Dynamics of neurons controlling movements of a locust hind leg: Wiener kernel analysis of the responses of proprioceptive afferents

Abstract

1. The response properties of proprioceptive sensory neurons providing input to the local circuits controlling leg movements of the locust have been analysed by the Wiener kernel method. The proprioceptor, the femoral chordotonal organ, encodes the position and movements of the tibia about the femorotibial joint. 2. Intracellular recordings were made from sensory neurons while the apodeme of the organ was moved with a band-limited Gaussian white noise signal with a cutoff frequency of 27, 58, or 117 Hz. To define the input-output characteristics of the neurons, the first- and second-order Wiener kernels were computed by a cross-correlation between the spike response of the afferents and the white noise stimulus. 3. White noise stimulation elicited sustained spiking in 50 out of 54 afferents throughout the 20 s periods of stimulation and recording. The first-order kernels, the linear response properties, of these afferents were of six basic types that were dependent on the cutoff frequency of the white noise stimulus. These included 1) flexion-sensitive afferents that were primarily position sensitive irrespective of stimulus frequency, 2) flexion-sensitive afferents that were position sensitive at low frequencies but also coded velocity at higher frequencies, 3) flexion-sensitive afferents that coded velocity at all stimulus frequencies, 4) flexion-sensitive afferents that coded velocity at low stimulus frequencies but also acceleration at high frequencies, 5) extension-sensitive afferents that coded velocity at all stimulus frequencies, and 6) extension-sensitive afferents that coded velocity at low stimulus frequencies and acceleration at high frequencies. A seventh type contained the four remaining afferents that adapted rapidly to the stimulus within 3-5 s. These were all extension-acceleration sensitive irrespective of stimulus frequency. 4. The gain curves (produced by Fourier transform of the 1st-order kernels) and the power spectra of the linear models (produced by convolving the 1st-order kernels with the white noise) demonstrated that responses in the position-sensitive afferents are representative of a constant gain low-pass filter with a cutoff frequency of approximately 80 Hz, whereas those in the velocity- and acceleration-sensitive afferents are band passed, having peaks at 80 Hz. 5. The main nonlinearity was a signal compression in which the diagonal peak(s) of the second-order nonlinear kernels offset one or more peaks of the first-order kernels and represents a rectification or directional sensitivity of the afferents.(ABSTRACT TRUNCATED AT 400 WORDS)

Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network

“…Locusts were mounted ventral-side-uppermost in modelling clay and the apodeme of the FeCO exposed by opening a small window of cuticle in the distal anterior femur (Kondoh et al 1995), grasped between the tips of fine forceps attached to a shaker (Ling Altec 101) and cut distal to the forceps. The metathoracic ganglion was exposed by making a small window in the ventral thorax, supported on a wax covered silver platform, and the sheath treated with protease (Sigma type XIV) for 1 min before recording.…”

“…The FeCO is responsible for mediating reflex movements of the leg. The approximately 90 sensory neurons encoding the dynamics of the movement of the tibia (Kondoh et al 1995;Matheson 1990) embedded in the FeCO project to the metathoracic ganglion that contains the local networks that produce and control the movements of the leg. These neural networks (Fig.…”

“…Timedelayed mutual information quantifies the co-dependence between variables by considering shared previous information content as a function of time (Nichols et al 2005;Palus et al 2001;Vastano and Harry 1988). Here we analyse the delayed mutual information (DMI) estimated from a large multivariate neurobiological data set obtained by Angarita-Jaimes et al (2012), Kondoh et al (1995), Newland and Kondoh (1997a,b) and Vidal-Gadea et al (2010) from a neural network that produces and controls movements of the hind leg of an insect, the desert locust. We utilize the nervous system of the locust for analysis due to its relative simplicity, accessible nervous system and extensive background of physiological and morphological studies (see Burrows 1996).…”

Sensory processing within antenna enables rapid implementation of feedback control for high-speed running maneuvers

“…The control theoretic framework allows us to test if the neural signals exiting the antenna enable implementation of wholebody feedback control for rapid turning. Based on previous characterizations of insect mechanoreceptors (Heinzel and Gewecke, 1979;Matheson, 1990;Kondoh et al, 1995;Ridgel et al, 2001;Spencer, 1974;Zill and Moran, 1981;Wong and Pearson, 1976;Hess and Büschges, 1997;Büschges and El Manira, 1998), it is expected that the receptors on the antenna would show phasic and phasic-tonic responses. However, what is surprising is that these mechanoreceptors can convey signals about a whole-body control variable at the level of primary afferents.…”

“…However, to our knowledge, no studies to date have linked sensory neural responses of primary afferents to the hypothesized requirements for whole-body control predicted by integrating the body within a control theoretical framework. From the sensory encoding perspective, extraordinary efforts have described local, proprioceptive feedback circuits and afferent processing for controlling slow walking behavior and posture (Duysens et al, 2000), such as in locusts (Matheson, 1990;Kondoh et al, 1995;Holtje and Hustert, 2003;Zill and Jepson Innes, 1988), cockroaches (Ridgel et al, 2001;Spencer, 1974;Zill and Moran, 1981;Wong and Pearson, 1976) and stick insects (Hess and Büschges, 1997;Büschges and El Manira, 1998;Zill et al, 2012Zill et al, , 2013, but it remains unclear to what extent afferent processing contributes to stabilize the whole body during high-speed locomotion.…”