Browsing by Author "Angelaki, Dora E."

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    Dynamical latent state computation in the male macaque posterior parietal cortex
    (Springer Nature, 2023) Lakshminarasimhan, Kaushik J.; Avila, Eric; Pitkow, Xaq; Angelaki, Dora E.
    Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
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    How Can Single Sensory Neurons Predict Behavior?
    (Elsevier, 2015) Pitkow, Xaq; Liu, Sheng; Angelaki, Dora E.; DeAngelis, Gregory C.; Pouget, Alex
    Singleﾠsensory neuronsﾠcan be surprisingly predictive of behavior in discrimination tasks. We propose this isﾠpossible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or by suboptimal decoding that is blind to correlations. These have different consequences for choice correlations, the correlations between neural responses and behavioral choices. In theﾠvestibularﾠandﾠcerebellar nucleiﾠand the dorsalﾠmedial superior temporal area, we found that choice correlations during heading discrimination are consistent with near-optimal decoding ofﾠneuronal responses corrupted by information-limiting correlations. In the ventral intraparietal area, the choice correlations are also consistent with the presence of information-limiting correlations, but this area does not appear to influence behavior, although the choice correlations are particularly large. These findings demonstrate how choice correlations can be used to assess the efficiency of the downstream readout and detect the presence of information-limiting correlations.
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    Inductive biases of neural network modularity in spatial navigation
    (AAAS, 2024) Zhang, Ruiyi; Pitkow, Xaq; Angelaki, Dora E.
    The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent’s behavior also resembles macaques’ behavior more closely. Our results shed light on the possible rationale for the brain’s modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.