucial computations across multiple neural systems.
We propose a transformative approach that uses advanced behavioral models and theory to infer internal states during macaque ethologically relevant foraging behavior that requires animals to build and maintain an internal model of the environment, and we aim to identify the neural representation of these internal variables and their interactions across a broad network of interconnected brain areas. Our approach will shift the paradigm in neuroscience towards theory-guided massive electrical recordings from multiple brain areas during free exploration in naturalistic environments. This research has the potential to provide a groundbreaking framework for understanding complex network computations in normal and dysfunctional brain states, and hence provide alternative solutions to improve mental health.
|Pitkow, Xaq; Angelaki, Dora E (2017) Inference in the Brain: Statistics Flowing in Redundant Population Codes. Neuron 94:943-953|
|Sunkara, Adhira; DeAngelis, Gregory C; Angelaki, Dora E (2016) Joint representation of translational and rotational components of optic flow in parietal cortex. Proc Natl Acad Sci U S A 113:5077-82|