44 The Physiology of Sense Organs 



membrane during the recovery from an action potentials One 

 theory of the control of frequency in sensory receptors, pro- 

 pounded by Adrian 2 in 1928, utilized the continuously changing 

 state of membrane refractoriness (during recovery) to account for 

 recurrent excitation in the face of a constantly maintained 

 stimulus. Adrian supposed that a steady stimulus might re-excite 

 a sensory neuron, so that another impulse could be initiated as 

 soon as the absolute refractory period of the preceding spike had 

 passed. As is illustrated in figure 17, the strongest stimuli (being 



INTERVAL BETWEEN SENSE ORGAN 

 DISCMARGE (seconds) 



v5^ 



Hoi |o2 /IO3 fto4 Aos / I06 



I I II I I I I I I I 



l ib. NJbe libs A b4 ft 



|b5 



Threshold .qos 01 



I I I I I I I I I I 



^1 



INTERVAL BETWEEN NERVE STIMULI 

 (seconds) 



Fig. 17. Control of impulse frequency solely by membrane 

 refractory period, as first proposed by Adrian. Impulse threshold 

 is infinite during the absolute refractory period, and thereafter it 

 falls with time as indicated by the curved line. Greater levels of 

 stimulating current will thus trigger successive impulses earlier in 

 the refractory period than will weak ones. As indicated, an a-level 

 shock or stimulus will generate a higher frequency of impulses than 

 one at 6-level. (From Adrian,^ Fig. 10.) 



able to excite less sensitive membrane) w^ould trigger successive 

 impulses early in the relative refractory period, whereas a weaker 

 stimulus would require longer periods of recovery between spikes. 

 Re-excitation would thus only occur after longer intervals of time. 

 A serious criticism of the ' refractory period hypothesis ' arises 

 from the finding that some neurons fire rhythmically at impulse 

 frequencies a good deal lower than those which would necessarily 

 be dictated by the duration of their relative refractory periods. 

 This point was emphasized by Hodgkin^^ in studies of a sense 

 organ model. Utilizing the large single motor axons available in 



