Learning by neural reassociation

Golub, Matthew D.; Sadtler, Patrick T.; Oby, Emily R.; Quick, Kristin M.; Ryu, Stephen I.; Tyler-Kabara, Elizabeth C.; Batista, Aaron P.; Chase, Steven M.; Yu, Byron M.

doi:10.1038/s41593-018-0095-3

Cited by 189 publications

(253 citation statements)

References 50 publications

Supporting

Mentioning

233

Contrasting

Order By: Relevance

“…In order to assess whether downstream regions indeed use the memory subspace of LPFC we could assess how trial-to-trial fluctuations of population responses relate the LPFC subspace with fluctuations in downstream areas (Semedo et al, 2019). However, a more direct test would involve the manipulation of LPFC activity, either within or outside the subspace, while measuring changes in activity in downstream regions (Golub et al, 2018). To our knowledge, these experiments would not be feasible with stimulation technology available today.…”

Section: Discussionmentioning

confidence: 99%

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

Parthasarathy

Cheng

Herikstad

et al. 2019

Preprint

View full text Add to dashboard Cite

Endogenous processes allow the maintenance of working memories. These processes presumably involve prefrontal networks with strong recurrent connections. Distractors evoke a morphing of the population code, even when memories are stable. But it is unclear whether these dynamic population responses contain stable memory information. Here we show that dynamic prefrontal activity contains stable memory information, and the stability depends on parallel movement of trajectories associated with different memories in state space. We used an optimization algorithm to find a subspace with stable memory information. In correct trials the stability extended to periods that were not used to find the subspace, but in error trials the information and the stability were reduced. A bump attractor model was able to replicate these behaviors. The model provided predictions that could be confirmed with the neural data. We conclude that downstream regions could read memory information from a stable subspace.

show abstract

Section: Discussionmentioning

confidence: 99%

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

Parthasarathy

Cheng

Herikstad

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…To elucidate the solutions to this problem, one should look at how population dynamics change during the learning phase of a task. There exists studies that look at population level changes during motor learning (Sadtler et al (2014); Golub et al (2018); Vyas et al (2018)), but similar work for cognitive learning has been scarce (although see Durstewitz et al (2010)). In this paper we present an initial effort to characterize population level dynamics during the learning phase of a cognitive task.…”

Section: Introductionmentioning

confidence: 99%

A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

Liu

Brincat

Miller

et al. 2019

Preprint

View full text Add to dashboard Cite

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However it is not clear how these mechanisms form by trial and error learning. In this paper we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of task-relevant variables whereas HPC only transiently encodes the identity of the stimuli. We also found partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population level encoding of task variables, and suggests further directions to explore the learning-dependent changes in the population activity.

show abstract

“…Using this framework, we found that in both regions there is a large number of neurons that were dynamically synchronized with the behavior, either in their angle or in their magnitude. This suggests that this neural representation indeed captures the learning process [50] and reorganizes to adapt to the new conditions [53]. Importantly, this representation revealed a dissociation in functionality: neurons in the dACC rotated to decrease their angle-to-rule, namely changed their strategy; whereas neurons in the Putamen changed their activity to reflect both strategy and confidence, by magnitudeincrease of the neural-vector that likely reflects strengthening and reinforcement of the correct strategy once identified during learning [39].…”

Section: Resultsmentioning

confidence: 88%

A geometric representation unveils learning dynamics in primate neurons

Cohen

Schneidman

2019

Preprint

View full text Add to dashboard Cite

Primates can quickly and advantageously adopt complex rule-based behaviors. We studied acquisition of rule-based classification while recording single neurons in the dorsal-anteriorcingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo rule-learning with varying conceptual complexities every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimulus features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact universal description of neural trajectories by observing changes in either vector-magnitude and/or angle-to-rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the rule, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we additionally find a prominent magnitude increase, suggesting strengthening of confidence. Moreover, magnitude increases in the striatum followed rotation in the dACC, and finally, the neural policy at the end of the session predicted next-day behavior. Using this novel framework enables tracking of neural dynamics during learning and suggests differential roles of confidence and policy for the different brain regions.

show abstract

Learning by neural reassociation

Cited by 189 publications

References 50 publications

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

A geometric characterization of population coding in the prefrontal cortex and hippocampus during a paired-associate learning task

A geometric representation unveils learning dynamics in primate neurons

Contact Info

Product

Resources

About