Recent shifts in the understanding of how the mind and brain retain information in working memory (WM) call for revision to traditional theories. Evidence of dynamic, “activity-silent,” short-term retention processes diverges from conventional models positing that information is always retained in WM by sustained neural activity in buffers. Such evidence comes from machine-learning methods that can decode patterns of brain activity and the simultaneous administration of transcranial magnetic stimulation (TMS) to causally manipulate brain activity in specific areas and time points. TMS can “ping” brain areas to both reactivate latent representations retained in WM and affect memory performance. On the basis of these findings, I argue for a supplement to sustained retention mechanisms. Brain-decoding methods also reveal that dynamic levels of representational codes are retained in WM, and these vary according to task context, from perceptual (sensory) codes in posterior areas to abstract, recoded representations distributed across frontoparietal regions. A dynamic-processing model of WM is advanced to account for the overall pattern of results.
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Mechanisms of distributed working memory in a large-scale network of macaque neocortex
Neural activity underlying working memory is not a local phenomenon but distributed across multiple brain regions. To elucidate the circuit mechanism of such distributed activity, we developed an anatomically constrained computational model of large-scale macaque cortex. We found that mnemonic internal states may emerge from inter-areal reverberation, even in a regime where none of the isolated areas is capable of generating self-sustained activity. The mnemonic activity pattern along the cortical hierarchy indicates a transition in space, separating areas engaged in working memory and those which do not. A host of spatially distinct attractor states is found, potentially subserving various internal processes. The model yields testable predictions, including the idea of counterstream inhibitory bias, the role of prefrontal areas in controlling distributed attractors, and the resilience of distributed activity to lesions or inactivation. This work provides a theoretical framework for identifying large-scale brain mechanisms and computational principles of distributed cognitive processes.
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- Award ID(s):
- 2015276
- NSF-PAR ID:
- 10331235
- Date Published:
- Journal Name:
- eLife
- Volume:
- 11
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Scene Perception and Visuospatial Memory Converge at the Anterior Edge of Visually Responsive Cortexmore » « less
To fluidly engage with the world, our brains must simultaneously represent both the scene in front of us and our memory of the immediate surrounding environment (i.e., local visuospatial context). How does the brain's functional architecture enable sensory and mnemonic representations to closely interface while also avoiding sensory-mnemonic interference? Here, we asked this question using first-person, head-mounted virtual reality and fMRI. Using virtual reality, human participants of both sexes learned a set of immersive, real-world visuospatial environments in which we systematically manipulated the extent of visuospatial context associated with a scene image in memory across three learning conditions, spanning from a single FOV to a city street. We used individualized, within-subject fMRI to determine which brain areas support memory of the visuospatial context associated with a scene during recall (Experiment 1) and recognition (Experiment 2). Across the whole brain, activity in three patches of cortex was modulated by the amount of known visuospatial context, each located immediately anterior to one of the three scene perception areas of high-level visual cortex. Individual subject analyses revealed that these anterior patches corresponded to three functionally defined place memory areas, which selectively respond when visually recalling personally familiar places. In addition to showing activity levels that were modulated by the amount of visuospatial context, multivariate analyses showed that these anterior areas represented the identity of the specific environment being recalled. Together, these results suggest a convergence zone for scene perception and memory of the local visuospatial context at the anterior edge of high-level visual cortex.
SIGNIFICANCE STATEMENT As we move through the world, the visual scene around us is integrated with our memory of the wider visuospatial context. Here, we sought to understand how the functional architecture of the brain enables coexisting representations of the current visual scene and memory of the surrounding environment. Using a combination of immersive virtual reality and fMRI, we show that memory of visuospatial context outside the current FOV is represented in a distinct set of brain areas immediately anterior and adjacent to the perceptually oriented scene-selective areas of high-level visual cortex. This functional architecture would allow efficient interaction between immediately adjacent mnemonic and perceptual areas while also minimizing interference between mnemonic and perceptual representations. -
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Abstract The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain’s low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.
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A new housing development in a familiar neighborhood, a wrong turn that ends up lengthening a Sunday stroll: our internal representation of the world requires constant updating, and we need to be able to associate events separated by long intervals of time to finetune future outcome. This often requires neural connections to be altered. A brain region known as the hippocampus is involved in building and maintaining a map of our environment. However, signals from other brain areas can activate silent neurons in the hippocampus when the body is in a specific location by triggering cellular events called dendritic calcium spikes. Milstein et al. explored whether dendritic calcium spikes in the hippocampus could also help the brain to update its map of the world by enabling neurons to stop being active at one location and to start responding at a new position. Experiments in mice showed that calcium spikes could change which features of the environment individual neurons respond to by strengthening or weaking connections between specific cells. Crucially, this mechanism allowed neurons to associate event sequences that unfold over a longer timescale that was more relevant to the ones encountered in day-to-day life. A computational model was then put together, and it demonstrated that dendritic calcium spikes in the hippocampus could enable the brain to make better spatial decisions in future. Indeed, these spikes are driven by inputs from brain regions involved in complex cognitive processes, potentially enabling the delayed outcomes of navigational choices to guide changes in the activity and wiring of neurons. Overall, the work by Milstein et al. advances the understanding of learning and memory in the brain and may inform the design of better systems for artificial learning.more » « less
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Introduction The notion of a single localized store of word representations has become increasingly less plausible as evidence has accumulated for the widely distributed neural representation of wordform grounded in motor, perceptual, and conceptual processes. Here, we attempt to combine machine learning methods and neurobiological frameworks to propose a computational model of brain systems potentially responsible for wordform representation. We tested the hypothesis that the functional specialization of word representation in the brain is driven partly by computational optimization. This hypothesis directly addresses the unique problem of mapping sound and articulation vs. mapping sound and meaning. Results We found that artificial neural networks trained on the mapping between sound and articulation performed poorly in recognizing the mapping between sound and meaning and vice versa. Moreover, a network trained on both tasks simultaneously could not discover the features required for efficient mapping between sound and higher-level cognitive states compared to the other two models. Furthermore, these networks developed internal representations reflecting specialized task-optimized functions without explicit training. Discussion Together, these findings demonstrate that different task-directed representations lead to more focused responses and better performance of a machine or algorithm and, hypothetically, the brain. Thus, we imply that the functional specialization of word representation mirrors a computational optimization strategy given the nature of the tasks that the human brain faces.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.72136
