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

Network models of frequency modulated sweep detection.

Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1) The 'facilitation' model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2) The 'duration tuned' model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3) The 'inhibitory sideband' model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP) to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia

A spiking network model of decision making employing rewarded STDP.

Reward-modulated spike timing dependent plasticity (STDP) combines unsupervised STDP with a reinforcement signal that modulates synaptic changes. It was proposed as a learning rule capable of solving the distal reward problem in reinforcement learning. Nonetheless, performance and limitations of this learning mechanism have yet to be tested for its ability to solve biological problems. In our work, rewarded STDP was implemented to model foraging behavior in a simulated environment. Over the course of training the network of spiking neurons developed the capability of producing highly successful decision-making. The network performance remained stable even after significant perturbations of synaptic structure. Rewarded STDP alone was insufficient to learn effective decision making due to the difficulty maintaining homeostatic equilibrium of synaptic weights and the development of local performance maxima. Our study predicts that successful learning requires stabilizing mechanisms that allow neurons to balance their input and output synapses as well as synaptic noise

Effect of synaptic noise.

<p>(A) Mean final performance of 8 runs with different levels of random perturbation of excitatory synaptic weights from middle layer to output layer. The simulations represented in green applied the perturbation only at the start. Those represented by blue applied perturbations at regular intervals. The thin red lines represent the limits of standard error. (B) 50% random variations applied to synaptic weights of the trained network. Learning was turned off and synapses were held at a fixed strength from 4,000,000 to 6,000,000 iterations. (C) Performance when reward and punishment conditions are reversed in an attempt to train avoidance behavior. Performance in “food” acquisition falls well below random (indicating successful learning) but the model failed to explicitly avoid all food.</p

Schematic excitatory and inhibitory components of an auditory neuron frequency receptive field in a network with inhibitory sidebands.

<p>Variations in two properties, ‘asymmetry’ and ‘inhibition strength’, can explain the broad range of FM sweep rate-dependent direction selectivity observed across species. See text for additional details.</p

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

<div><p>Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.</p></div

Synaptic weights dynamics during learning.

<p>(A, B, C) These plots show the strength of synaptic outputs of three different cells over the course of the experiment. Values shown are relative to the mean weight of out put synapses of the cell. Each graph shows the synapses from one middle layer cell to each of the 9 output cells. The synapses are color-coded based on which output cell they connect to. Synaptic values are truncated within the range [−0.6, +1]. (D) Schematic location of the 3 cells shown in panels A–C (left) and color-coding of output cells (right).</p