Learning shapes neural geometry in the prefrontal cortex

Wójcik, Michał J.; Stroud, Jake P.; Wasmuht, Dante F.; Kusunoku, Makoto; Kadohisa, Mikiko; Myers, Norman; Hunt, Laurence T.; Duncan, John; Stokes, Mark G.

doi:10.1101/2023.04.24.538054

Cited by 10 publications

(7 citation statements)

References 56 publications

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“…If considering chosen probability decoding, high cross-condition decoding performance will only be observed if the weights associated with chosen probability representation for a given chosen flavor generalized from the alternative flavor, meaning chosen probability decoding is independent of the chosen flavor representation. Thus, high cross-condition could only be achieved if both chosen probability and chosen juice are represented within distinct neural subspaces (Bernardi et al, 2020; Wójcik et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

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Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Stoll,

Rudebeck

2024

Preprint

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Ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subregions of VFC in macaque monkeys performing a two-choice probabilistic task for different fruit juices outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral subdivision 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within 12o, by contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subregions, while the properties of the reward, such as its flavor, were more strongly represented in areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

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Section: Resultsmentioning

confidence: 99%

“…We assessed the overlap in neural activity related to chosen flavor and chosen probability using a cross-condition decoding approach (e.g., (Wójcik et al, 2023), Fig. 7B-C ).…”

Section: Methodsmentioning

confidence: 99%

Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Stoll,

Rudebeck

2024

Preprint

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“…Together, these results suggest scaling of stimulus information is a common computation across all the three tasks that can be flexibly engaged based on the monkeys' belief about the current task, dynamically adjusting the representational geometry of sensory representations [28][29][30] .…”

Section: Task Belief Scaled Representations Within the Shared Subspacesmentioning

confidence: 86%

Building compositional tasks with shared neural subspaces

Tafazoli,

Bouchacourt,

Ardalan

et al. 2024

Preprint

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Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations and computational components across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks. Yet, whether such compositionality is found in the brain is unknown. Here, we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task compositionally combining these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. Neural recordings found task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Subspaces were flexibly engaged as monkeys discovered the task in effect; their internal belief about the current task predicted the strength of representations in task-relevant subspaces. In sum, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations across tasks.

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“…However, due to mathematical properties, in high-dimensional spaces, such as the neural space formed by hundreds of neurons, symmetrical geometries are the easiest to obtain, for instance, through random embedding (Gorban et al, 2020). The focus then should be on how asymmetric geometries realizing cognitive functions (Bernardi et al, 2020; Wójcik et al, 2023), and in this work, encoding relational information in the mnemonic manifold.…”

Section: Discussionmentioning

confidence: 99%

Information is asymmetry: spatial relations were encoded by asymmetric mnemonic manifolds

Jiang,

Zhu,

Liu

2024

Preprint

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Substantial evidence suggests that working memory (WM) leverages relational representations to provide flexible support for cognitive functions, a capacity likely derived from the dynamic nature of neural codes in WM. However, how these dynamic codes represent and maintain relations remains unclear. Here, we examined the transformation of neural geometries in the dorsal prefrontal cortex of monkeys performing a visuospatial delayed-match/nonmatch task, where the monkeys were instructed to hold the spatial location of a white square in WM to match it with the spatial location of a subsequent square. We found that the sensory manifold during the square's presence and the mnemonic manifold after the square's offset both aligned with the stimulus manifold. However, significant differences emerged between the sensory and mnemonic manifolds, exhibiting little correlation in their neural geometries. Further analysis on the dynamic transformation from the sensory to mnemonic manifold revealed a process of expansion followed by flattening: the asymmetric sensory manifold first expanded into a symmetric neural geometry immediately after the square's onset offset, which then gradually flattened along dimensions different from those initially expanded, culminating in an asymmetric mnemonic manifold. This dynamic process of reconstruction not only remained its faithfulness to the stimulus geometry but also gained the flexibility to meet task demands. In sum, this transformation from asymmetry to symmetry and back to asymmetry in neural geometry precisely illustrates the dynamics of memory reconstruction, shedding lights on the subjective nature of WM that generates both accurate and illusory representation of the world we lived in.

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Learning shapes neural geometry in the prefrontal cortex

Cited by 10 publications

References 56 publications

Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Dissociable representations of decision variables within subdivisions of macaque orbitofrontal and ventrolateral frontal cortex

Building compositional tasks with shared neural subspaces

Information is asymmetry: spatial relations were encoded by asymmetric mnemonic manifolds

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