1,679 research outputs found
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses
Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
Trajectory Representation Learning (TRL) is a powerful tool for
spatial-temporal data analysis and management. TRL aims to convert complicated
raw trajectories into low-dimensional representation vectors, which can be
applied to various downstream tasks, such as trajectory classification,
clustering, and similarity computation. Existing TRL works usually treat
trajectories as ordinary sequence data, while some important spatial-temporal
characteristics, such as temporal regularities and travel semantics, are not
fully exploited. To fill this gap, we propose a novel Self-supervised
trajectory representation learning framework with TemporAl Regularities and
Travel semantics, namely START. The proposed method consists of two stages. The
first stage is a Trajectory Pattern-Enhanced Graph Attention Network (TPE-GAT),
which converts the road network features and travel semantics into
representation vectors of road segments. The second stage is a Time-Aware
Trajectory Encoder (TAT-Enc), which encodes representation vectors of road
segments in the same trajectory as a trajectory representation vector,
meanwhile incorporating temporal regularities with the trajectory
representation. Moreover, we also design two self-supervised tasks, i.e.,
span-masked trajectory recovery and trajectory contrastive learning, to
introduce spatial-temporal characteristics of trajectories into the training
process of our START framework. The effectiveness of the proposed method is
verified by extensive experiments on two large-scale real-world datasets for
three downstream tasks. The experiments also demonstrate that our method can be
transferred across different cities to adapt heterogeneous trajectory datasets.Comment: 13 pages, 10 figures, Accepted by ICDE 202
Noise-induced inhibitory suppression of malfunction neural oscillators
Motivated by the aim to find new medical strategies to suppress undesirable
neural synchronization we study the control of oscillations in a system of
inhibitory coupled noisy oscillators. Using dynamical properties of inhibition,
we find regimes when the malfunction oscillations can be suppressed but the
information signal of a certain frequency can be transmitted through the
system. The mechanism of this phenomenon is a resonant interplay of noise and
the transmission signal provided by certain value of inhibitory coupling.
Analyzing a system of three or four oscillators representing neural clusters,
we show that this suppression can be effectively controlled by coupling and
noise amplitudes.Comment: 10 pages, 14 figure
PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation
Alzheimer's disease (AD) is a gradually advancing neurodegenerative disorder characterized by a concealed onset. Acetylcholinesterase (AChE) is an efficient hydrolase that catalyzes the hydrolysis of acetylcholine (ACh), which regulates the concentration of ACh at synapses and then terminates ACh-mediated neurotransmission. There are inhibitors to inhibit the activity of AChE currently, but its side effects are inevitable. In various application fields where Al have gained prominence, neural network-based models for molecular design have recently emerged and demonstrate encouraging outcomes. However, in the conditional molecular generation task, most of the current generation models need additional optimization algorithms to generate molecules with intended properties which make molecular generation inefficient. Consequently, we introduce a cognitive-conditional molecular design model, termed PED, which leverages the variational auto-encoder. Its primary function is to adeptly produce a molecular library tailored for specific properties. From this library, we can then identify molecules that inhibit AChE activity without adverse effects. These molecules serve as lead compounds, hastening AD treatment and concurrently enhancing the AI's cognitive abilities. In this study, we aim to fine-tune a VAE model pre-trained on the ZINC database using active compounds of AChE collected from Binding DB. Different from other molecular generation models, the PED can simultaneously perform both property prediction and molecule generation, consequently, it can generate molecules with intended properties without additional optimization process. Experiments of evaluation show that proposed model performs better than other methods benchmarked on the same data sets. The results indicated that the model learns a good representation of potential chemical space, it can well generate molecules with intended properties. Extensive experiments on benchmark datasets confirmed PED's efficiency and efficacy. Furthermore, we also verified the binding ability of molecules to AChE through molecular docking. The results showed that our molecular generation system for AD shows excellent cognitive capacities, the molecules within the molecular library could bind well to AChE and inhibit its activity, thus preventing the hydrolysis of ACh
A Health Monitoring System Based on Flexible Triboelectric Sensors for Intelligence Medical Internet of Things and its Applications in Virtual Reality
The Internet of Medical Things (IoMT) is a platform that combines Internet of
Things (IoT) technology with medical applications, enabling the realization of
precision medicine, intelligent healthcare, and telemedicine in the era of
digitalization and intelligence. However, the IoMT faces various challenges,
including sustainable power supply, human adaptability of sensors and the
intelligence of sensors. In this study, we designed a robust and intelligent
IoMT system through the synergistic integration of flexible wearable
triboelectric sensors and deep learning-assisted data analytics. We embedded
four triboelectric sensors into a wristband to detect and analyze limb
movements in patients suffering from Parkinson's Disease (PD). By further
integrating deep learning-assisted data analytics, we actualized an intelligent
healthcare monitoring system for the surveillance and interaction of PD
patients, which includes location/trajectory tracking, heart monitoring and
identity recognition. This innovative approach enabled us to accurately capture
and scrutinize the subtle movements and fine motor of PD patients, thus
providing insightful feedback and comprehensive assessment of the patients
conditions. This monitoring system is cost-effective, easily fabricated, highly
sensitive, and intelligent, consequently underscores the immense potential of
human body sensing technology in a Health 4.0 society
Development and characterization of a new inbred transgenic rat strain expressing DsRed monomeric fluorescent protein
The inbred rat is a suitable model for studying human disease and because of its larger size is more amenable to complex surgical manipulation than the mouse. While the rodent fulfills many of the criteria for transplantation research, an important requirement is the ability to mark and track donors cells and assess organ viability. However, tracking ability is limited by the availability of transgenic (Tg) rats that express suitable luminescent or fluorescent proteins. Red fluorescent protein cloned from Discosoma coral (DsRed) has several advantages over other fluorescent proteins, including in vivo detection in the whole animal and ex vivo visualization in organs as there is no interference with autofluorescence. We generated and characterized a novel inbred Tg Lewis rat strain expressing DsRed monomeric (DsRed mono) fluorescent protein under the control of a ubiquitously expressed ROSA26 promoter. DsRed mono Tg rats ubiquitously expressed the marker gene as detected by RT-PCR but the protein was expressed at varying levels in different organs. Conventional skin grafting experiments showed acceptance of DsRed monomeric Tg rat skin on wild-type rats for more than 30 days. Cardiac transplantation of DsRed monomeric Tg rat hearts into wild-type recipients further showed graft acceptance and long-term organ viability (>6 months). The DsRed monomeric Tg rat provides marked cells and/or organs that can be followed for long periods without immune rejection and therefore is a suitable model to investigate cell tracking and organ transplantationFil: Montanari, Sonia. University Health Network. Toronto; Canadá. University of Toronto; Canadá. Princess Margaret Cancer Centre. Toronto; CanadáFil: Wang, Xing-Hua. University Health Network. Toronto; CanadáFil: Yannarelli, Gustavo Gabriel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. University Health Network. Toronto; CanadáFil: Dayan, Victor. University Health Network. Toronto; CanadáFil: Berger, Thorsten. University Health Network. Toronto; CanadáFil: Zocche, Larissa. University Health Network. Toronto; CanadáFil: Kobayashi, Eiji. Jichi Medical School. Tochigi; JapĂłnFil: Viswanathan, Sowmya. University Health Network. Toronto; CanadáFil: Keating, Armand. University of Toronto; Canadá. University Health Network. Toronto; Canad
A passive transmitter for quantum key distribution with coherent light
Signal state preparation in quantum key distribution schemes can be realized
using either an active or a passive source. Passive sources might be valuable
in some scenarios; for instance, in those experimental setups operating at high
transmission rates, since no externally driven element is required. Typical
passive transmitters involve parametric down-conversion. More recently, it has
been shown that phase-randomized coherent pulses also allow passive generation
of decoy states and Bennett-Brassard 1984 (BB84) polarization signals, though
the combination of both setups in a single passive source is cumbersome. In
this paper, we present a complete passive transmitter that prepares decoy-state
BB84 signals using coherent light. Our method employs sum-frequency generation
together with linear optical components and classical photodetectors. In the
asymptotic limit of an infinite long experiment, the resulting secret key rate
(per pulse) is comparable to the one delivered by an active decoy-state BB84
setup with an infinite number of decoy settings.Comment: 10 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1009.383
- …