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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Neuronal Communication01:28

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
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Postsynaptic Potential (PSP)01:32

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Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Neuromorphic Sentiment Analysis Using Spiking Neural Networks.

Raghavendra K Chunduri1, Darshika G Perera1

  • 1Department of Electrical and Computer Engineering, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, USA.

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|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces a novel Spiking Neural Network (SNN) model for sentiment analysis on SpiNNaker neuromorphic hardware, achieving 100% accuracy with low energy consumption. This brain-inspired approach enhances natural language processing for resource-constrained applications.

Keywords:
SpiNNakerartificial neural networknatural language processingneuromorphic computingsentiment analysisspiking neural networks

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Area of Science:

  • Neuromorphic Computing
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Deep neural networks (DNNs) are computationally intensive and power-hungry, limiting their use in resource-constrained environments.
  • Spiking neural networks (SNNs) offer a power-efficient alternative, mimicking brain functionality for applications like robotics and drones.
  • Natural Language Processing (NLP) techniques are also inefficient on traditional hardware.

Purpose of the Study:

  • To enhance NLP capabilities by integrating SNNs and deploying them on neuromorphic hardware.
  • To develop a novel, efficient sentiment analysis model using SNNs on SpiNNaker hardware.
  • To address the computational complexity and power consumption challenges in AI.

Main Methods:

  • Developed a Spiking Sentiment Analysis (SSA) model by converting a pre-trained DNN.
  • Utilized the SpiNNaker neuromorphic platform for real-time SNN simulation.
  • Trained the initial DNN model on the Internet Movie Database (IMDB) dataset.

Main Results:

  • The SSA-SpiNNaker model achieved 100% accuracy in sentiment analysis.
  • The model demonstrated high energy efficiency, consuming only 3970 Joules for processing ~10,000 words.
  • Outperformed traditional DNN models in performance due to SpiNNaker's parallel processing.

Conclusions:

  • The proposed SSA-SpiNNaker model offers a unique synergy between SNNs and NLP on neuromorphic hardware.
  • This brain-inspired approach significantly reduces power consumption and computational complexity.
  • The model has potential applications in various resource-constrained and low-power systems, advancing AI and brain-inspired computing.