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By Gordon Pask, Warren S. McCulloch

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The other is fully conntttabJe. 85 BIOLOGICAL CONTROLLERS I I I I finite automaton - a fixed network of 'artificial neurones' - could extract Gestalten. Recently Lettvin and Matturana, working in collaboration with McCulloch and Pitts,71 have discovered a network in the frog performing abstraction up to the level of a conceptual category, a primitive Gestalt. The frog retina is divided into regions, receptive fields, containing many receptors. For each receptive field, networks among the bipolar and ganglionic neurones compute the value of four distinct attributes: (i) Presence of sharp edge of an object imaged upon the receptors.

The nne memory element, 4~+- R~o"wl$ I. Enri_nt TN,;. .... ' '1 ...... :. _-_. _- . : ,"..... ", '\onrthJ . " " ' - ... loci I I pia",. nd;a,o...... oj: _ t... poho ·lrtIfidoI lKW .... ' '~ pcflo4l ,"'" thot. ~ '" . "..... " ,.... l"ifra e ,op"~ctiofl' gr, ""'~;',;:,::: but go to 01 ... dlml, "'-''''''I'"d. M r,::::--:R:':DdQU~ Fig. 17 (ii). An imitative unit, made up of singte action autonomous elements (equi valently 'artificial neuroncs'), performs trial s upon in environmen t and also interrogates an internal 'memory', (Each 'memory' register is represented by a small circle at a line intersection).

Problem Sofl>ing When a control system achieves stability it 'solves the problem' posed by not being stable. To say a controller is a 'problem solver' only when fonnal logical variables are manipulated and 0 is an explicit function of their state seems unduly pedantic. Now we • Evolutionary networks have been computer simulated (by BeurJe" Foulkes'· and using a much more restricted system Farley and Clark"') and realized in a restricted, but logically tract~ble way on a special puroose machine (G.

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