Pyramidal Neuron as Two-Layer Neural Network

Pyramidal Neuron as Two-Layer Neural Network

The pyramidal neuron is the principal cell type in the mammalian forebrain, but its function remains poorly understood. Using a detailed compartmental model of a hippocampal CA1 pyramidal cell, we recorded responses to complex stimuli consisting of dozens of high-frequency activated synapses distributed throughout the apical dendrites. We found the cell's firing rate could be predicted by a sim...

A new feedforward neural network hidden layer neuron pruning algorithm

This paper deals with a new approach to detect the structure (i.e. determination of the number of hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear inputoutput model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation fun...

hopf bifurcation analysis of a delayed five-neuron bam neural network with two neurons in the x-layer

in this paper, a bidirectional associative memory (bam) neural network, which consists of two neurons in the x-layer and three neurons in the y-layer, with two time delays will be studied. we conclude that under some assumptions, hopf bifurcation occurs when the sum of two delays passes through a critical value. a numerical example is presented to support our theoretical results.

A novel two-layer neural network classifier

Generalization in a two-layer neural network.

Learning of a fully connected two-layer neural networks with N input nodes, M hidden nodes and a single output node is studied using the annealed approximation. We study the generalization curve, i.e. the average generalization error as a function of the number of the examples. When the number of examples is the order of N, the generalization error is rapidly decreasing and the system is in a p...