Long-term potentiation (LTP) is the most widely studied form of neuroplasticity and is believed by many in the field to be the substrate for learning and memory. For this reason, an understanding of the mechanisms underlying LTP is thought to be of fundamental importance to the neurosciences, but a definitive linkage of LTP to learning or memory has not been achieved. Much of the correlational ...

Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community. Among those efforts on improving the capability to represent temporal data, the Long Short-Term Memory (LSTM) has achieved great success in many areas. Although the LSTM can capture long-range dependency in the time domain, it does not exp...

Memory formation has been shown recently to be dependent on energy status in Drosophila. A well-established energy sensor is the insulin signaling (InS) pathway. Previous studies in various animal models including human have revealed the role of insulin levels in short-term memory but its role in long-term memory remains less clear. We therefore investigated genetically the spatial and temporal...

Here we study the effect of acute and chronic physical exercise in a treadmill and of daily stress (because forced exercise involves a degree of stress) during 2 or 8 weeks on different types of memory in male Wistar rats. The memory tests employed were: habituation in an open field, object recognition and spatial learning in the Morris water maze. Daily foot-shock stress enhanced habituation l...

We use model-free reinforcement learning, extensive simulation, and transfer learning to develop a continuous control algorithm that has good zero-shot performance in a real physical environment. We train a simulated agent to act optimally across a set of similar environments, each with dynamics drawn from a prior distribution. We propose that the agent is able to adjust its actions almost imme...

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) b...

HTM-MAT is a MATLAB® based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper presents an implementation of HTM-MAT with several illustrative examples in...

Since William James (1890) first distinguished primary from secondary memory, equivalent to short- and long-term memory, respectively, it has been assumed that short-term memory processes are in charge of cognition while long-term memory is being consolidated. From those days a major question has been whether short-term memory is merely a initial phase of long-term memory, or a separate phenome...

The goal of this study is to develop a psychocomputational model of human phoneme acquisition that includes the knowledge of linguistic universals [1, 2, 3] to “teach” Artificial Neural Nets incrementally. Long Short-Term Memory (LSTM) artificial neural networks are capable to outperform previous recurrent networks on many tasks ranging from grammar recognition to speech [4] and robot control [...

Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurr...