Neuron layers

The architecture of a backpropagation neuronal network is comparatively simple. The network consists of different neurone layers. The layer connected to the network input is called input layer while the layer at the network output is called the output layer. The different layers between input and output are named hidden layers. The number of neurones in the layers is determined by the developer of the network. Networks used for classification have commonly as much input neurones as there are features and as much output neurones as there are classes to be separated. 

Figure 13.9 Architecture of a fully connected feed-forward network. Formal neurons are drawn as circles, and weights are represented by lines connecting the neuron layers. Fan-out neurons are drawn in white, sigmoidal neurons in black.

Fig. 5. a1B-AR expression in the mouse medulla. The a]B-AR is expressed in the various neuronal layers of the mouse medulla. Original magnification xlO. 

The GCL is the deepest neuronal layer in the bulb, and it contains the largest number of cells. Most of the neurons of the GCL are the GCs, but there are also small numbers of Golgi cells, Cajal cells, and Blanes cells. As discussed earlier, the GCs are inhibitory GABAergic cells that form dendrodendritic synapses with mitral/tufted cells in the EPL. 

PC has two principal layers of pyramidal neurons, layers II and III, corresponding to superficial and deep pyramidal cells (Fig. 26). A third morphologically distinct subtype, the middle pyramidal cell has also been suggested, but is supported by only limited evidence (Martinez et al. 1987). Pyramidal neurons have several characteristic features and are similar to those in other cortical regions and the hippocampus (1) A primary apical dendritic trunk that extends radially towards the pial surface and arborizes into numerous smaller branches that ramify in layer la and Ib (Haberly, 1983 Martinez et al. 1987). Some of these branches turn and run parallel to the pial surface for short distances. (2) A large number of relatively thin secondary or basal dendrites that emerge from the soma and extend several hundred microns into deeper parts of PC. Both the apical and basal dendritic tree are heavily invested with spines and varicosities. (3) A myelinated axon that typically extends deep to the soma terminates on other local pyramidal cells and interneurons (see below). ... 

The learning stage the number of neurons, layers, and type of architecture, transfer function, and algorithm are established, after which the network is allowed to achieve the desired outputs linked to an input. 

Fig. 2. Appearance of hippocampal slices from postnatal d 10 rat pups immediately after slicing and after 21 DIV. Transverse slices were immediately prepared (0 DIV) or cultured (21 DIV), Slices were fixed and stained with cresyl violet. The neuronal layers of the dentate gyrus, CAl, CA3, the subiculum, and the entorhinal cortex are evident immediately after slicing. After 21 DIV, CA3, CAl and the dentate gyrus are well preserved whereas the entorhinal cortex and subiculum have degenerated. Neuronal layers in the 2IDIV culture are wider because of the thinning of the slice in vitro. Superior preservation of the slice is indicated by a similar density of staining between the CA3 and CAl pyramidal cell layers or between the upper and lower limbs of the dentate gyrus.

A neural network is a system of interconnected processing elements called neurones or nodes. Each node has a number of inputs and one output, which is a function of the inputs. There are three types of neurone layers input, hidden, and output layers. Two layers communicate via a weight connection network. The nodes are connected together in complex systems, enabling comprehensive processing capabilities. The archetype neural network is of course the human brain, but there is no further resemblance between the brain and the mathematical algorithms of neural networks used today. 

---