TY - CHAP
T1 - Artificial Neural Networks
AU - Vanneschi, Leonardo
AU - Silva, Sara
N1 - Vanneschi, L., & Silva, S. (2023). Artificial Neural Networks. In Lectures on Intelligent Systems (pp. 161-204). (Natural Computing Series). Springer, Cham. https://doi.org/10.1007/978-3-031-17922-8_7
PY - 2023/1/13
Y1 - 2023/1/13
N2 - Artificial Neural Networks (ANNs) are computational methods that belong to the field of Machine Learning [Mitchell, 1997, Kelleher et al., 2015, Gabriel, 2016]. The aim of ANNs is to implement a very simplified model of the human brain. In this way, ANNs try to learn tasks (to solve problems) mimicking the behavior of the brain. The brain is composed of a large set of specialized cells, called neurons. Each single neuron is, in itself, a very simple entity, and the power of the brain is given by the fact that neurons are numerous and strongly interconnected, by means of connections called synapses. The brain learns because neurons are able to communicate with each other. A picture of a biological neuron and its synapses is shown in Figure 7.1. Biological neurons can receive stimuli and, as a consequence, emit (electric) signals, which can stimulate other neurons. When a biological neuron emits its signal, we say that it “fires”.
AB - Artificial Neural Networks (ANNs) are computational methods that belong to the field of Machine Learning [Mitchell, 1997, Kelleher et al., 2015, Gabriel, 2016]. The aim of ANNs is to implement a very simplified model of the human brain. In this way, ANNs try to learn tasks (to solve problems) mimicking the behavior of the brain. The brain is composed of a large set of specialized cells, called neurons. Each single neuron is, in itself, a very simple entity, and the power of the brain is given by the fact that neurons are numerous and strongly interconnected, by means of connections called synapses. The brain learns because neurons are able to communicate with each other. A picture of a biological neuron and its synapses is shown in Figure 7.1. Biological neurons can receive stimuli and, as a consequence, emit (electric) signals, which can stimulate other neurons. When a biological neuron emits its signal, we say that it “fires”.
UR - http://www.scopus.com/inward/record.url?scp=85147453178&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-17922-8_7
DO - 10.1007/978-3-031-17922-8_7
M3 - Chapter
AN - SCOPUS:85147453178
SN - 978-3-031-17921-1
SN - 978-3-031-17924-2
T3 - Natural Computing Series
SP - 161
EP - 204
BT - Lectures on Intelligent Systems
PB - Springer, Cham
ER -