Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

Latimer, Kenneth W.; Freedman, David J.

doi:10.1101/2021.10.07.463576

Cited by 5 publications

(6 citation statements)

References 59 publications

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“…Based on the task relevant motifs, one could systematically design a set of tasks to learn a sufficient set of motifs rather than designing a curriculum through guesswork. Additionally, this work highlights the relevance of reporting training protocols as they likely shape the dynamical motifs that implement computation 48,49 . Beyond experimental predictions, our work provides some intuition for why we find functional specialization in the brain.…”

Section: Discussionmentioning

confidence: 96%

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Driscoll

Shenoy

Sussillo

2022

Preprint

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Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.

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Section: Discussionmentioning

confidence: 96%

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Driscoll

Shenoy

Sussillo

2022

Preprint

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“…Moreover, the final distribution of selectivity indices, and the final patterns of correlations, bear some resemblance to the initial ones (see, e.g., Figure 6 ); for this reason, we characterized activity evolution via changes in activity measures, rather than their asymptotic, post-learning values. Overall, these findings stress the importance of recording activity throughout the learning process to correctly interpret neural data ( Steinmetz et al, 2021 ; Latimer and Freedman, 2021 ).…”

Section: Discussionmentioning

confidence: 64%

Evolution of neural activity in circuits bridging sensory and abstract knowledge

Mastrogiuseppe

Hiratani

Latham

2023

eLife

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The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient-descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to recapitulate experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

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“…6); for this reason, in the analysis we characterized activity evolution via changes in activity measures, rather than their asymptotic, post-learning values. Overall, these findings stress the importance of recording activity throughout the learning process to correctly interpret neural data [54, 55].…”

Section: Discussionmentioning

confidence: 92%

Evolution of neural activity in circuits bridging sensory and abstract knowledge

Mastrogiuseppe

Hiratani

Latham

2022

Preprint

View full text Add to dashboard Cite

The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory inputs into abstract classes via gradient descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments we focus on the latter, and analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to capture experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these activity measures depends on details of the circuit and the task. These dependencies make experimentally-testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

show abstract

Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

Cited by 5 publications

References 59 publications

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs

Evolution of neural activity in circuits bridging sensory and abstract knowledge

Evolution of neural activity in circuits bridging sensory and abstract knowledge

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