1. Language Project

2.  Neural networks have a large appeal to many researchers due to their great closeness to the structure of the brain, a characteristic not shared by more traditional systems.  In an analogy to the brain, an entity made up of interconnected neurons, neural networks are made up of interconnected processing elements called units, which respond in parallel to a set of input signals given to each. The unit is the equivalent of its brain counterpart, the neuron.

3.  A neural network consists of four main parts:  1. Processing units.  2. Weighted interconnections between the various processing units which determine how the activation of one unit leads to input for another unit.  3. An activation rule which acts on the set of input signals at a unit to produce a new output signal, or activation.  4. Optionally, a learning rule that specifies how to adjust the weights for a given input/output pair.

6.  A-> O; A | D; A | N ; A | Y; A | S ; A | R;A| e  O-> soma I F --------body of a neuron  E->dendrite E  I-> id  Y->synapse II --------- a connection  F->function P  P->( P’)  P’->Z,P’| Z  D-> dendrite I F -------- input to neuron  N -> neuron E --- a neuron composed of soma and dendrite  S->sense Z I ---- information is supplied to this node  R-> result Z id ---- results are supplied to this node  Z->number

7.  // create first neuron (Logistic and Triangle are functions) soma s1 Logistic(10, 2, 5); dendrite d1 Value(1); dendrite d2 Rand(1,2); neuron n1 s1 d1 d2 ; // create second neuron soma s2 Triangle(3); dendrite d3 Value(1); dendrite d4 Rand(1,2); neuron n2 s2 d3 d4 ;

8.  // create second neuron soma s3 Triangle(3); dendrite d5 value(4); dendrite d6 Rand(1,2); neuron n3 s3 d5 d6; // connect neurons synapse n2 d2; synapse n3 d1; // input sense 1 d1; sense 2 d3; sense 3 d4; // out result 1 n1;

9.  Our Programming language constitutes of three parts in general 1. Framework initialization 2. Topology implementation 3. Processing engine

10.  Facilitates Back Propagation- With this feature we can go back and trace the assign values of the neurons **** back propagation is used for learning  Can Determine two neurons Train two or more neural networks simultaneously- explanation

14.  Concurrency Issues ◦ Simulated currency by processing layers