A simple algorithm for averaging spike trains

The control of nocifensive movements in the caterpillar Manduca sexta

In response to a noxious stimulus on the abdomen, caterpillars lunge their head towards the site of stimulation. This nocifensive 'strike' behavior is fast (∼0.5 s duration), targeted and usually unilateral. It is not clear how the fast strike movement is generated and controlled, because caterpillar muscle develops peak force relatively slowly (∼1 s) and the baseline hemolymph pressure is low (<2 kPa). Here, we show that strike movements are largely driven by ipsilateral muscle activation that propagates from anterior to posterior segments. There is no sustained pre-strike muscle activation that would be expected for movements powered by the rapid release of stored elastic energy. Although muscle activation on the ipsilateral side is correlated with segment shortening, activity on the contralateral side consists of two phases of muscle stimulation and a marked decline between them. This decrease in motor activity precedes rapid expansion of the segment on the contralateral side, presumably allowing the body wall to stretch more easily. The subsequent increase in contralateral motor activation may slow or stabilize movements as the head reaches its target. Strike behavior is therefore a controlled fast movement involving the coordination of muscle activity on each side and along the length of the body.

Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.