Adaptive stretching of representations across brain regions and deep learning model layers

Zhang, Xin-Ya; Bobadilla-Suarez, Sebastian; Luo, Xiaoliang; Lemonari, Marilena; Brincat, Scott L.; Siegel, Markus; Miller, Earl K.; Love, Bradley C.

doi:10.1101/2023.12.01.569615

Cited by 2 publications

References 35 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Decoding region-level visual functions from invasive EEG data

Zhang,

Lin,

Deng

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Decoding vision is an ambitious task as it aims to transform scalar brain activity into dynamic images with refined shapes, colors and movements. In familiar environments, the brain may trigger activity that resembles specific pattern, thereby facilitating decoding. Can an artificial neural network (ANN) decipher such latent patterns? Here, we explore this question using invasive electroencephalography data from monkeys. By decoding multi-region brain activity, ANN effectively captures individual regions’ functional roles as a consequence of minimizing visual errors. For example, ANN recognizes that regions V4 and LIP are involved in visual color and shape processing while MT predominantly handles visual motion, aligning with regional visual functions evident in the brain. ANN likely reconstructs vision by seizing hidden spike patterns, representing stimuli distinctly in a two-dimensional plane. Furthermore, during the encoding process of transforming visual stimuli into neuronal activity, optimal performance is achieved in regions closely associated with vision processing.

show abstract

Decoding region-level visual functions from invasive EEG data

Zhang,

Lin,

Deng

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Dissociable Roles of the Dorsolateral and Ventromedial Prefrontal Cortex in Human Categorization

Broschard,

Turner,

Tranel

et al. 2024

J. Neurosci.

View full text Add to dashboard Cite

Models of human categorization predict the prefrontal cortex (PFC) serves a central role in category learning. The dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) have been implicated in categorization; however, it is unclear whether both are critical for categorization and whether they support unique functions. We administered three categorization tasks to patients with PFC lesions (mean age = 69.6 years; 5 men, 5 women) to examine how prefrontal subregions contribute to categorization. These included a rule-based (RB) task that was solved via a unidimensional rule, an information integration (II) task that was solved by combining information from two stimulus dimensions, and a deterministic/probabilistic (DP) task with stimulus features that had varying amounts of category-predictive information. Compared to healthy comparison participants, both patient groups had impaired performance. Impairments in the dlPFC patients were largest during the RB task, whereas impairments in the vmPFC patients were largest during the DP task. A hierarchical model was fit to the participants’ data to assess learning deficits in the patient groups. PFC damage was correlated with a regularization term that limited updates to attention after each trial. Our results suggest that the PFC, as a whole, is important for learning to orient attention to relevant stimulus information. The dlPFC may be especially important for rule-based learning, whereas the vmPFC may be important for focusing attention on deterministic (highly diagnostic) features and ignoring less predictive features. These results support overarching functions of the dlPFC in executive functioning and the vmPFC in value-based decision making.Significance StatementCategory learning creates flexible memory representations that easily generalize to novel situations. Although it is generally established that the prefrontal cortex is central to categorization, it is unclear how different prefrontal subregions contribute to learning. Separate literatures have implicated both the dorsolateral prefrontal cortex (dlPFC) and the ventromedial prefrontal cortex (vmPFC) in categorization, but there has been little effort to bridge these literatures. The current study is the first to examine categorization in patients with lesions centered in the dlPFC and vmPFC. We found that, as a whole, the PFC orients attention to relevant stimulus information. The dlPFC is important for rule-based learning, whereas the vmPFC is important for focusing attention on highly diagnostic features and ignoring less predictive features.

show abstract

Adaptive stretching of representations across brain regions and deep learning model layers

Cited by 2 publications

References 35 publications

Decoding region-level visual functions from invasive EEG data

Decoding region-level visual functions from invasive EEG data

Dissociable Roles of the Dorsolateral and Ventromedial Prefrontal Cortex in Human Categorization

Contact Info

Product

Resources

About