Behavior Research Methods, Instruments, & Computers 1985, 17(2),339-344 SESSION XI TEACHING APPLICATIONS OF MICROCOMPUTERS N. John Castellan, Presider Teaching students to model neural circuits and neural networks using an electronic spreadsheet simulator THOMAS T. HEWETT Drexel University, Philadelphia. Pennsylvania An electronic spreadsheet simulator can be used to enable students to conduct simulated micro­ electrode recording experiments. In addition, it can be used both to let students explore the oper­ ation of models of hypothetical neural networks and to let them design and develop their own neural models. A recent paper (Smith et al., 1984) described the educa­ demand. This calculation cycle often involves recalcula­ tional goals of student ownership of a personal microcom­ tion of new values for previously entered formulas that puter at Drexel University. In a later paper, Hewett and depend upon still other values that may have been changed Perkey (1984) argued that many computer programs in­ during or since the last calculation cycle. The typical tended for instructional purposes are impractical or of little spreadsheet simulator also offers a number of program­ use when each student has a personal machine. However, ming language capabilities, including the ability to required student access to a personal computer creates new manipulate strings and to do iterative calculations. In ad­ opportunities for the use of microcomputer applications dition, built-in functions can be combined to produce a programs. One obvious, but nontrivial, example is the im­ limited plotting capability, thereby providing both a pact of the word processor on what is considered accept­ graphical and a numerical method for illustrating relational able written work, from lab reports to term papers. When concepts. Although the spreadsheet program requires that every student has a word processor, the instructor no the simulation designer be able to specify the relationships longer need worry about creating time-consuming extra to be explored, it does not require that the end user be work (e.g., retyping) when demanding written work be a programmer. revised until acceptable. There are a number of areas of instruction in psychol­ A less obvious example of a new use for a microcom­ ogy in which this tool can be applied. For example, in puter application program involves the electronic spread­ dealing with sensation, perception, and pattern recogni­ sheet simulator (e.g., Multiplan, SuperCalc, VisiCalc, tion, it is sometimes desirable for students to understand etc.). This kind of program was originally designed to both the basic neurophysiology and the functional opera­ simulate and replace the large paper worksheet used in tion of various hypothetical neural circuits and networks. accounting and financial planning. It is, however, a more One approach to helping students build their understand­ generally useful tool that can be used to study and ex­ ing was illustrated by Bott and Munro (1977) and Lind­ plore functional relationships among a number of say and Norman (1977). This approach involves present­ parameters. Consequently, it can be used in fields as ing basic neurophysiological data, describing the diverse as ecology (e.g., Silvert, 1984), nutrition, physics, procedures by which the data have been gathered, and and psychology. then abstracting fundamental concepts. These basic con­ An electronic spreadsheet simulator is oriented around cepts then become the building blocks used in develop­ a matrix ofrows and columns, into which the user enters ing a series ofincreasingly more complex, and more ade­ text, data, formulas, or instructions. The program exe­ quate, explanatory models. A number ofthese theoretical cutes user-specified calculations either automatically, af­ models can be quickly and easily represented and simu­ ter a new value is entered anywhere in the matrix, or upon lated using an electronic spreadsheet program. By begin­ ning with inadequate but intuitively simple models, one The author's mailing address is: Department of Psychology and So­ can gradually introduce the necessary complexities re­ ciology, Drexel University, Philadelphia, PA 19104. quired to deal with a broad range ofavailable phenomena. 339 Copyright 1985 Psychonomic Society, Inc. 340 HEWETT SIMULATING MICROELECTRODE RECORDING ing been protected so that a user can change only the un­ derlined values. Consider the following heuristic simplification. A typi­ Between the columns of receptors and transmitters, cal microelectrode recording experiment involves insert­ there is a column for the most recently calculated output ing a microelectrode into a nerve bundle in an attempt of each receptor and a column of question marks. The to pick up the neural impulses generated by a single cell. question marks symbolize a lack of knowledge about the Once the investigator is recording from a single transmit­ nature and number ofconnections between the two layers ter, different patterns of stimulation are introduced to the of cells. Finally, there is a single value representing the receptors. Then an attempt is made to discover meaning­ most recently calculated firing rate of the transmitter neu­ ful relationships by comparing the patterns of stimulation ron that is being recorded. With no receptor stimula­ with their effects on recorded cell output. A further sim­ tion, this transmitter shows a background rate of plification can be introduced by narrowing focus to a sin­ 100 impulses/sec. The user's problem is to discover which gle, one-cell-wide slice of neural tissue, and concentrat­ receptors affect the transmitter, whether the effects are ing on two sets of cells, a set of receptors, and a set of excitatory or inhibitory, and how large each effect is. transmitters. What is not visible here is which transmitter cell is be­ Now, assume that the output of a single transmitter is ing recorded and the underlying formula that the spread­ being recorded. With no receptor stimulation, this cell sheet program uses to calculate the transmitter's output. shows a steady background firing rate. By temporarily In that formula, the total output is the sum of the trans­ stimulating each individual receptor, observed changes mitter's background firing rate and values that represent in the firing pattern ofthe transmitter cell can be attributed the effects of receptors. Each value for a particular recep­ to the effect of the stimulated receptor. A similar result tor is the current receptor firing rate multiplied by a gain can be obtained by moving a square wave of light from factor between 1.0 and - 1.0. This gain factor indicates one end to the other over the set of receptors. Either proce­ the nature and magnitude of the receptor's effect. Posi­ dure allows identification of the receptor cells that are part tive values represent excitatory connections, negative of the neural circuit, identification of whether the effect values, inhibitory ones. The absolute value of a gain fac­ of a receptor is excitatory or inhibitory, and determina­ tor determines the percentage of the receptor firing rate tion of how strong the effect is. that is passed on to the transmitter being recorded. These One way of simulating this situation is represented in firing rates and gain factors can be specified in the un­ Figure 1, which is the screen display from an Apple derlying formula in a variety of ways. They can be en­ Macintosh running Microsoft Multiplan. The first column tered as constants, they can be referenced in the formula symbolizes the current input values for each of a set of by identifying another location in the spreadsheet that the receptor cells. Next, the display represents the one-cell­ program is to access in order to find the needed value, wide slice of neural tissue with two sets ofneural cells­ or they can be the result of a calculation. In this particu­ receptors and transmitters. The vertical orientation is not lar example, the contribution of each receptor in the required, but does decrease computation time. It also unknown circuit is the product of a constant and a value makes possible some limited graphics and plotting. The found in another spreadsheet location. grid lines that typically provide visual definition for in­ After appropriate preparation, a student can be asked dividuallocations in a spreadsheet matrix are not visible to discover the relationships by modifying the levels of here. This is a result of the spreadsheet template's hav- stimulation to the receptors. For example, Figure 2 represents a screen display after a user has begun to pass .. File Edit Select Format Options Calculate a square wave of light over the receptors. The receptor input values here are in the number of units of light re­ quired to produce 100 impulses/sec in the receptors. As indicated by the pull-down menu on the upper right, the Recorded output spreadsheet is set for automatic calculation. This means __.> R(l) ? T( 1) from a that each time the user enters another value, the program ....9 --> R(Z) ? T(2) single ...9 --> R(3) ? T(3) Transmitter automatically calculates or recalculates every other value. .....9 --> R(4) ? T(" ) neuron .....9 --> R(5) ? T(5) For those not familiar with the Macintosh, the pull-down .....0 --> R(6) ? T(6) 100 menu is not visible to the user during calculation. .....9 --> R(7) ? T(7) ...9 --> R(S) ? T(S) At this point, the user has just entered an input to the .. .0 --> R(9) ? T(9) fifth receptor cell. Prior entries produced no changes in .....0 --> R(10) ? T(1 0) the transmitter firing rate. However, after the fifth entry, the transmitter firing rate has dropped to 90 impulses/sec. This is a decrease by 10% of the receptor cell's output and indicates two things. First, the fifth receptor has an in­ Figure 1. Screen display of a spreadsheet template simulating the hibitory effect. Second, the gain is -0.1 for this connec­ recording of a single transmitter neuron.