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A neural network model based on pulse generation time can be established. [17] Using the exact time of pulse occurrence, a neural network can employ more information and offer better computing properties. [18] The SNN approach produces a continuous output instead of the binary output of traditional artificial neural networks (ANNs). Pulse ...
The neural system is defined by a possibly large number of neurons and their connections. In a NEST network, different neuron and synapse models can coexist. Any two neurons can have multiple connections with different properties. Thus, the connectivity can in general not be described by a weight or connectivity matrix but rather as an ...
Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence.It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural networks, large-scale simulations of neural microcircuits) and actual ...
The fact is that biological neural cognitive systems are many orders of magnitude more energy-and compute-efficient than current state-of-the-art AI and neuromorphic engineering is an attempt to narrow this gap by inspiring from the brain's mechanism just like many engineering designs have bio-inspired features.
A liquid state machine (LSM) is a type of reservoir computer that uses a spiking neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other.
In 2013, IBM developed Watson, a cognitive computer that uses neural networks and deep learning techniques. [5] The following year, it developed the 2014 TrueNorth microchip architecture [ 6 ] which is designed to be closer in structure to the human brain than the von Neumann architecture used in conventional computers. [ 1 ]
Later on, in 2019, he authored Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, exploring spiking neural networks (SNN), looking into classical theory, while also introducing brain-inspired AI (BI-AI) systems and their applications across various domains. [19]
The company's primary products are the MetaTF development environment, which allows the training and deployment of spiking neural networks (SNN), and the AKD1000 neuromorphic processor, a hardware implementation of their spiking neural network system.