State-dependencies of learning across brain scales

Abstract

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.

Making Sense of Real-World Scenes

“…One approach to address this problem is to use large numbers of naturalistic images and model multiple dimensions simultaneously. For example, a comprehensive analysis [125; Figure 3B] revealed that the dimensions of spatial frequency, subjective distance and object category all explained variance in scene-selective regions. However, most of the variance explained was shared across the models suggesting that, for example, the apparent sensitivity to scene category could just as easily be interpreted as reflecting differences in spatial frequency.…”

“…Note that scenes may differ from one another at multiple levels; for example, the beach scene can be distinguished from the park and living room based on virtually all dimensions, whereas the park and living room image share some but not all properties. Due to the inherent correlations between scene features, assessing their individual contributions to scene representations is challenging [125]. …”

“…As a result, most of the variance in response magnitude in scene-selective areas is shared across models: Venn diagram colors indicate variance explained by each model and their combinations, and grey shows shared variance across all three models. Adapted, with permission, from [125]. …”

Temporally correlated fluctuations drive epileptiform dynamics

“…Recent studies of OU processes driving neural models have investigated the effects of coloured noise on temporal distributions of neuronal spiking (Braun et al, 2015, da Silva and Vilela, 2015) and the generation of multimodal patterns of alpha activity (Freyer et al, 2011). In addition, networks of spiking neurons (Sancristóbal et al, 2013) and of neuronal populations (Jedynak et al, 2015) have been shown to generate realistic $1/f^b\mathrm{-}\mathrm{like}$ spectra when driven by OU noise, or more complex dynamics when subjected to driving at specific frequencies (Spiegler et al, 2011, Malagarriga et al, 2015). However, we lack an understanding of the ways in which non-white noise or rhythmic perturbations interact with neuronal populations to produce epileptiform dynamics.…”

The structure function as new integral measure of spatial and temporal properties of multichannel EEG

“…It should be noted that the various entropy methods that are very popular in EEG time series analysis [1, 6, 7] like Sample Entropy (SampEn) [8], Lempel–Ziv complexity [9], auto-mutual information, Shannon’s entropy, and other approximate entropies are most similar according to Fulcher et al [5] to the approximate entropy algorithm (ApEn) proposed by Pincus [10]. Unfortunately, current entropy measures are mostly unable to quantify the complexity of any underlying structure in the series, as well as determine if the variation arises from a random process [11].…”