More Like this

1. Neuromodulation of Brain State and Behavior

   https://doi.org/10.1146/annurev-neuro-100219-105424  

   McCormick, David A.; Nestvogel, Dennis B.; He, Biyu J. (July 2020, Annual Review of Neuroscience)

   null (Ed.)

   Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior. 

   more » « less
2. The Illusion of State in State-Space Models

   Merrill, William; Petty, Jackson; Sabharwal, Ashish (July 2024, International Conference on Machine Learning (ICML) 2024)

   State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks (RNNs). But do SSMs truly have an advantage (over transformers) in expressive power for state tracking? Surprisingly, the answer is no. Our analysis reveals that the expressive power of SSMs is limited very similarly to transformers: SSMs cannot express computation outside the complexity class 𝖳𝖢0. In particular, this means they cannot solve simple state-tracking problems like permutation composition. It follows that SSMs are provably unable to accurately track chess moves with certain notation, evaluate code, or track entities in a long narrative. To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking. Thus, despite its recurrent formulation, the "state" in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems. 

   more » « less
3. Steady-State Convergence of Discrete Time State-Space Modeling with State-Dependent Switching

   https://doi.org/10.1109/COMPEL49091.2020.9265734  

   Baxter, Jared A.; Costinett, Daniel J. (November 2020, 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL))

   null (Ed.)

   Steady-state modeling plays an important role in the design of advanced power converters. Typically, steady-state modeling is completed by time-stepping simulators, which may be slow to converge to steady-state, or by dedicated analysis, which is time-consuming to develop across multiple topologies. Discrete time state-space modeling is a uniform approach to rapidly simulate arbitrary power converter designs. However, the approach requires modification to capture state-dependent switching, such as diode switching or current programmed modulation. This work provides a framework to identify and correct state-dependent switching within discrete time state-space modeling and shows the utility of the proposed method within the power converter design process. 

   more » « less
4. State capacity and varieties of climate policy

   https://doi.org/10.1038/s41467-024-54221-1  

   Meckling, Jonas; Benkler, Ari (December 2024, Nature Communications)
5. State of the State Tree: Historical and Modern Ecology of Kukui (Candlenut, Aleurites Moluccanus) in Hawai‘i

   https://doi.org/10.2984/74.4.9  

   Lincoln, Noa; Zhang, Qian; Chen, Qi (October 2020, Pacific Science)

   null (Ed.)