VOLUME 4, FEBRUARY 2011
Neural Networks of Dendritic Networks?
By : Khalid Isa
The above question was given by Dr. Loo Chu Kiong from Multimedia University during the Symposium on Autonomous Vehicle Development in Malaysia on January 13, 2011. He said that the neural networks approach which currently being used as robot brain only focus on the surface of human brain neurons. Due to that, the ability of autonomous robot to be intelligent as human brain is far from perfection. In order to overcome the weakness of neural networks, researchers should explore the dendrites instead of neurons, because the number of dendrites inside human brain is more than the number of neurons. But the real question is what is dendrites and how to produce the dendritic computations?
Figure 1 Neuron's dendritic tree
Dendrites are short, thick branched extensions which extend like the roots of a tree over other neurons or body cells. The dendrites all branch off dendritic spines, which in turn branch of the cell body. Dendrites are the receptive sites of the neurons. Here, the neurons receive electric messages from other neurons or body cells. The site where one dendrite meets another neuron's impulse is called the synapse. Usually, neurons have hundreds of dendrite extensions. These extensions are spread over a large area, giving the neuron better reception of signals. Some dendrites are specialized for the accumulation of information. These cells are finer than other dendrites and found near the brain.
Based on the literature, the computations of dendritic networks can be divided into two: passive dendritic and active dendritic. The first thing that we need to understand is the computations in passive dendritic. It is important to recognize that the passive properties of the dendritic tree provide the backbone for the electrical signaling in dendrites, even when they are richly endowed with voltage-dependent ionic currents. For example, the threshold for initiation of a dendritic spike is determined in part by the availability of sodium channels, but perhaps even more by the passive load of the surrounding dendrites, which dictates how much of the input current will depolarize the membrane and how much will flow axially toward other dendritic regions . In terms of signal propagation, dendrites behave like electrical cables with medium-quality insulation. As such, passive dendrites linearly filter the input signal as it spread to the site of initiation, where its compared with the threshold. This filtering tends to attenuate the dendritic signal as a function of the distance it travels and the frequency of the original signal.
The understanding of computations in active dendritic also crucial. The active properties of the dendritic tree provide a feedback mechanism. Solely on the basis of anatomical observations states that in the nervous system information flows in one direction: from dendrites to soma to axon. In the past decade it has become clear that in many types of neurons the presence of excitable ionic currents in the dendrites supports dendritic action potentials that travel in the reverse direction, from the soma into the dendrites . Computationally this “backpropagation” has major consequences because it implies that the neuron is no longer an open-loop system but has an internal mechanism of feedback. It is thus no longer the case that feedback is a property only of the network, but rather it is a salient property of each element of the network. Moreover, the feedback conveyed by the backpropagating action potential is highly sophisticated and has many important consequences for dendritic function, and also for synaptic plasticity .
Although dendrites have been studied for decades, the field of dendritic computation is still in its infancy. This is partly because dendrites remain relatively inaccessible and have only recently begun to yield their secrets to the onslaught of multiple new experimental tools. However, the real challenge is deeper one: how to evaluate the importance of mechanisms on the molecular and cellular level for computation at the behavior level. The ability not only to record electrical and chemical signals in the intact brain but also to manipulate the structure and function of dendrites using molecular tools will hopefully allow us to move from the descriptive level, correlating dendritic signals linked to computation with behavior, toward directly testing the causal nature of these links. Such experiments will provide a deeper understanding of how single neurons contribute to computation in the brain and should inspire the development of novel neural network architectures with the computational powers of real brains.
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