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Previous work has shown that humans distribute their visual working memory (VWM) resources flexibly across items: the higher the importance of an item, the better it is remembered. A related, but much less studied question is whether people also have control over the total amount of VWM resource allocated to a task. Here, we approach this question by testing whether increasing monetary incentives results in better overall VWM performance. In three experiments, subjects performed a delayed-estimation task on the Amazon Turk platform. In the first two experiments, four groups of subjects received a bonus payment based on their performance, with the maximum bonus ranging from$0 to $ 10 between groups. We found no effect of the amount of bonus on intrinsic motivation or on VWM performance in either experiment. In the third experiment, reward was manipulated on a trial-by-trial basis using a within-subjects design. Again, no evidence was found that VWM performance depended on the magnitude of potential reward. These results suggest that encoding quality in visual working memory is insensitive to monetary reward, which has implications for resource-rational theories of VWM.

To better understand the functional roles of hippocampal forward and reverse replays, we trained rats in a spatial sequence memory task and examined how these replays are modulated by reward and navigation history. We found that reward enhances both forward and reverse replays during the awake state, but in different ways. Reward enhances the rate of reverse replays, but it increases the fidelity of forward replays for recently traveled as well as other alternative trajectories heading toward a rewarding location. This suggests roles for forward and reverse replays in reinforcing representations for all potential rewarding trajectories. We also found more faithful reactivation of upcoming than already rewarded trajectories in forward replays. This suggests a role for forward replays in preferentially reinforcing representations for high-value trajectories. We propose that hippocampal forward and reverse replays might contribute to constructing a map of potential navigation trajectories and their associated values (a “value map”) via distinct mechanisms.

The electroencephalography (EEG)-based approach provides a promising low-cost and non-invasive approach to the early detection of pathological cognitive decline. However, current studies predominantly utilize EEGs from resting state (rsEEG) or task-state (task EEG), posing challenges to classification performances due to the unconstrainted nature of mind wandering during resting state or the inherent inter-participant variability from task execution. To address these limitations, this study proposes a novel feature extraction framework, working memory task-induced EEG response (WM-TIER), which adjusts task EEG features by rsEEG features and leverages the often-overlooked inter-state changes of EEGs. We recorded EEGs from 21 AD individuals, 24 MCI individuals, and 27 healthy controls (HC) during both resting and working memory task conditions. We then compared the classification performance of WM-TIER to the conventional rsEEG or task EEG framework. For each framework, three feature types were examined: relative power, spectral coherence, and filter-bank phase lag index (FB-PLI). Our results indicated that FB-PLI-based WM-TIER features provide (1) better AD/MCI versus HC classification accuracy than rsEEG and task EEG frameworks and (2) high accuracy for three-class classification of AD vs. MCI vs. HC. These findings suggest that the EEG-based rest-to-task state transition can be an effective neural marker for the early detection of pathological cognitive decline.

Reservoir computing based on cellular automata (ReCA) constructs a novel bridge between automata computational theory and recurrent neural networks. ReCA has been trained to solve 5-bit memory tasks. Several methods are proposed to implement the reservoir where the distributed representation of cellular automata (CA) in recurrent architecture could solve the 5-bit tasks with minimum complexity and minimum number of training examples. CA distributed representation in recurrent architecture outperforms the local representation in recurrent architecture (stack reservoir), then echo state networks and feed-forward architecture using local or distributed representation. Extracted features from the reservoir, using the natural diffusion of CA states in the reservoir offers the state-of-the-art results in terms of feature vector length and the required training examples. Another extension is obtained by combining the reservoir CA states using XOR, Binary or Gray operator to produce a single feature vector to reduce the feature space. This method gives promising results, however using the natural diffusion of CA states still outperform. ReCA can be considered to operate around the lower bound of complexity; due to using the elementary CA in the reservoir.

Hippocampal sharp wave ripples (SPW-Rs) have been hypothesized as a mechanism for memory consolidation and action planning. The duration of ripples shows a skewed distribution with a minority of long-duration events. We discovered that long-duration ripples are increased in situations demanding memory in rats. Prolongation of spontaneously occurring ripples by optogenetic stimulation, but not randomly induced ripples, increased memory during maze learning. The neuronal content of randomly induced ripples was similar to short-duration spontaneous ripples and contained little spatial information. The spike content of the optogenetically prolonged ripples was biased by the ongoing, naturally initiated neuronal sequences. Prolonged ripples recruited new neurons that represented either arm of the maze. Long-duration hippocampal SPW-Rs replaying large parts of planned routes are critical for memory.

Cognitive decline remains an unaddressed problem for the elderly. We show that myelination is highly active in young mice and greatly inhibited in aged mice, coinciding with spatial memory deficits. Inhibiting myelination by deletion of Olig2 in oligodendrocyte precursor cells impairs spatial memory in young mice, while enhancing myelination by deleting the muscarinic acetylcholine receptor 1 in oligodendrocyte precursor cells, or promoting oligodendroglial differentiation and myelination via clemastine treatment, rescues spatial memory decline during aging.Wang et al. show that myelination is greatly inhibited in aged brains. Enhancing myelination by ablation of M1R in OPCs or clemastine treatment promotes oligodendroglial differentiation and consequently rescues spatial memory decline during aging.

Neural oscillations are evident across cortex but their spatial structure is not well- explored. Are oscillations stationary or do they form “traveling waves”, i.e., spatially organized patterns whose peaks and troughs move sequentially across cortex? Here, we show that oscillations in the prefrontal cortex (PFC) organized as traveling waves in the theta (4-8Hz), alpha (8-12Hz) and beta (12-30Hz) bands. Some traveling waves were planar but most rotated. The waves were modulated during performance of a working memory task. During baseline conditions, waves flowed bidirectionally along a specific axis of orientation. Waves in different frequency bands could travel in different directions. During task performance, there was an increase in waves in one direction over the other, especially in the beta band.

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, in-memory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory. Here, we show that a cross-point array of resistive memory devices can directly solve a system of linear equations, or find the matrix eigenvectors. These operations are completed in just one single step, thanks to the physical computing with Ohm’s and Kirchhoff’s laws, and thanks to the negative feedback connection in the cross-point circuit. Algebraic problems are demonstrated in hardware and applied to classical computing tasks, such as ranking webpages and solving the Schrödinger equation in one step.

Alzheimer's disease (AD) is an intractable progressive neurodegenerative disease characterized by cognitive decline and dementia. An inflammatory neurodegenerative pathway, involving Caspase-1 activation, is associated with human age-dependent cognitive impairment and several classical AD brain pathologies. Here, we show that the nontoxic and blood–brain barrier permeable small molecule Caspase-1 inhibitor VX-765 dose-dependently reverses episodic and spatial memory impairment, and hyperactivity in the J20 mouse model of AD. Cessation of VX-765 results in the reappearance of memory deficits in the mice after 1 month and recommencement of treatment re-establishes normal cognition. VX-765 prevents progressive amyloid beta peptide deposition, reverses brain inflammation, and normalizes synaptophysin protein levels in mouse hippocampus. Consistent with these findings, Caspase-1 null J20 mice are protected from episodic and spatial memory deficits, neuroinflammation and Aβ accumulation. These results provide in vivo proof of concept for Caspase-1 inhibition against AD cognitive deficits and pathologies. Caspase-1, activated by stress in immune cells and in CNS human neurons, may contribute to neuronal degeneration. Here, the authors investigate the therapeutic potential of a Caspase-1 inhibitor in a mouse model of Alzheimer’s disease.

During our daily life, we depend on memories of past experiences to plan future behaviour. These memories are represented by the activity of specific neuronal groups or ‘engrams’ 1 , 2 . Neuronal engrams are assembled during learning by synaptic modification, and engram reactivation represents the memorized experience 1 . Engrams of conscious memories are initially stored in the hippocampus for several days and then transferred to cortical areas 2 . In the dentate gyrus of the hippocampus, granule cells transform rich inputs from the entorhinal cortex into a sparse output, which is forwarded to the highly interconnected pyramidal cell network in hippocampal area CA3 3 . This process is thought to support pattern separation 4 (but see refs. 5 , 6 ). CA3 pyramidal neurons project to CA1, the hippocampal output region. Consistent with the idea of transient memory storage in the hippocampus, engrams in CA1 and CA2 do not stabilize over time 7 – 10 . Nevertheless, reactivation of engrams in the dentate gyrus can induce recall of artificial memories even after weeks 2 . Reconciliation of this apparent paradox will require recordings from dentate gyrus granule cells throughout learning, which has so far not been performed for more than a single day 6 , 11 , 12 . Here, we use chronic two-photon calcium imaging in head-fixed mice performing a multiple-day spatial memory task in a virtual environment to record neuronal activity in all major hippocampal subfields. Whereas pyramidal neurons in CA1–CA3 show precise and highly context-specific, but continuously changing, representations of the learned spatial sceneries in our behavioural paradigm, granule cells in the dentate gyrus have a spatial code that is stable over many days, with low place- or context-specificity. Our results suggest that synaptic weights along the hippocampal trisynaptic loop are constantly reassigned to support the formation of dynamic representations in downstream hippocampal areas based on a stable code provided by the dentate gyrus. Imaging of hippocampal neuron activity in mice performing a memory task across several days identifies both stable and dynamic memory engrams.