Chapter 2 Questions
- Nothing in the universe is linear, and everything varies with time. Why do we study linear time-invariant systems if they do not exist?
- What are the primary physical sensor types most useful for reverse-engineering? How do the primary senses (touch, taste, smell, vision, and hearing) fit into these types?
- What are the three basic types of stimulus reception in biological sensory systems?
- What are the three basic maps of sensory receptive fields found in the brain?
- What are the similarities between natural and artificial neural networks?
- If neurons are so much slower than transistors, how could there be promise of significant performance improvement for computers built with diodes and transistors that model neuronal behavior?
- If biological systems are constantly maturing and adapting, why is it beneficial to study the structure and function of the neuronal system of a mature adult animal (or human).
- What is an immediate environmental adaptation in the human vision system?
- Compare and contrast charge and steady-state charge neutrality in neurons and transistors.
- How do the membrane resistance and axonal resistance affect transmission ability of an action potential down an axon? What other factors can improve transmission? That is, what other factors increase the length of the spatial constant?
- If a layer of cells exhibits lateral inhibition and a single neuron fires (produces an action potential), what happens to adjacent cells that are connected to this cell? What happens to cells that are connected but are farther away?
- How are signals typically processed in neuronal layers? Examples may include the neuronal layers of the retina or the brain.
- How is the strength of a signal measured when encoded as action potentials of the same peak value (around +55 mV)?
- Action potentials are triggered when the intracellular fluid potential exceeds a threshold. How is it that for a steady input the output firing rate (frequency of action potentials) adapts from an initial firing rate to a slower firing rate?
- What controls the rate of adaptation?
Chapter 2 References
[Dowl87] Dowling, J. E., The Retina: An Approachable Part of the Brain, Harvard University Press, Cambridge, Massachusetts, 1987.
[Dowl92] Dowling, J. E., Neurons and Networks–An Introduction to Neuroscience, Harvard University Press, Cambridge, Massachusetts, 1992.
[Hass56] Hassenstein, B. and Reichardt, W., Systemtheoretische Analyse der Zeit-, Reihenfolgen und Vorzeichenauswertung bei der Bewegungsperzeption des Russelkafers Chlorophanus. Zeitsschrift fur Naturforschung, Teil B, Vol. 11, pp. 513–524, 1956.
[Hech90] Hecht-Nielsen, R., Neurocomputing, Addison-Wesley, New York, 1990.
[Horen96] Horenstein, M., Microelectronic Circuits and Devices, Pearson, 1996.
[Kand81] Kandel, E. R. and Schwartz, J. H., Principles of Neural Sciences, Elsevier/North-Holland, New York, 1981.
[Koch91] Koch, C., “Implementing early vision algorithms in analog hardware: An introduction”, in Mathur, B. P., and Koch, C., Eds., Visual Information Processing: From Neurons to Chips, Proc. of the SPIE, Vol. 1473, 1991.
[MacG91] MacGregor, R. J., Neural and Brain Modeling, Academic Press, Inc., New York, 1987.
[MatLab] MatLab, which stands for “MATrix LABoratory”, is a trademark of the computational software product developed by the Mathworks, Inc.
[Vass95] Vassileu, A., Mitou, D., and Manahilou, V., “Grating detection and identification dissociated by pattern adaptation”, Spatial Vision, Vol. 9, No. 2, pp. 221–234, 1995.
[Warner77] Warner, G. F., The Biology of Crabs, Van Nostrand Reinhold Company, NewYork, ISBN: 0-442-29205-8, 1977.
[Zorn90] Zornetzer, S., Davis, J., and Lau, C., Eds., An Introduction to Neural and Electronic Networks, Academic Press, Inc., ISBN: 0-12-781881-2, 1990.