Towards Spike based Models of Visual Attention in the Brain

Towards Spike based Models of Visual Attention in the Brain

Terje Kristensen
DOI: 10.4018/IJARAS.2015070106
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A numerical solution of Hodgkin Huxley equations is presented to simulate the spiking behavior of a biological neuron. The solution is illustrated by building a graphical chart interface to finely tune the behavior of the neuron under different stimulations. In addition, a Multi-Agent System (MAS) has been developed to simulate the Visual Attention Network Model of the brain. Tasks are assigned to the agents according to the Attention Network Theory, developed by neuroscientists. A sequential communication model based on simple objects has been constructed, aiming to show the relations and the workflow between the different visual attention networks. Each agent is being used as an analogy to a role or function of the visual attention systems in the brain. Some experimental results based on this model have been presented in an earlier paper. The two approaches are at the moment not integrated. The long term goal is to develop an integrated parallel layered object model of the visual attention process, as a tool for simulating neuron interactions described by Hodgkin Huxley's equations or the Leaky-Integrate-and-Fire model.
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The building block of the human brain is the neuron. A neuron in the human brain may be connected to about 10,000 other neurons. The neuron may receive signals from other neurons through their dendrites and may pass signals to other neurons through their axons. These signals are short electrical pulses of similar form and define the building blocks for information transmission between any neurons in the brain. It is a common belief that their form does not carry any information. The dendrites and axons are the channels of these pulses. The junction between an axon and a dendrite is called a synapse. See Figure 1. Most synapses are chemical which means that an electrical signal from a sending neuron leads to a release of certain molecules called neurotransmitters. These molecules are caught by receptors at the receiving side of the synaptic cleft. They lead to an ion influx which again changes the voltage of the membrane of the receiving neuron. Other synapses are electrical in which specialized membrane proteins make a direct electrical connection between the two neurons.

Figure 1.

The biological neuron


The potential difference between the interior of the cell and its surroundings is called the membrane potential. Without any activity, that means no signals, the membrane potential will have a constant value of about -65 mV. After the arrival of a signal (a spike), the potential changes. If the potential change is positive, we say the synapse is excitatory. In the opposite case the synapse is inhibitory. A negative potential change will after some time approach the resting potential. With a positive potential change there are two possibilities. If no or a few more spikes are received during a short time span, the potential will decay to its resting potential. However, if enough excitatory spikes arrive within this short time span, the membrane potential will reach a critical value, known as its firing threshold.

The membrane potential then exhibits a pulse-like excursion with an amplitude of about 100 mV and a duration of 1-2 milliseconds. This membrane potential is called the action potential or simply a spike. The action potential propagates along the axon of the neuron to the synapses (Figure 2) of other neurons. After a spike has been generated the resting potential after some time returns to its resting value (Gerstner & Kistler, 2002).

Figure 2.

The structure of the synapse


Spiking Neuron Models

Engineers are interested to understand the mechanisms by which neurons interact witch each other including how this may be used to build complex neural systems. The prominent goal in the field of neural engineering is focused on increasing the knowledge about human functions via direct interactions between the nervous system and artificial devices. The main objective is to introduce a general and comprehensive overview of different spiking neuron models based on their neural action potential behavior. The Hodgkin Huxley model (H-H) is used in this paper to generate spikes (Kristensen & McNearey, 2013), but also the useful Leaky-Integrate-and-Fire model (LIF) is presented. The spiking neuron models presented here transmit information by pulses, also called action potentials or spikes. First the background knowledge necessary to understand the Hodgkin-Huxley and Leaky Integrate-and-fire models are described. Then the simulation of these neuron models will be performed in Java and graphical solutions of the action potentials is shown.

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