Tonic Spiking CPG Network

The last series of simulations was run to determine whether the ratio of PADI and ARIN activation affected the network dynamics when the circuit was a tonic spiking state. For elevated levels of activation to the CPG network (12.5 nA) the circuit exhibited bursting and tonic spiking depending on the value of positive feedback gain (Fig. 6.6A, No Feedback). When the gain of positive feedback was low to moderate (0 – 50%) the network was bursting at a frequency around 0.15 Hz (Fig. 6.6B, No Feedback). At elevated levels of positive feedback gain (70 – 100%) the network exhibited tonic spiking (Fig. 6.6C, No Feedback). Separating the two regions was a band of higher frequency bursting (0.18 Hz) around 55% of positive

feedback gain (Fig. 6.6D, No Feedback).

When the sensory feedback loop was closed the frequency of bursting increased and the spike frequency during tonic activity increased (Fig 6.6A, Feedback). Whereas the burst frequency was heterogeneous in the absence of feedback the frequency of bursts increased as the gain of positive feedback increased in the presence of feedback. For low values of positive feedback gain (0 – 15%) the frequency of bursts was about 0.15 Hz. When the gain of positive feedback ranged between 15% to 60% the frequency of bursting was about 0.22 Hz (Fig. 6.6B, Feedback). For high values of positive feedback gain (60 – 100%) the network exhibited tonic spiking and closing the sensory feedback loop resulted in a higher frequency of spiking than in open loop (Fig. 6.6C, Feedback). In the band where the frequency of bursting was higher

A. TONIC SPIKING BURSTING

Duration (s) Spike Frequency (Hz) Frequency (Hz)

B.

C.

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Figure 6.6. Activity maps and sample rasters for tonic spiking CPG state

The ratio of positive and negative feedback levels change how the network behaves in a tonically active state. A) Activity maps show how the duration of tonic spiking episodes, frequency of tonic spiking, and frequency of bursting vary depending on feedback conditions. Simulations were run in one of two conditions: 1) No Feedback: movements of the leg were only due to its weight under gravity and were not affected by motor neuron activity; and, 2) Feedback: movements of the leg were due to its weight under gravity and motor neuron activity. Axes are scaled from 0% to 100% based on the maximum value sampled as

described in Methods. B-D) Raster plots illustrate examples of network activity for select levels of positive and negative feedback. Levator (blue rasters) motor neuron (MN) activity raises the leg and depressor (red rasters) MN activity lowers the leg as indicated by the joint position (green trace). B) Positive feedback gain was low and negative feedback gain was moderate. C) Positive feedback gain was higher and negative feedback gain was moderate. D) Positive feedback gain was high and negative feedback gain was moderate.

without feedback (55% of positive feedback gain) closing the sensory feedback loop resulted in bursting and bouts of tonic spiking that had higher spike frequencies than in the absence of feedback.

While the tonic spiking regime was largely unaffected by blocking afferent feedback, the properties of bursting were modulated by negative and positive feedback when sensory

feedback was present. When the tonic resistance reflex afferents were disabled the burst frequency was reduced and short duration tonic spiking activity remained unchanged

(Appendix C, Fig. C.5A). Disabling the phasic resistance reflex afferents, however, resulted in higher burst frequencies and short duration tonic spiking was eliminated (Appendix C, Fig. C.5B). When the phasic assistance reflex afferents were disabled the burst frequency was reduced when the sensory feedback loop was closed and short duration tonic spiking was also eliminated (Appendix C, Fig. C.5C). While the tonic afferents mediated a subtle effect, the phasic afferents mediated the bouts of tonic spiking when the sensory feedback loop was closed and also acted to slow the burst frequency. Together these results indicated that

sensory feedback was not effective when the CPG was tonically active, but could still modulate network activity when CPG activation was high. While it was not clear whether the tonic spiking regime was biologically significant, the interesting result here was that sensory feedback

slowed the bursting rhythm, which may correspond to biomechanically favorable frequencies.

6.4 Discussion

The results presented here illustrated that the balance of positive and negative feedback can have large implications for the range of behaviors that a sensorimotor circuit exhibited. When the network was in a quiescent state that corresponded to postural behaviors, for example, simulations in which sensory feedback was absent only yielded one state in which short bouts of low frequency tonic spiking activity occurred. Coupling biomechanical feedback

169 to motor output in closed loop simulations restructured network dynamics into three regimes that depended on the ratio of positive and negative feedback and included bursting, low frequency tonic spiking, and high frequency tonic spiking. In addition, simulations in which individual afferents were disabled showed that the changes induced by feedback were largely mediated by phasic afferents. More specifically, episodes of tonic spiking were mediated by phasic resistance reflex afferents in response to bursts that were mediated by phasic

assistance reflex afferents. This sensorimotor integration effect suggests that sensory feedback might be organizing the network while it is in a quiescent, postural state so as to produce a step cycle or a stronger activation of muscles in response perturbations.