Higher Network Activity Induced by Tactile Compared to Electrical Stimulation of Leech Mechanoreceptors

The tiny ensemble of neurons in the leech ganglion can discriminate the locations of touch stimuli on the skin as precisely as a human fingertip. The leech uses this ability to locally bend the body-wall away from the stimulus. It is assumed that a three-layered feedforward network of pressure mechanoreceptors, interneurons, and motor neurons controls this behavior. Most previous studies identified and characterized the local bend network based on electrical stimulation of a single pressure mechanoreceptor, which was sufficient to trigger the local bend response. Recent studies showed, however, that up to six mechanoreceptors of three types innervating the stimulated patch of skin carry information about both touch intensity and location simultaneously. Therefore, we hypothesized that interneurons involved in the local bend network might require the temporally concerted inputs from the population of mechanoreceptors representing tactile stimuli, to decode the tactile information and to provide appropriate synaptic inputs to the motor neurons. We examined the influence of current injection into a single mechanoreceptor on activity of postsynaptic interneurons in the network and compared it to responses of interneurons to skin stimulation with different pressure intensities. We used voltage-sensitive dye imaging to monitor the graded membrane potential changes of all visible cells on the ventral side of the ganglion. Our results showed that stimulation of a single mechanoreceptor activates several local bend interneurons, consistent with previous intracellular studies. Tactile skin stimulation, however, evoked a more pronounced, longer-lasting, stimulus intensity-dependent network dynamics involving more interneurons. We concluded that the underlying local bend network enables a non-linear processing of tactile information provided by population of mechanoreceptors. This task requires a more complex network structure than previously assumed, probably containing polysynaptic interneuron connections and feedback loops. This small, experimentally well-accessible neuronal system highlights the general importance of selecting adequate sensory stimulation to investigate the network dynamics in the context of natural behavior

Encoding of Tactile Stimuli by Mechanoreceptors and Interneurons of the Medicinal Leech

For many animals processing of tactile information is a crucial task in behavioral contexts like exploration, foraging, and stimulus avoidance. The leech, having infrequent access to food, developed an energy efficient reaction to tactile stimuli, avoiding unnecessary muscle movements: The local bend behavior moves only a small part of the body wall away from an object touching the skin, while the rest of the animal remains stationary. Amazingly, the precision of this localized behavioral response is similar to the spatial discrimination threshold of the human fingertip, although the leech skin is innervated by an order of magnitude fewer mechanoreceptors and each midbody ganglion contains only 400 individually identified neurons in total. Prior studies suggested that this behavior is controlled by a three-layered feed-forward network, consisting of four mechanoreceptors (P cells), approximately 20 interneurons and 10 individually characterized motor neurons, all of which encode tactile stimulus location by overlapping, symmetrical tuning curves. Additionally, encoding of mechanical force was attributed to three types of mechanoreceptors reacting to distinct intensity ranges: T cells for touch, P cells for pressure, and N cells for strong, noxious skin stimulation. In this study, we provide evidences that tactile stimulus encoding in the leech is more complex than previously thought. Combined electrophysiological, anatomical, and voltage sensitive dye approaches indicate that P and T cells both play a major role in tactile information processing resulting in local bending. Our results indicate that tactile encoding neither relies on distinct force intensity ranges of different cell types, nor location encoding is restricted to spike count tuning. Instead, we propose that P and T cells form a mixed type population, which simultaneously employs temporal response features and spike counts for multiplexed encoding of touch location and force intensity. This hypothesis is supported by our finding that previously identified local bend interneurons receive input from both P and T cells. Some of these interneurons seem to integrate mechanoreceptor inputs, while others appear to use temporal response cues, presumably acting as coincidence detectors. Further voltage sensitive dye studies can test these hypotheses how a tiny nervous system performs highly precise stimulus processing

Cell anatomy and network input explain differences within but not between leech touch cells at two different locations

Mechanosensory cells in the leech share several common features with mechanoreceptors in the human glabrous skin. Previous studies showed that the six T (touch) cells in each body segment of the leech are highly variable in their responses to somatic current injection and change their excitability over time. Here, we investigate three potential reasons for this variability in excitability by comparing the responses of T cells at two soma locations (T2 and T3): (1) Differential effects of time-dependent changes in excitability, (2) divergent synaptic input from the network, and (3) different anatomical structures. These hypotheses were explored with a combination of electrophysiological double recordings, 3D reconstruction of neurobiotin-filled cells, and compartmental model simulations. Current injection triggered significantly more spikes with shorter latency and larger amplitudes in cells at soma location T2 than at T3. During longer recordings, cells at both locations increased their excitability over time in the same way. T2 and T3 cells received the same amount of synaptic input from the unstimulated network, and the polysynaptic connections between both T cells were mutually symmetric. However, we found a striking anatomical difference: While in our data set all T2 cells innervated two roots connecting the ganglion with the skin, 50% of the T3 cells had only one root process. The sub-sample of T3 cells with one root process was significantly less excitable than the T3 cells with two root processes and the T2 cells. To test if the additional root process causes higher excitability, we simulated the responses of 3D reconstructed cells of both anatomies with detailed multi-compartment models. The anatomical subtypes do not differ in excitability when identical biophysical parameters and a homogeneous channel distribution are assumed. Hence, all three hypotheses may contribute to the highly variable T cell responses, but none of them is the only factor accounting for the observed systematic difference in excitability between cells at T2 vs. T3 soma location. Therefore, future patch clamp and modeling studies are needed to analyze how biophysical properties and spatial distribution of ion channels on the cell surface contribute to the variability and systematic differences of electrophysiological phenotypes

Neural coding with graded membrane potential changes and spikes

Kretzberg J, Warzecha A-K, Egelhaaf M. Neural coding with graded membrane potential changes and spikes. Journal of computational neuroscience. 2001;11(2):153-164.The neural encoding of sensory stimuli is usually investigated for spike responses, although many neurons are known to convey information by graded membrane potential changes. We compare by model simulations how well different dynamical stimuli can be discriminated on the basis of spiking or graded responses. Although a continuously varying membrane potential contains more information than binary spike trains, we find situations where different stimuli can be better discriminated on the basis of spike responses than on the basis of graded responses. Spikes can be superior to graded membrane potential fluctuations if spikes sharpen the temporal structure of neuronal responses by amplifying fast transients of the membrane potential. Such fast membrane potential changes can be induced deterministically by the stimulus or can be due to membrane potential noise that is influenced in its statistical properties by the stimulus. The graded response mode is superior for discrimination between stimuli on a fine time scale

Membrane potential fluctuations determine the precision of spike timing and synchronous activity: a model study

Kretzberg J, Egelhaaf M, Warzecha A-K. Membrane potential fluctuations determine the precision of spike timing and synchronous activity: a model study. Journal of computational neuroscience. 2001;10(1):79-97.It is much debated on what time scale information is encoded by neuronal spike activity. With a phenomenological model that transforms time-dependent membrane potential fluctuations into spike trains, we investigate constraints for the timing of spikes and for synchronous activity of neurons with common input. The model of spike generation has a variable threshold that depends on the time elapsed since the previous action potential and on the preceding membrane potential changes. To ensure that the model operates in a biologically meaningful range, the model was adjusted to fit the responses of a fly visual interneuron to motion stimuli. The dependence of spike timing on the membrane potential dynamics was analyzed. Fast membrane potential fluctuations are needed to trigger spikes with a high temporal precision. Slow fluctuations lead to spike activity with a rate about proportional to the membrane potential. Thus, for a given level of stochastic input, the frequency range of membrane potential fluctuations induced by a stimulus determines whether a neuron can use a rate code or a temporal code. The relationship between the steepness of membrane potential fluctuations and the timing of spikes has also implications for synchronous activity in neurons with common input. Fast membrane potential changes must be shared by the neurons to produce synchronous activity

Temporal precision of the encoding of motion information by visual interneurons

Warzecha A-K, Kretzberg J, Egelhaaf M. Temporal precision of the encoding of motion information by visual interneurons. Current Biology. 1998;8(7):359-368.BACKGROUND: There is much controversy about the timescale on which neurons process and transmit information. On the one hand, a vast amount of information can be processed by the nervous system if the precise timing of individual spikes on a millisecond timescale is important. On the other hand, neuronal responses to identical stimuli often vary considerably and stochastic response fluctuations can exceed the mean response amplitude. Here, we examined the timescale on which neural responses could be locked to visual motion stimuli. RESULTS: Spikes of motion-sensitive neurons in the visual system of the blowfly are time-locked to visual motion with a precision in the range of several tens of milliseconds. Nevertheless, different motion-sensitive neurons with largely overlapping receptive fields generate a large proportion of spikes almost synchronously. This precision is brought about by stochastic rather than by motion-induced membrane-potential fluctuations elicited by the common peripheral input. The stochastic membrane-potential fluctuations contain more power at frequencies above 30-40 Hz than the motion-induced potential changes. A model of spike generation indicates that such fast membrane-potential changes are a major determinant of the precise timing of spikes. CONCLUSIONS: The timing of spikes in neurons of the motion pathway of the blowfly is controlled on a millisecond timescale by fast membrane-potential fluctuations. Despite this precision, spikes do not lock to motion stimuli on this timescale because visual motion does not induce sufficiently rapid changes in the membrane potential

Impact of photon noise on the reliability of a motion-sensitive neuron in the fly's visual system

Grewe J, Kretzberg J, Warzecha A-K, Egelhaaf M. Impact of photon noise on the reliability of a motion-sensitive neuron in the fly's visual system. The journal of neuroscience. 2003;23(34):10776-10783.Variable behavioral responses to identical visual stimuli can, in part, be traced back to variable neuronal signals that provide unreliable information about the outside world. This unreliability in encoding of visual information is caused by several noise sources such as photon noise, synaptic noise, or the stochastic nature of ion channels. Neurons of the fly's visual motion pathway have been claimed to represent perfect encoders, with photon noise as the main noise source limiting their performance. Other studies on the fly's visual system suggest, however, that internal noise emerging within the nervous system also affects the reliability of motion vision. To resolve these contradictory interpretations, we performed an electrophysiological investigation, inspired by the "equivalent noise" paradigm applied in psychophysics, on the fly's motion-sensitive H1 neuron. Noise-like brightness fluctuations of different strength were superimposed on the motion stimuli. Because the noise level found to affect the temporal properties of the spike responses is much larger than the estimate of photon noise under the experimental conditions, our results indicate that motion vision is more likely to be limited by internal sources of variability than by photon noise