Traveling waves

How does a cat gracefully walk and suddenly freeze when spotting a mouse? In this talk, we look at how networks in the spinal cord generate movement. In particular, we address the fundamental yet poorly understood question of motor control: How can rhythmic movements, such as walking, be generated and stopped at any point in the cycle while posture is preserved? Since conventional models of spinal motor function rely on alternation between flexor and extensor modules, which are limited to just two phases, this question exposes the essential shortcoming of the conventional understanding: How ca

Prompt execution of planned motor action is context-dependent and a critical component of animal behavior. Studies from humans, non-human primates, and rodents have ubiquitously established that the primary motor cortex (M1) is the main hub for skilled motor learning, which relies on the precise coordination of multiple brain regions. Yet, how various populations of neurons separated by millimeters reliably communicate is still unclear. Here, we investigate how moment-to-moment and trial-to-trial fluctuations of population dynamics in the secondary (M2) motor cortex relate to stable population

Mounting evidence suggests that traveling waves are a key dynamical motif in biological neural information processing systems [Muller et al., 2018], yet their precise computational role remains under investigation. While the Transformer architecture [Vaswani et al., 2023] has achieved state-of-the-art performance across various data modalities, it relies heavily on an explicit `context’ – processing the entire input sequence simultaneously. This approach is both biologically implausible and computationally inefficient, resulting in quadratic complexity relative to sequence length. Recent inter

…we show that large-scale cortical “traveling waves” underlie this rapid and selective reorganization, by propagating in specific directions, thus flexibly and efficiently communicating between the given brain regions. We designed

Traveling waves (TWs) in the brain are a form of neural synchrony. They have been observed across various regions, including the visual cortex and hippocampus, during both spontaneous and task-related activities. These waves are believed to play crucial roles in cognitive processes such as sensory perception and working memory. However, most identified TWs propagate along the cortical surface, leaving it unclear whether they also travel through the depth of the cortex. We address this gap by analyzing laminar TWs of macaque visual areas V1 and V4. We analyzed neural activities recorded from mu

Spontaneous neural fluctuations and traveling waves are often observed in large scale population activity in diverse brain areas and in various brain states (Lubenov et al. 2009; Muller et al. 2016; Hamid et al. 2021). Spatiotemporal dynamics of neural activity, including traveling waves, likely implement diverse computations, including motor planning, decision making, and memory consolidation (Ermentrout & Kleinfeld 2001; Muller et al 2018). However, the detailed spatiotemporal patterns of wave propagation have not been well characterized in awake mammals. Critically, it is still largely

Cortical activity is characterized by state-specific dynamics arising from the interplay between connectivity, cellular diversity, and intrinsic properties. During non-Rapid Eye Movement (NREM) sleep, cortical population activity alternates between periods of neuronal firing (“UP” states) and neuronal silence (“DOWN” states). Patterns of neuronal activity at DOWN-to-UP (DU) transitions have functional relevance beyond sleep: they are related to sensory coding during wakefulness and support homeostatic processes and memory consolidation. Despite this functional importance, the factors that orga

Complex spatio-temporal neural population dynamics such as traveling waves are known to exist across multiple brain regions (Lubenov & Siapas, 2009; Muller et al., 2014), and have been hypothesized to play diverse roles from information transfer (Sato et al., 2012) to long-term memory consolidation (Muller et al., 2016). To-date, however, the empirical validation of these computational hypotheses has been hindered by the lack of a flexible and efficiently trainable model of such behavior. In this work, we introduce the Locally Coupled Oscillatory Recurrent Neural Network (LocoRNN), and sho

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approac

The ability to remember spatial environments is critical for everyday life. In this talk, I will discuss my lab’s findings on how the human brain supports spatial memory and navigation based on our experiments with direct brain recordings from neurosurgical patients performing virtual-reality spatial memory tasks. I will show that humans have a network of neurons that represent where we are located and trying to go. This network includes some cell types that are similar to those seen in animals, such as place and grid cells, as well as others that have not been seen before in animals, such as