Interconnections between neurons

The so-called inferior group (B1-B4) projects mainly to brainstem nuclei, the head nuclei of some cranial nerves and the spinal cord. This means that these neurons are well placed for serving a key role in regulation of motor activity, autonomic function and nociception. In addition, there are numerous interconnections between the different... 

As stated in [14], it takes only 7 floating point operations to simulate 1 ms of a theta model as compared to 1,200 for a conductance based model. This reduced complexity leads more easily to large scale simulations. Thus, it is interesting to simulate a network of 900 PNs and 300 LNs that corresponds to the entire locust AL at scale 1, as in [6] only the scale 1/10 was simulated. The simulations performed below will allow us to confirm that results obtained with the reduced size were valid. In our model, we have considered a probability of coimection of 0.05 for the total number of 300 LNs and 900 PNs. As mentioned above, we did not consider interconnections between PNs. The parameters for the input stimulus and for the theta neurons and the synapses are given in the appendix. The simulation of the model at scale 1 takes 20 minutes only on a Pentium 4 based PC running at 2.66 GHz. Note that the simulation is three times longer when interconnections between PNs are taken into account. 

Larger architectures emerged since then, among them, the feed-forward multilayer perceptron (MLP) network has became the most popular network architecture (Hertz et al. 1991). The disposition of neurons in such ANN is quite different from the disposition in the brain they are disposed in layers with different number of neurons each. Layers are named according to their position in the architecture an MLP network has an input layer, an output layer and one or more hidden layers between them. Interconnection between neurons is accomplished by weighted connections that represent the synaptic efficacy of a biological neuron. 

The word network in the term artificial neural network refers to the interconnections between the neurons in the different layers of each system. An example system has three layers. The first layer has input neurons, which send data via synapses to the second layer of neurons, and then via more synapses to the third layer of ouqjut neurons. More complex systems will have more layers of neurons with some having increased layers of input neurons and output neurons. The synapses store parameters called weights that manipulate the data in the calculations. 

Nerve cells do not divide, and cut interconnections between nerve cells in the CNS are not able to regenerate, which leads to dysfunctions after brain or spinal cord injuries. This makes it also difficult to utilize these cells in technical devices because no cell lines can be established. This means that only primary cell cultures, that is, cells that come directly from a living animal, can be used. There are some neuron-hke cell lines in which hybridoma cells between a type of cancer cells and a neural cell are used. Cancer cells divide easily and hence cell lines can be established. However, these hybridoma cell lines do not easily form synaptic junctions. Because without synapses there... 

Neural networks are used as prediction tools. A neural network consists of simulated neurons and interconnections between them. Each connection has an associated strength, called its connection weight. The neural network has to learn the connection weights from a set of inputs with corresponding optimal outputs. It learns a pattern between input and output by modifying the connection weights between neurons. After learning, a network is used to predict the output for new input of a process. 

A neural network consists of processing imits called neurons and information flow channels between the neurons—interconnections. The way in which neurons are... 

The exceptional computational abilities of the human brain have motivated the concept of an NN. The brain can perform certain types of computation, such as perception, pattern recognition, and motor control, much faster than existing digital computers (Haykin, 2009). The operation of the human brain is complex and nonlinear and involves massive parallel computation. Its computations are performed using structural constituents called neurons and the synaptic interconnections between them (that is, a neural network), The development of artificial neural networks is an admittedly approximate attempt to mimic this biological neural network, in order to achieve some of its computational advantages. 

The so-called Hebbian learning rule (to honour Canadian neuropsychologist Donald Hebb, who proposed it in 1949) specifies how much the weight of the connection between two neurons should increase or decrease as a function of their activation. The rule states that the weight should increase whether the two interconnected neurons are active simultaneously (otherwise the weight should decrease). This rule is not used too frequently nowadays because it works well provided that all the input patterns are orthogonal or uncorrelated. 

Typically, a neural network consists of three layers of neurons, input, hidden and output layers, and of information flow channels between the neurons called interconnects (Figure 33). 

It is now well known that the artificial neural networks (ANNs) are nonlinear tools well suited to find complex relationships among large data sets [43], Basically an ANN consists of processing elements (i.e., neurons) organized in different oriented groups (i.e., layers). The arrangement of neurons and their interconnections can have an important impact on the modeling capabilities of the ANNs. Data can flow between the neurons in these layers in different ways. In feedforward networks no loops occur, whereas in recurrent networks feedback connections are found [79,80],... 

The basic circuitry of the MOB. Axons of ORNs travel in the ONL and synapse in the GL on the dendrites of mitrai ceiis (MC), tufted ceiis (externai tufted ceii, ET middie tufted ceii, MT), and generic juxtagiomeruiar (JG) neurons, which include perigiomeruiar ceiis (PG), ET ceiis, and short axon ceiis (SA). SA ceiis interconnect different giomeruii. There are serial and reciprocal synapses between the apicai dendrites of mitral/tufted cells and the processes of JG neurons. Superficial tufted cells (ST) are located in the superficial EPL or at the GL-EPL border. The lateral dendrites of mitral/tufted cells form serial and reciprocal synapses with the apical dendrites of granule cells (GC) in the EPL. GCs are located in the GCL and the MCL. The axons of mitral/tufted cells project locally to GCs (not shown) and also to primary olfactory cortex via the lateral olfactory tract (LOT). The bulb also contains other populations of interneurons neurons, including the van Gehuchten cells (VG) within the EPL... 

The FFBP distinguishes itself by the presence of one or more hidden layers, whose computation nodes are correspondingly called hidden neurons of hidden units. The function of hidden neurons is to intervene between the external input and the network output in some useful manner. By adding one or more hidden layers, the network is enabled to extract higher order statistics. In a rather loose sense, the network acquires a global perspective despite its local connectivity due to the extra set of synaptic connections and the extra dimension of NN interconnections (Hagan and Menhaj, 1994). Figure 1 depicts die structure of a FFBP neural network. 

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