Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty.

O037 Genioglossus motor control following the return to sleep after brief arousal

Rationale Arousal from sleep has been shown to elicit a prolonged increase in genioglossus muscle activity that persists following the return to sleep and may protect against airway collapse. We hypothesised that this increased genioglossal activity following return to sleep after an arousal is due to persistent firing of inspiratory single motor units (SMUs) recruited during the arousal. Methods 34 healthy participants were studied overnight while wearing a nasal mask/pneumotachograph to measure ventilation and with 4 intramuscular genioglossus SMU electrodes. During stable N2 and N3 sleep, auditory tones were played to induce brief (3-15s) AASM arousals. Ventilation and genioglossus SMUs were quantified for 5 breaths before the tone, during the arousal and for 10 breaths after the return to sleep. Results A total of 1089 tones were played and gave rise to 236 SMUs recorded across arousal and the return to sleep in 20 participants (age 23±4.2 years and BMI 22.5±2.2kg/m2). Ventilation was elevated above baseline during arousal and the first post-arousal breath (p<0.001). The peak firing frequency of expiratory and tonic SMUs was unchanged during arousal and return to sleep, whereas inspiratory modulated SMUs were increased during the arousal and for 4 breaths following the return to sleep (p<0.001). Conclusions The prolonged increase in genioglossus activity that occurs on return to sleep after arousal is a result of persistent activity of inspiratory SMUs. Strategies to elevate inspiratory genioglossus SMU activity may be beneficial in preventing/treating obstructive sleep apnea.

Persistent Activity During Working Memory From Front to Back

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major “arcs” of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two “arcs” of research.

Exploring the Inner Workings of Neuron Circuits That Exhibit Persistent Activity To Explain How Working Memory and Executive Function Are Implemented in The Brain

In this research we explore in detail how a phenomenon called sustained persistent activity is achieved by circuits of interconnected neurons. Persistent activity is a phenomenon that has been extensively studied (Papoutsi et al. 2013; Kaminski et. al. 2017; McCormick et al. 2003; Rahman, and Berger, 2011). Persistent activity consists in neuron circuits whose spiking activity remains even after the initial stimuli are removed. Persistent activity has been found in the prefrontal cortex (PFC) and has been correlated to working memory and decision making (Clayton E. Curtis and Daeyeol Lee, 2010). We go beyond the explanation of how persistent activity happens and show how arrangements of those basic circuits encode and store data and are used to perform more elaborated tasks and computations. The purpose of the model we propose here is to describe the minimum number of neurons and their interconnections required to explain persistent activity and how this phenomenon is actually a fast storage mechanism required for implementing working memory, task processing and decision making.

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

Unsupervised learning for robust working memory

Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it suffers from a fine-tuning of network connectivity, in particular, to form continuous attractors suggested for working memory encoding analog signals. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity targeting the slip of instant neural activity and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity with less sensitivity to learning parameters. However, for the maintenance of spatially structured persistent activity, differential plasticity could recover persistent activity, but its pattern can be irregular for different stimulus locations. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations, while it was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules.

Persistent Activity during Working Memory from Front to Back

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major arcs of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. A comprehensive theory of WM will require a unification of these two arcs of research.

A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity

Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. The mainstream view has long been that persistent activity is the neural basis of working memory, but recent experiments have observed that activity-silent memory can also be correctly recalled. The underlying mechanism of activity-silent memory is considered to be an alternative scheme that rejects the theory of persistent activity. We propose a working memory model based on spike-timing-dependent plasticity (STDP). Different from models based on spike-rate coding, our model adopts temporal patterns of action potentials to represent information, so it can flexibly encode new memory representation. The model can work in both persistent and silent states, i.e., it is compatible with both of these seemingly conflicting neural mechanisms. We conducted a simulation experiment, and the results are similar to the real experimental results, which suggests that our model is plausible in biology.