The Neural Net of Absolute Strength

Diagram of the Anatomy of the Layers That Comprise the Network & Each Layer Defined.

Absolute Concept: The Neural Network of Absolute Strength

One of the concepts that is unique to Absolute has been the neural network of absolute strength and its necessary carry over to other strength behaviours that are possessed by the athlete.

As we have discussed, absolute strength is a quality of strength that all athletes should demonstrate to some level depending on their sport. The reason being is that it is a strength behaviour that permeates all other strength behaviours that must be displayed by the athlete at the level of competition. In the realm of Point B, absolute strength should always be programmed to be acquired or maintained to allow the athlete the highest opportunity to possess other aspects of Point B.

This concept is so foundational to Absolute, that it requires a deeper explanation as to its conceptual origins. To do so, it is a requirement to discuss neural networks and what they are.

What is a Neural Network?

Before going further and discussing neural networks and the neural net specifically for strength behaviours, it must be articulated that neural networks are models that have been born in the field of machine learning and artificial intelligence and are not truly accurate representations of the human system—specifically the linkage of the nervous system and the biological system. A neural network is a model of how an output can be generated from an input through the interconnectedness of the parts of the network. In this way, very large networks can output detailed phenomena by the simple connections between the parts of the network. It must be mentioned that the human system is far more complex than any neural network model to this point and functions in a far more advanced way. From the viewpoint of Absolute, the concept of a neural network and specifically:

the neural net of strength is a proxy to try to help understand the behaviours of strength and how they are propagated through the human nervous system in a more simple type of framework.

Components of a Neural Network

All neural networks are made of neurons. In AI and machine learning, the neurons often referred to as nodes are mathematical functions that allow the network to process and analyze information and data sets. This is done by weighting, biasing and activation functions that occur within each node.

The biological neural net of strength is also made of neurons, specifically those within the CNS and those existing within the peripheral muscles. The neural network of strength creates outputs based on the ever refining connection between the CNS and the periphery.

Structure of a Neural Network: The Three Layers

From a basic perspective a neural network is composed of three main components articulated as layers within the network. The layers refers to those networks that exist in artificial intelligence, however they can be used as a analogical description for the true biological neural network of strength. Much like an artificial network must take in raw data and convert it to meaningful information any biological network must do the same, as the outputs determine the representativeness of how the network interacts within its environment.

Input Layer

The first layer is what is called the Input Layer. This layer is the gateway to the network, as this is where the relevant data enters the network to eventually be processed. The input layer consists of a variety of nodes that respond to the information coming into the network. The load¹ being moved and the speed that it moves represent simple examples of data that would enter the network. Within the neural network of strength the input layer is represented by all of the sensory neurons that are stimulated while performing an act of strength. Much like an artificial network must take in raw data and deem it to be useful, the neural network of strength as governed by the CNS must do the same with all inputs that are coming into the system.

Hidden Layers

The next layer(s) of the network are called Hidden Layers. The hidden layers consist of multiple nodes of varying type that will process the input data or information coming into the network. Based on the complexity of the network there can be many different hidden layers. It would be appropriate to think of the hidden layer(s) of the network as the processing nodes, whereby the incoming data is weighted and biased towards determining the eventual output of the network. One of the important characteristics of the nodes in the hidden layers are that with the different weighting and biasing occurring through each layer it leads to the non-linearity of the network.

Within the network of absolute strength, the hidden layers are represented by the processing centers of the CNS. Without specifics to the exact areas and types of neural processing occurring, there are three main hidden layers within the CNS that are relevant to the neural net of strength. The first is the spinal cored where inputs are initially analyzed superficially. More important data is then analyzed at subsequently deeper hidden layers, the first being the subcortical regions (thalamus, basal ganglia, amygdala, hippocampus) and the deep cortical regions of the human brain.

Thinking from a general systems perspective the hidden layer(s) of the network (whether artificial or biological) act to determine the attractors of the network such that through computation the network is able to generate the best, most effective output.

Output Layer

The last layer of the network is the Output Layer. Based on the processing of information that has occurred within the hidden layer(s) the output layer produces the final result of the network. From a coarse grained perspective the motor cortex activation from the previous layer is output into the motor system, from the activation of motor pathways, motor neurons at the spinal cord level, to motor units at the muscle level, all are components of the output layer of the neural network of strength. Activation of this layer determines the effectiveness and accuracy of the response of the network.

Absolute Concept: The Neurological Network of Absolute Strength. The input layer is the sensory neurons, the hidden layer is the central nervous system and the output layer is muscle tissue.

How Does This Relate to Absolute Strength?