Brandmeir, N. J. | bioRxiv Science

bioRxiv2023

Single neurons in the human medial temporal lobe flexibly shift representations across spatial and memory tasks

Investigations into how individual neurons encode behavioral variables of interest have revealed specific representations in single neurons, such as place and object cells, as well as a wide range of cells with conjunctive encodings or mixed selectivity. However, as most experiments examine neural activity within individual tasks, it is currently unclear if and how neural representations change across different task contexts. Within this discussion, the medial temporal lobe is particularly salient, as it is known to be important for multiple behaviors including spatial navigation and memory, however the relationship between these functions is currently unclear. Here, to investigate how representations in single neurons vary across different task contexts in the MTL, we collected and analyzed single-neuron activity from human participants as they completed a paired-task session consisting of a passive-viewing visual working memory and a spatial navigation and memory task. Five patients contributed 22 paired-task sessions, which were spike sorted together to allow for the same putative single neurons to be compared between the different tasks. Within each task, we replicated concept-related activations in the working memory task, as well as target-location and serial-position responsive cells in the navigation task. When comparing neuronal activity between tasks, we first established that a significant number of neurons maintained the same kind of representation, responding to stimuli presentations across tasks. Further, we found cells that changed the nature of their representation across tasks, including a significant number of cells that were stimulus responsive in the working memory task that responded to serial position in the spatial task. Overall, our results support a flexible encoding of multiple, distinct aspects of different tasks by single neurons in the human MTL, whereby some individual neurons change the nature of their feature coding between task contexts.

neuroscience↗

bioRxiv2021

Face identity selectivity in the deep neural network and human brain

A central challenge in face perception research is to understand how neurons encode various face identities. However, this challenge has not been met largely due to the lack of simultaneous access to the activity of the entire face processing neural network as well as the lack of a comprehensive multifaceted model that is able to characterize a large number of facial features. In this study, we address this challenge by conducting in silico experiments using a deep neural network (DNN) capable of face recognition with a diverse array of stimuli. We identified a subset of DNN neurons selective to face identities, and these identity-selective neurons demonstrated generalized discriminability to novel faces not involved in the training and in many different styles. Visualization of the network explained the response of the DNN neurons and manipulation of the network confirmed the importance of identity-selective neurons in face recognition. Importantly, using our human single-neuron recordings, we directly compared the response of artificial neurons with 490 real human neurons to the same stimuli and found that artificial neurons did share a similar representation of facial features as human neurons. We also observed a novel region-based feature coding mechanism in DNN neurons as in human neurons, which may explain how the DNN performs face recognition. Together, by directly linking between artificial and human neurons, our results shed light on how human neurons encode face identities.

neuroscience↗