Dose-Dependent Effects of Closed-Loop tACS Delivered During Slow-Wave Oscillations on Memory Consolidation

Sleep is critically important to consolidate information learned throughout the day. Slow-wave sleep (SWS) serves to consolidate declarative memories, a process previously modulated with open-loop non-invasive electrical stimulation, though not always effectively. These failures to replicate could be explained by the fact that stimulation has only been performed in open-loop, as opposed to closed-loop where phase and frequency of the endogenous slow-wave oscillations (SWOs) are matched for optimal timing. The current study investigated the effects of closed-loop transcranial Alternating Current Stimulation (tACS) targeting SWOs during sleep on memory consolidation. 21 participants took part in a three-night, counterbalanced, randomized, single-blind, within-subjects study, investigating performance changes (correct rate and F1 score) on images in a target detection task over 24 h. During sleep, 1.5 mA closed-loop tACS was delivered in phase over electrodes at F3 and F4 and 180° out of phase over electrodes at bilateral mastoids at the frequency (range 0.5–1.2 Hz) and phase of ongoing SWOs for a duration of 5 cycles in each discrete event throughout the night. Data were analyzed in a repeated measures ANOVA framework, and results show that verum stimulation improved post-sleep performance specifically on generalized versions of images used in training at both morning and afternoon tests compared to sham, suggesting the facilitation of schematization of information, but not of rote, veridical recall. We also found a surprising inverted U-shaped dose effect of sleep tACS, which is interpreted in terms of tACS-induced faciliatory and subsequent refractory dynamics of SWO power in scalp EEG. This is the first study showing a selective modulation of long-term memory generalization using a novel closed-loop tACS approach, which holds great potential for both healthy and neuropsychiatric populations

An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks

Cellular level learning is vital to almost all brain function, and extensive homeostatic plasticity is required to maintain brain functionality. While much has been learned about cellular level plasticity in vivo, how these mechanisms affect higher level functionality is not readily apparent. The cellular level circuitry of most networks that process information is unknown. A variety of models were developed to better understand plasticity in both learning and homeostasis. Spike time dependent plasticity (STDP) and reward-modulated plasticity may be the primary methods through which neurons record information. We implemented rewarded STDP to model foraging behavior in a virtual environment. When appropriate homeostatic mechanisms were in place, the network of spiking neurons developed the capability of producing highly successful decision-making. The networks used in the foraging model used a very simple initial configuration to avoid assumptions about network organization. More realistic network configurations can help to show how plasticity interacts with genetically determined network. We developed three network models of synaptic mechanisms of FM sweep processing based on published experimental data. One of these, the 'inhibitory sideband' model, used frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections resulted in neurons responsive to sweeps in a single direction and of sufficient rate. STDP was shown to be capable of causing to become selective for sweeps in the same direction as a repeatedly presented training sweep. The experience dependent plasticity, occurs primarily during the waking state, however, sleep is essential for learning. Slow wave sleep activity may be essential for memory consolidation and homeostasis. We developed a model of slow wave sleep that included methods to calculate the electrical field in the space around the network. We show here that a network model of up and down states displays this CSD profile only if a frequency-filtering extracellular medium is assumed. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the it's termination remains poorly understood. We explored the impact of intrinsic and synaptic inhibition on the state transition. We found that synaptic inhibition controls the duration and the synchrony of active state termination. Together these models set the stage for a model network that can learn through input driven processes in a waking state then explore the consolidation of memory in a sleeping state. This will allow us to explore in greater detail how plasticity on the level of a single cell contributes to learning and stability on the level of the whole brain