Abstract
Sequential transitions between metastable states are ubiquitously observed in the neural system and underlying various cognitive functions such as perception and decision making. Although a number of studies with asymmetric Hebbian connectivity have investigated how such sequences are generated, the focused sequences are simple Markov ones. On the other hand, fine recurrent neural networks trained with supervised machine learning methods can generate complex non-Markov sequences, but these sequences are vulnerable against perturbations and such learning methods are biologically implausible. How stable and complex sequences are generated in the neural system still remains unclear. We have developed a neural network with fast and slow dynamics, which are inspired by the hierarchy of timescales on neural activities in the cortex. The slow dynamics store the history of inputs and outputs and affect the fast dynamics depending on the stored history. We show that the learning rule that requires only local information can form the network generating the complex and robust sequences in the fast dynamics. The slow dynamics work as bifurcation parameters for the fast one, wherein they stabilize the next pattern of the sequence before the current pattern is destabilized depending on the previous patterns. This co-existence period leads to the stable transition between the current and the next pattern in the non-Markov sequence. We further find that timescale balance is critical to the co-existence period. Our study provides a novel mechanism generating robust complex sequences with multiple timescales. Considering the multiple timescales are widely observed, the mechanism advances our understanding of temporal processing in the neural system.
Highlights
IntroductionActivated patterns are widely observed in neural systems, for instance, the cerebral cortex (Jones et al, 2007; Ponce-Alvarez et al, 2012; Stokes et al, 2013; Mazzucato et al, 2015; Kurikawa et al, 2018; Taghia et al, 2018), hippocampus (HPC) (Gupta et al, 2010; Maboudi et al, 2018; Schuck and Niv, 2019; Wimmer et al, 2020), and the striatum (Akhlaghpour et al, 2016)
We provide a novel framework in temporal processing in the neural system in which the slow dynamics control successive bifurcations of fixed points of fast dynamics, based on the stored history of previous patterns and inputs
Sequential transitions between metastable patterns are ubiquitously observed in the neural system (Miller, 2016) during various tasks, such as perception (Jones et al, 2007; Miller and Katz, 2010), decision making (Ponce-Alvarez et al, 2012), working memory (Stokes et al, 2013; Taghia et al, 2018),and recall of long-term memory (Wimmer et al, 2020)
Summary
Activated patterns are widely observed in neural systems, for instance, the cerebral cortex (Jones et al, 2007; Ponce-Alvarez et al, 2012; Stokes et al, 2013; Mazzucato et al, 2015; Kurikawa et al, 2018; Taghia et al, 2018), hippocampus (HPC) (Gupta et al, 2010; Maboudi et al, 2018; Schuck and Niv, 2019; Wimmer et al, 2020), and the striatum (Akhlaghpour et al, 2016) These patterns underlie a range of cognitive functions: perception (Jones et al, 2007; Miller and Katz, 2010), decision making (Ponce-Alvarez et al, 2012), working memory (Stokes et al, 2013; Taghia et al, 2018), and recall of long-term memory (Wimmer et al, 2020). In other studies (Sussillo and Abbott, 2009; Laje and Buonomano, 2013; Mante et al, 2013; Chaisangmongkon et al, 2017), recurrent neural networks (RNN) are trained by using machine learning methods so that experimentally observed neural dynamics are generated
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