Datasets:

Meyerger

/

ASAG2024

like 1

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision being the cup falling.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Looking for the cup to fall.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Is to avoid the falling cup.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision and hearing.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The falling cup.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Getting your hand out.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is that she got her hand out most times.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

To move your hand out of the way.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

A cup is falling towards your hand.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Taking out your hand before the cup drops.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The hits and the misses on the chart.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Moving your hand before the cup hits it.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Hand eye coordination.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

I think the stimulus is the falling cup.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Aisha stimulus is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision is the stimulus of this activity.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus was vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision in the activity.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

How fast you can move your hand.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Sight.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Moving out of the way.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus Aisha would use for this activity is eye sight.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Eye sight.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus in this activity is vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is seeing.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is vision or sight.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus in this activity is to practice to move faster.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

She saw the cup.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Your stimulus is too move your hand out of the way.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is sight along with how fast you can use your hand or foot.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Vision.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is hit it since a vibration.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Well it is all of the left hand has 5 misses and there is 3 misses on the right hand.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

Dropping a cup on your hand.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

Look at the tables for Aisha. She moved each of her hands out of the way of a falling cup. She continued until she reached 5 hits or 5 misses and then stopped. What is the stimulus in this activity?

The stimulus is touch.

The stimulus is seeing or hearing the cup fall.

SciEntsBank

An artificial neural network is a massively parallel distributed processor with simple processing units that has the natural propensity to store experiential knowledge and make use of them. An artificial neural network is similar to the human brain in two ways: 1. The ANN works by the process of learning from its environment. 2. Interneuron connections called synaptic weights are used to store the knowledge gained.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network consists of: . Largely parallel distributed processor . simple processing units . that has ability to store the experential knowledge and making it available to use It resembles to human brain in two ways: . Knowledge is acquired from the environment by the network as learning process . Synaptic strengths called weights are used to store the knowledge

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a massive distributed processor. It consists of several information processing units which are able to acquire and store knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An ANN is a layered graphical model containing neurons and weighted connections, resembling the excitatory properties of the human brain. Weights of the ANN are changed after presenting it training examples from an environment, where weights are changed based on the training procedure used. Artificial neurons also are biased, just like real ones, adding a constant level of activation before being activated by a (nonlinear) activation function. Depending on the training procedure, both weights, topology or even activation functions may be learned.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial Neural Networks are large parallel processing units that have the natural ability to learn experiential knowledge. They are composed of interconnected neurons as basic units; which in turn cosists of weights, squashing functions and adder functions. ANN resembles brain in the manner that like in human brain, it is composed of a network of neurons which help in learning by adjusting the synaptic weights of the connections between neurons. This enables it to learn experiential knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An articial neural network consists of neurons. Each neuron can have several weighted inputs, an activation function and output. Usually several neurons are connected together. Often in layers. The network then calculates the output given an input to the network. The human brain works in a similar way. It also consits of neurons that are connected in several ways.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An ANN is a - massivly parallel distibuted Processor - made up of simple processing units - which have the capability of storing experiantal knowlenge - and is made up for use. An ANN resembles the brain because: 1) it gets its knowlenge through a learning process from its environment. 2) it stores its knowlenge in its interneuron connections (synaptic weights)

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

A ANN is a massively distributed processor. It has the propensity to store experiental knowledge and make it available for use. The knowledge is gained throug a process of learning. The knowledge is stored in the weights between the neurons. This structure resembles the structure of the brain. Neurons are a the basic information unit in the ANN and act similiar to real neurons.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is defined as a learning machine which is divided by layers and each layer is composed by neurons. The neurons from different layers can be connected between each other, and give an output or multiple outputs by a given input. This structure is very similar with the neurological structure of our brain, where neurons are interconnected by synapses. Also important to mention, if a feature is really important for a given task, this wil have more connections and neurons participating (like in the human brain, the important humasn functions have more synapses).

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a graph of small and identical processing units that these small units called neurons and they are connected to each other in different architectures and the whole network adapt and itself to the environment inputs by trying to decrease the error or the cost function and increase its preciseness by manipulating the free variables of the network which are the synaptic weights. It is similar to human brain because similar to the human brain we have many small processing units that are connected together and they react to the environment and learn from the environment.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is highly parralel processing. It has a mathematical model similar to human brain, which it was inspired from, as human brain does computation in an extremely parallel manner. Similarities also lay in terminology, ANN is using neurons that are smallest computing unit of a network, similarly to human brain.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

It is a massive parallel distributed processor made up of smaller processing units, that aquire knowledge through the environmnet through a learning process and makes it available for use. It resembles the brain in two ways: - Knowledge is aquired through a stimulating process in the environment - The knowledge is embedded in the synaptic links (weights) of the neurons.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

ANN is a learning machine which is composed of neurons as units of computation. The ANN learns via interacting with its environment. The ANN has built-in capacity to dynamically adapt upon input stimulus. The ANN is motivated from biological brain and resembles human brain in terms of its localized representation for the inputs. In terms of motor cortex, the sensory stimulus to diffrent body-parts activates local part of the brain, similar to ANN local representation of similar type of input.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

A neural network is a massively parallel distributed prcoessor made up for simple processing units that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects * Knowledge is acquired by the network from its environment through the learning process * Interneuron connection strengths known as synaptic weights, are used to stor the acquired knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is a massively parallel distributed processor which consists of one or more processing unit called neuron. It resembles the human brain for that it acquires knowledge from the environment through learning process, and that the acquired knowledge is stored in the synapses.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Definition: 1. Artificial neural networks are massively distributed parallel processor. 2. It is made up of small units, 3. Which has the propensity for storing the experential knowledge. 4. And making it available for use. It resembles the brain in 2 aspects. 1. Similar to the brain, artificial neural network does the process of learning from the environment. 2. It as a pair of inter neuron links known as the synaptic weights, which is used for storing the information.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is massive parallely distributed processor. It comprises of small processing units called neurons. It learns from experiencial knowledge which is then stored and can be used for making predictions. It resembles human brain in 2 ways: * It learns from experiencial knowledge * Knowledge is stored in synaptic interneuron connections.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

HERE: Artificial neural network is a massively distributed parallel processor which is composed of simple processing units called neurons, which have the natural propensity for storing experiential information and making it available for use. It resembles the human brain in the following aspects. - Knowledge is acquired by the network from its environment through a learning process. - Synaptic links are used to store the acquired knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

ANN is a learning machine which can perform complex parallel computation. It has the ability to learn through the interactions withthe environment and store the learned knowedge. It resembles the human brain in performing complex learning tasks, acquiring information, apadpting to the environment, and exploiting the acquired information.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a massively distributed parallel processor made up of simple processing units that have the natural propensity for storing experiental knowledge and making it available for future use. It resembles the brain in the following ways: 1. Artificial neural networks have the ability to acquire knowledge from the environment in which they are are embedded. 2. Inter-neuron connection strenghts called synaptic links activate each neuron during the learning process.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An Artificial Neural Network is a massively parallel distributed processor which interacts with its surrounding environment, with a propensity to store knowledge and make it available to use. It resembles the brain in two aspects: 1. It has the ability to learn from its environment 2. The knowledge is stored in synaptic weights

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is massively distributed paralled processor containing simple processing units and has natural propensity to store experiential knowledge and use it.It resembles the human brain in two aspects, it gains knowledge from the environment and adapts the synaptic weight to store the knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

It is a massively parallel distributed processor consisting of simple processing units, which can store experiential knowledge and make it available for use. it resembles the human brain in 2 ways: 1. knowledge is acquired from environment through a learning process; 2. interneuron connections are used to store the experiential knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is a massively parallel distributed processor that is made up of simple processing units called neuron. It can replicate human brain by storing information in their weights

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is a **massively parallel distributed processor** with synaptic links that can able to **store experimental knowledge** and make it available for use. It resembles human brain in two ways, * Knowledge is acquired by the neural network from its environment through learning process. * Interneuron connection called synaptic links stores the acquired knowledge.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network are the network of the units that learn data from the environment and store them using synaptic weights. The structure of the artificial neural network is similar to human brain. It has neurons, ie., the store units and the axoms called synapses which link the stored data.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is massive parallel processor made up of simple processing units called neurons. They are capable of storing experential knowledge and make it available for later use. Similarity to human brain: 1. they learn from the envirnoment 2. they store knowledge as synaptic weight in the interneuron connection

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a highly distributed processor which consists of several simple processing units. It resembles the human brain, because the processing units are neurons, which are connected with weights. The human brain also consists of neurons.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

A massively distributed processor, consisting of single processing units that have a natural prospensity of storing experimental knowledge and making it available for use.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network consists of neurons, which are small computation devices,and synapses, the connections between the neurons. This resembles the brain because it also has neurons and synapses. Also a artificial neural network has weights, which are used to store learned features from the environment. Like the brain a neural network learns from the environment. An artificial neural network also has an activation function, which creates the output.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a highly parallel computation model with learning and memory capacities. Similar to the brain it learns from the environment by strengthening the synapses between neurons. Once a task is learned it can be quickly used by reactivating those learned synapses.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a highly parallel working machine which consists of simple processing units (neurons) wich are connected to each other in layers. they are function approximators the brain is resembled in the architecure, the processing units and thge weights and how the learning process takes place and the properties of the brain: fault tolerance, parallel computing, ...

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An ANN is a massivly parrallel distributed learing machine made up of small computational units. Computational units are connected via synapses defined by a weight. It resembles the human brain in two aspectes:

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is massively parallel distributed processor made up of simple computing units called neurons which aquires knowledge from environment through learning. It resembles brainlike structure in two ways, 1. It aquires knowledge through learning and experience 2. It stores knowledge in interneuron connections called synapses.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

ANN is huge parallel distributed processor , consist of simple processing units and which has propensity of storing experintial knowlegde and making it available for use.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

Artificial neural network is a massively parrallal distributed processor made up of simple processing units which has a natural propensity to acquire knowledge from the environment and make it available for future use. It resembels the human brain in following ways. 1. Both of them acquire knowledge from the environment. 2. The neurons are connected by synapses cahrecterized by their weights which can be adjusted.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

An artificial neural network is a massively distributed parallel processor made of simple processing units. It has natural propensity to store experential knowledge and it makes the knowledge available for further use. An artificial neural network uses inter neuron connections called synaptic weights to store the knowledge acquired knowledge which is very similar to how human brain works.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

ANN is a massively distributed processor, consisting of simple processing units called neurons. These neurons in terms of ANN are similar to neurons in human brain. Both neurons are characterized by synapses(connection links). They represent connections used for data flow between neurons. In both ANN and Human brain, the knowledge is represented by its very structure and activation state of neurons.

A neural network is a massively parallel distributed processor which is made up of simple processing units. It has a natural propensity for storing experiential knowledge. Neural networks resemble the brain in two aspects; knowledge is acquired by the network from its environment through a learning process, interneuron connection strength known as synaptic weights are used to store the acquired knowledge.

DigiKlausur

A neuron is the simplest processing unit of a neural network which has: 1. synaptic weights to store the knowledge gained. 2. Adder function (linear combiner) which adds the weighted values of the input signals to produce the local field. 3. An activation function which squashes the local field to a range of values. $ \phi(\sum{i=0}^{N} wi \cdot xi) $

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

Mathematical model of a neuron is given as : y = $\phi(V)$ , where activation function is applied to local field(V) V = $\summation (w{i}x{i} + b)$ . Local field is weighted(w) sum of inputs(x) plus bias(b)

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron is an information processing unit. It consits of: inputs associated with weights, sum of inputs and an acitvation function

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

Input vector $x$ Weight matrix $w$ Net input $net=\sum x^Tw$ Net output $o=\phi(net)$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron consists of three basic components: - *Synaptic Weights*: The synaptic weights are connections between neurons and are adjusted through training. - *Squashing/Activation Functions*: The squashing functions may be non linear or linear functions that that are applied to the signals from the neurons - *Adder Functions*: The adder functions help in combining outputs from several neurons.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

$N$ number inputs, $xi$ input i, $vj$ local field, $\varphi(vj)$ activiation function, $yj$ output, $w{ji}$ weight from node i to j $yj = \varphi(vj)$ $vj = \sum{i=0}^{N}w{ji}xi$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron is a simple processing unit of an ANN, that is made up of - the synaptik links which are defines by a weights $(w1,...,wn)$ - a adder function that combines the weighted input $(wi*xi)$ plus some bias $(b)$ to the local field $(\sum{wi*wi}) +b=v$ - a activation function phi that squaches the local field to the output $(phi(v)=y)$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

The neuron consists of synapses/connecting link each characterised by a weight. A linear combiner sums up the weighted sum of inputs to a local field. The local field is then passed through an activation function. The result of the activation function is the output.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron is defined by the following elements: - A number of input values x - A number of weights w - A bias b - An activation function $/phi$. The inputs x are multiplied with the weights, and the result is summed with the bias (also, the bias can be used just as a weight value b and a single connetion with an stable input equal to 1, for mathematical simplicity). The resulting value, known as local field (v), will be the input to the activation function. The mathematical model can be summarized in the formula: $v = \sum^{n}{i = 0} x(i)*w(i) + b$ $y = \phi(v)$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron consists of a set of inputs and a bias which these inputs and predefined bias will be multiplied by a weight and then we have sum the results of all the inputs and bias multiplied by the weights which called induced field and after that we send this to an activation function which can be a linear or non-linear function and the output of this function is the final output of our neuron.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

Neuron is a simplest computation unit of a neural network that consists of input variables, weights, bias, summation term (combiner), activation function and output variables.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

The neuron is the basic processing unit of a neural network and is made of three main component: - Weights: $w1, w2, ...,wn$ - Adder function: it is the linear combination of the input and weights plus bias. (induced local field) $v = \sum wi xi + b$ - Squashing function: it is the activation function applied to the local field used to limit the output of the neuron. $\phi(v)$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron is a computational unit composed of + synapses which are stored in the form of weights $w$. These are the variables that can are dynamical. + summing function that computes the weighted sum of inputs: $v = \sumi (wixi)$ + activation function $\phi$: gives nonlinear nature to network, determines and normalizes the output produced by neuron. e.g. sigmoid function + bias: another synaptic tunable variable with input 1. Therefore the net output of neuron: $ y = \sumi (wixi) +b$.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

The following equations describe a nonlinear model of a neuron, labeled k. 1)uk = sum from j=1 to m w{kj} x{j} 2)yk = phi(u{k} + b{k}) where x{j} are the input signals; w{kj} are the weights of the neurons; u{k} is the linear combiner output due to the input signals; b{k} is the bias; phi() is the activation function; and yk is the output signal of te neuron.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A neuron is a processing unit that contains three main components: a set of synaptic weights that connect the neuron with other neurons; an adder that computes the induced local field, or the weighted sum of the signals flowing through the neuron; an activation function that constrains the magnitude of the output signal from the neuron.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A Mathematical model of a neurons consits of a 1. A set of synaptic links which are classified based on weights(w1, w2, w3...wn) 2. It consits of a adder function, which performs the weighted sum of the inputs and the bias. $\Sigma{i=1....n} wn.x + b$ 3. It consists of an activation function, used to minimize the amplitude of the neuron output. $\Phi(\Sigma{i=1....n} wn.x + b)$

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A mathematical model of neuron comprises of 2 main units: * Adder functions: it sums up all the product of all synaptic connections and inputs of neuron * Synaptic weights: these are interneuron connections in which the knowledge is stored * Activation function: it is used for introducing non-linearity

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

HERE: The neuronal model consists of the following: - Synaptic links characterized by their weights which connects the network to the environment it is embedded in. - An adder function which sums up the weighted inputs and outputs the induced local field of the neuron. - An activation function which takes the induced local field of the neuron as it's input and limits the output of the neuron.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

A mathematical model of neuron consists of 3 important parts. A neuron is the smallest computaional node with: 1) Input vectors : set of vectors of a certan dimension to train the model 2) Weights (and biases): each of the input vectors are weighted using weight vectors in accordance withthe output that is required. Bias is added when necessary. 3) Activation function : The linear combination of weights and inputs are passed through the activation function which produces an output.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur

The neuron is the fundamental processing unit of an aritificial neural network that is characterised by the followig features: 1. A neuron has a set of non-linear synaptic links, an externally applied bias, and possibly one or more linear activation links. The bias is represented by a synaptic link from an input fixed at +1. 2. The synaptic links of the neuron weight the respective inputs. 3. An adder function (linear combiner) computes the weighted sum of the inputs to the neurons. 4. An activation function (squashing function) limits the amplitude of the neuron's output.

Mathematical model of a neuron consists of a set of synapses or connecting links where each link is characterized by a weight, an adder function (linear combiner), which computes the weighted sum (local field) of the inputs plus some bias and an activation function (squashing function) for limiting the amplitude of a neuron’s output.

DigiKlausur