Topic: single-neuron recordings

Neural circuits adapt their computations to perform cognitive functions within and across tasks. These adaptations are vital for decision making, which often requires the integration of past information and present context to generate appropriate behaviors. Neurons in association cortices exhibit selective activity for task-relevant signals such as current and past choices and outcomes, yet how such selectivity is modulated by the task context is unclear. Interestingly, neuronal activity encodes time or epoch within the task as strongly as conventional task variables. Here we examined the intr

Hierarchical working memory refers to the human ability to group information on-the-fly into larger chunks, thereby overcoming the limited capacity of Working Memory (WM), as first suggested by George A. Miller in his 1956 paper. Although chunking has been explored experimentally in numerous studies since then, its neuronal underpinnings remain unknown to this day. In our work, we address this issue by proposing a novel Recurrent Neural Network (RNN) model that enables the spontaneous organization of memory into chunks within WM. Our model leverages specialized “chunking neurons” and long-term

The electrophysiological mechanisms of human memory encoding and recall are incompletely understood. Precise interaction of single-neuron activity and brain oscillations presumably play a critical role in this process. Here, we used human single-neuron recordings from an object-location memory task with separate periods for encoding and retrieval to analyze the timing of single-neuron activity in the medial temporal lobe relative to the local theta rhythm. 18 epilepsy patients participated in the task and contributed to 27 experimental sessions. We extracted single-neuron action potentials fro