Topic: 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

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

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

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