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A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. [1] ... a reverberating circuit, and a ...
[40] [41] Hebb considered "reverberating circuit" as an explanation for short-term memory. [42] The McCulloch and Pitts paper (1943) considered neural networks that contains cycles, and noted that the current activity of such networks can be affected by activity indefinitely far in the past. [43]
Hebb considered "reverberating circuit" as an explanation for short-term memory. [62] The McCulloch and Pitts paper (1943) considered neural networks that contains cycles, and noted that the current activity of such networks can be affected by activity indefinitely far in the past. [12]
The trisynaptic circuit or trisynaptic loop is a relay of synaptic transmission in the hippocampus. The trisynaptic circuit is a neural circuit in the hippocampus, which is made up of three major cell groups: granule cells in the dentate gyrus, pyramidal neurons in CA3, and pyramidal neurons in CA1. The hippocampal relay involves 3 main regions ...
The Papez circuit involves various structures of the brain. It begins and ends with the hippocampus (or the hippocampal formation). Fiber dissection indicates that the average size of the circuit is 350 millimeters. The Papez circuit goes through the following neural pathways: Hippocampal formation (subiculum) → fornix → mammillary bodies ...
Winner-take-all is a computational principle applied in computational models of neural networks by which neurons compete with each other for activation. In the classical form, only the neuron with the highest activation stays active while all other neurons shut down; however, other variations allow more than one neuron to be active, for example the soft winner take-all, by which a power ...
The circuit board is composed of 16 custom-designed chips, referred to as NeuroCores. Each NeuroCore's analog circuitry is designed to emulate neural elements for 65536 neurons, maximizing energy efficiency. The emulated neurons are connected using digital circuitry designed to maximize spiking throughput. [20] [21]
Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. [1][2] Neural networks are an important part of the connectionist approach to cognitive science. The issue of catastrophic interference ...