The Success Of Accuracy And Efficiency Of Neuromorphic Architectures

Upon reviewing the video entitled “Mirror Neurons” by Nova Science now, I gained a better understanding of the the whole mirror neuron system (MNS) and it's functions. In fact, a neuron is defined as “a specialized cell that conducts impulses through the nervous system” (Wood, Wood, and Boyd, 2014. p.41). The difference between a neuron and a mirror neuron is simple, a mirror neuron is defined as “a network of cells that the brain uses to interpret and produce motor actions and emotion-related behavior” (p.99). In my opinion mirror neurons are essential to establishing a human bond, considering they help the human brain translate or connect to one's own actions/behaviors to the actions and behaviors of another person. Mirror neurons help an individual experience empathy, which may assist in an individual's ability to relate to the world.

To send a message, a neuron will send a ripple of electrical energy down its axon. This ripple is called "action potential." The way it works is by changing the chemical makeup of the axon's negatively charged interior. Positively charged sodium ions move into the cell and negatively charged potassium ions move out, then the ions move to their original positions. This produces a wave of positively charged

Once a presynaptic neuron is passive, an electrical current is spread along the length of the axon (Schiff, 2012). This is known as action potential (Pinel, 2011). Action potential happens once an abundant amount of depolarisation reaches the limit through the entry of sodium, by means of voltage gated sodium channels

Neurons can fire efficiently or it can also work too hard or too little. These patterns can in

Neurons are nerve cells that transmit nerve signals to and from the brain at up to 200 mph. The neuron consists of a cell body (or soma) with branching dendrites(signal receivers) and a projection called

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

Neural Networks consist of three layers which are interconnected. The first layer consists of input neurons. Those neurons send the data to the second layer, which in turn sends the output neurons to the third layer.

The nervous system operates using an electrochemical process (see Video Clip: The Electrochemical Action of the Neuron). An electrical charge moves through the neuron itself and chemicals are used to transmit information between neurons. Within the neuron, when a signal is received by the dendrites, is it transmitted to the soma in the form of an electrical signal, and, if the signal is strong enough, it may then be passed on to the axon and then to the terminal buttons. If the signal reaches the terminal buttons, they are signaled to emit chemicals known as neurotransmitters, which communicate with other neurons across the spaces between the cells, known as synapses.

Impulses will travel along the neuron pathways as the electrical charges move across each neural membrane. Ions that are moving across the membrane can cause the impulse to move along the nerve cells.

Microfluidic chips allow for the manipulation, at small quantities of neuronal cells. Further, these chips allow for precise temporal and spatial control. This model is one that can be useful in the framework of neurodegeneration. Deleglise et al (2013) fabricated a 3 chamber microfluidic chip. The chambers held one of: coritical neuron soma and dendrites, cortical axons, or striatal neurons. The chip was designed to replicate an oriented neuronal connection from the coritcal to the striatal cells. Through the use of a chemically induced axotomy, by adding fluid to the central chamber (housing the cortical axon), showed that their chip could be used to simulate a lesion in the neuronal network. An emphasis was placed on the protection of synapses, and event that in axotomy studies, traumatic injury and in many neurodegenerative disorders precedes the loss of neuronal cell soma and axons. They then showed that zVAD-fmk, a caspase inhibitor and resveratrol did not show synaptic protection, while NAD+ and Y27632, a Rho Kinase inhibitor showed significant synaptic protection, despite the mechanism not being clear. Pointing at potential therapeutic targets for neurodegeneration. This can also be further looked at from the functionality of the chip - it is a useful tool in the evaluation of drugs in the axotomy model they presented.

To learn from stressful events, memories of these events must be consolidated into long-term memory in case a similar situation occurs in the future. In the normal process of consolidating a memory, three different processes take place: synaptic long-term potentiation or LTP, systems consolidation, and reconsolidation (Depperman et al., 2014). LTP is a mechanism of neuroplasticity that increases the strength of certain synaptic connections through the stimulation of neurons involved in the connection. After an event happens, the new memory is transmitted to the hippocampus. Systems consolidation is the slow process by which memories are transferred from the hippocampus to the cortex such that memories are no longer hippocampus-dependent. Reconsolidation

Synaptic plasticity refers to a process through which the brain undergoes neural changes due to alterations in synaptic strength. Many studies have demonstrated that these synapses have the ability to strengthen or weaken on account of synaptic activity. In other words, an increase in synaptic activity will further strengthen that connection, making it more sensitive to a particular stimulus. Conversely, a decrease in synaptic activity will weaken the connection such that it loses its sensitivity to a given stimulus. The neuronal events that result in the strengthening or weakening of a synapse are explained through two mechanisms – Long-Term Potentiation (LTP) and Long-Term Depression (LTD). In fact, scientists believe that the coupling of these two mechanisms essentially contributes to memory and learning of an individual.

Nerve cells generate electrical signals to transmit information. Neurons are not necessarily intrinsically great electrical conductors, however, they have evolved specialized mechanisms for propagating signals based on the flow of ions across their membranes.

In 1999 Bill Gates and Collins Hemmingway collaborated on a project that produced the book: Business @ the Speed of Thought using a Digital Nervous System. Detailing businesses and public agencies using technology that transforms information to a more responsive element, energizing three major functions of any business: business operations, knowledge management, and commerce. Gates gives general lessons to other organizations with the ability to adapt quickly to constant change using stimulated business strategies. These strategies touch on the importance of digital information passing quickly throughout a company.

Von Neumann architecture is a type of computer architecture model that acts as a store-program digital computer which uses a processing unit and a separate storage system that holds instruction and data. The processing unit is a combination of the control unit which has program counter and an instruction register and processor registers with an Arithmetic logic Unit (ALU). The memory unit is a block of shared storage registers that stores both data and instructions (Petterson & Lennessy, 2014). The memory block has a data bus and an address bus for communication with the processor. A Von Neumann system is characterized by a common bus that does both instruction fetching and operations of data. This means handling of data and instructions has to be done in sequential order which is known as Von Neumann Bottleneck, since the bus cannot operate in a full duplex manner.