Which Of The Following Happens First Within An Animal Response

• Journal List
• J Neurosci
• v.35(6); 2015 Feb 11
• PMC4323524

J Neurosci. 2015 February xi; 35(vi): 2398–2406.

Responses to Alien Stimuli in a Simple Stimulus–Response Pathway

Pieter Laurens Baljon

¹California Institute of Technology, Division of Biological science, Pasadena, California 91125, and

Daniel A. Wagenaar

¹California Institute of Technology, Segmentation of Biology, Pasadena, California 91125, and

²Academy of Cincinnati, Department of Biological Sciences, Cincinnati, Ohio 45221

Received 2014 Sep 12; Revised 2014 December 2; Accepted 2014 Dec 12.

Abstract

The "local bend response" of the medicinal leech (Hirudo verbana) is a stimulus–response pathway that enables the animate being to bend abroad from a pressure stimulus applied anywhere along its body. The neuronal circuitry that supports this behavior has been well described, and its responses to private stimuli are understood in quantitative item. Nosotros probed the local bend arrangement with pairs of electrical stimuli to sensory neurons that could non logically be interpreted as a single touch to the torso wall and used multiple suction electrodes to record simultaneously the responses in big numbers of motor neurons. In all cases, responses lasted much longer than the stimuli that triggered them, implying the presence of some course of positive feedback loop to sustain the response. When stimuli were delivered simultaneously, the resulting motor neuron output could be described as an evenly weighted linear combination of the responses to the constituent stimuli. However, when stimuli were delivered sequentially, the 2nd stimulus had greater impact on the motor neuron output, implying that the positive feedback in the organization is not strong enough to render it immune to further input.

Keywords: invertebrate, neuronal circuits, sensory disharmonize, stimulus–response pathways

Introduction

Sensory systems accept traditionally been studied by neuroscientists one modality and i stimulus at a time. This approach has been tremendously successful, nonetheless in nature behaviorally relevant singular events, such equally the appearance of a predator or casualty, are typically heralded by multiple sensory modalities, and it is also very mutual for multiple unrelated sensory events to occur with temporal overlap. Of item involvement are sensory conflicts: situations in which multiple simultaneous stimuli would direct the receiver toward contrasting behaviors. Despite contempo advances in recording and imaging techniques, establishing the neuronal footing of sensory disharmonize resolution remains difficult to achieve at the neuronal level in higher animals. Accordingly, little is known nigh sensory conflict processing at the level of microcircuits. This is unfortunate because one can oftentimes learn a lot about the inner workings of a system past exploring the edges of its capabilities (e.chiliad., Marder and Fineberg, 1996).

Fortunately, lower animals also come across sensory conflicts and thus offering opportunities for studying circuits involved in conflict processing in the context of much simpler nervous systems. A particularly attractive instance is the local curve circuit in the midbody ganglia of the medicinal leech Hirudo sp., one of the nearly well-studied examples of a uncomplicated stimulus–response pathway (Kristan, 1982; Lockery and Kristan, 1990a, b; Lockery and Sejnowski, 1992; Lewis and Kristan, 1998a, b, c). Consisting chiefly of a three-layer feedforward network of sensory neurons, interneurons, and motorneurons (Fig. 1 A), the local bend circuit allows the leech to motility away from objects touching any location on its body: Four pressure-sensitive sensory neurons, the P cells, collectively encode location along the body circumference; 17 identified interneurons procedure this data (Lockery and Kristan, 1990b) and feedforward to 10 identified motor neurons (Kristan, 1982); these command the musculature to accurately movement the torso away from the stimulus. Traditionally described as a reflex-like response, the local bend response is actually subject to inhibitory control that shapes both the strength of the overall response and its directional tuning (Baca et al., 2008).

Recording and stimulation. A , Schematic cross department of a leech midbody segment and overview of the local curve circuitry. Four sensory neurons, bilateral P_D and P_Five, reply to force per unit area applied to the trunk wall (illustrated by hands). The "P" cells project to a set of interneurons (yellowish) that in turn project to a set up of excitatory and inhibitory motor neurons (dark-green) that innervate the musculature in the body wall. B , Overview of recording and stimulation. A P_D cell and a contralateral P_V cell (labeled P_V ^c) are impaled with abrupt electrodes used to deliver fasten-evoking stimuli. A pick of iv nerves is targeted with suction electrodes for simultaneous extracellular recording from the axons of multiple motor neurons. Naming of fretfulness follows Ort et al. (1974).

Previous piece of work past Lockery and Kristan (1990a) established that leeches react to simultaneous electric stimuli to adjacent P cells by interpolation of the appropriate responses. This matches the behavioral response to touch to positions intermediate betwixt the centers of the receptive fields of these cells (Lewis and Kristan, 1998b). Plainly, leeches do not appear to perceive a sensory disharmonize under these conditions. Here, we report how the local bend system reacts to the simultaneous application of ii stimuli that would trigger diametrically contrary responses when practical individually: stimulation of i dorsal P cell and the contralateral ventral P cell. In before studies, responses to diametrically opposite P cells were not investigated because they were deemed as well variable at the level of behavior. We overcome this challenge by analyzing electrical activity recording simultaneously from a large number of motor neurons using nerve suction electrodes.

Materials and Methods

Animals and recording.

Medicinal leeches (Hirudo verbana) were obtained from Niagara Leeches and maintained in standard weather condition (Harley et al., 2011). Before dissection, leeches were anesthetized in water ice-cold water. Leeches were immobilized on Sylgard (Dow Corning) in a chilled autopsy tray and opened forth the dorsal midline. Later on removal of overlying tissue, several nerve roots were exposed surrounding a midbody ganglion (M8-M12) (Kristan, 1982). The ganglion with attached nerve roots was transferred to a Petri dish and pinned downward ventral-side up. Suction electrodes were fashioned out of flame-polished microelectrode glass sized for optimal fit to each type of nerve and applied to the nerve ends (Wagenaar et al., 2010). Sharp electrodes (containing 3 Thousand potassium acetate; ∼30 MΩ) were used to penetrate the P cells (Baca et al., 2008). Figure 1 B summarizes the recording setup. Extracellular and intracellular signals were amplified with A-Grand Systems model 1700 and 1600 amplifiers, respectively. P prison cell identities were confirmed based on their electrophysiological properties. Signals were digitized with a National Instruments data acquisition card and recorded with custom software.

Electrical stimulation.

Action potentials were evoked in P cells by current stimulation: x-ms-long depolarizing pulses of 0.7–1.5 nA, as low as possible to reliably evoke exactly one action potential in the P cell for each current pulse. Complete stimuli consisted of trains of 2–10 such pulses in a 500 ms window. Commonly, it proved necessary over the class of an experiment to slightly increase the stimulation current to maintain reliability, or to employ a weakly hyperpolarizing holding current betwixt stimuli to proceed a P prison cell from firing additional action potentials following stimulation.

Spike sorting.

Spikes were extracted from each of the extracellular traces and sorted into putative units using the UltraMegaSort2000 toolbox (Fee et al., 1996; Hill et al., 2011). A typical example of a recorded trace with sorted spikes is shown in Figure 2. Waveforms of some units gradually inverse over time. When this happened and the algorithm spuriously split up such units into several clusters, these were manually recombined. Because a single-jail cell trunk may project axons into multiple fretfulness, it was common to find matching units in pairs of nerves (Fig. ii C). Such units were identified in the dataset and henceforth considered as a unmarried unit. Units were merged if they exhibited virtually-perfect fasten correspondence (>xc%) inside a modest asymmetric time window, typically 1 or 2 ms. Remaining unclassified spikes, mainly artifacts and occasional distorted action potentials, were removed. This concerned well <1% of all spikes.

Spike sorting. A , A short segment of an extracellular recording from an A:B1 nerve (black) with spikes from multiple sources indicated (colored symbols). The spikes marked with red crosses represent the action potentials in the P cell (intracellular trace shown in gray). Calibration: 200 ms, 100 μV (100 mV for intracellular trace). B , Montage of all the spike waveforms in the various clusters extracted from this recording. Colors correspond to the colors in A . Calibration: ii ms, 100 μV. C , Consistency exam of spike sorting results. Fasten waveforms of identified clusters on A:B1 (left) and corresponding fasten-triggered averages of simultaneously recorded ipsilateral PP:B1 nerve (right). Scale as in B .

Quantifying stimulus responses.

Responses in each of the units (putative motor neurons) from which we recorded in a given experiment were combined into a high-dimensional vector in which each dimension represented the number of spikes fired by a particular (putative) neuron. We subtracted the baseline firing rate, calculated over long stretches of recording between stimuli. (The alternative, calculating baseline over just the last several seconds before a particular stimulus yielded estimates that were too noisy for applied use due to relatively low firing rates; the scatter in the two "earlier stimulus" panels in Fig. 5 A reflect this stochastic firing.)

Fourth dimension development of responses to P_D and P_Five ^c stimulation. A , Responses (i.due east., baseline-subtracted firing rates) of 367 isolated putative neurons (dots) in 35 ganglia to stimulation of a P_D neuron (x-axis) or the contralateral P₅ neuron (P_Five ^c) (y-axis). Panels represent consecutive 0.v-s-broad windows. Units positively identified as DE-3 neurons ipsilateral to the P_D neuron are colored ruby (n = eleven); contralateral blueish (north = 14). B , Units that responded to both P_D and P_V ^c stimulation classified by the signs of their responses as a function of time. Reddish represents units with firing rate upregulated past P_D stimulation and downregulated by P_V ^c stimulation. Black represents units downregulated by both. Grey represents units upregulated by both. Blue represents units downregulated past P_D stimulation and upregulated by P_V ^c stimulation. (Numbers do not add together up to 100% considering simply units that, in a given time window, had firing rates >0.v SD dissimilar from their baseline in both P_D and P_V ^c conditions are included in this graph.) Inset, How the various regions in the panels in A are represented in B . White indicates regions not represented. C , Units that responded to either P_D or P_V ^c stimulation with a firing rate increase classified by the ratio of those increases ("D:V"). (Numbers do not add together up to 100% because only units are included that, in a given time window, had firing rates >one SD above their baseline in at least one of the two stimulus conditions.) Inset, How the various regions in the panels in A are represented in C . White indicates regions not represented.

If we had M trials in which, for example, the P_D cell received north pulses, we wrote $\vec{f}$ _thousand,n ^D for the response in the k-th such trial. We and so defined the "canonical dorsal response" in this ganglion every bit follows:

Each component in $\vec{f}$ _D thus represents the number of spikes recorded on average per P_D-cell stimulus in a given motor neuron.

We so used this canonical response to model the responses in trials in which (only) P_D cells were stimulated as follows:

and determined the values for the scaling coefficient a _D in each of the trials. The procedure is illustrated in Figure 3.

Illustration of the calculation of canonical responses. A , Raster plot of responses to P_D stimulation with 2, 4, seven, and 10 pulses (blue, green, orangish, and red, respectively) in three arbitrarily chosen cells simultaneously recorded in one ganglion. B , Response count (number of spikes detected in first iv due south) as a function of number of pulses in the stimulus; same iii cells as in A . Arbitrarily calling these cells "ten," "y," and "z," we calculate the x-, y-, and z-components of the canonical dorsal response vector $\vec{f}$ _D based on these graphs. C , Response count for stimulation to the contralateral P₅ cell. The ten-, y-, and z-components of the approved ventral response vector $\vec{f}$ _V are based on these graphs. D , Visualization of the canonical dorsal (crimson) and ventral (blue) responses in three dimensions with projection downward to the x,z airplane. Also shown is the decomposition of a response in a single trial to a train of two pulses to P_D as a scalar multiple of $\vec{f}$ _D plus a balance (brown). This analogy is based on a pocket-sized subset of the data from one experiment; the full dataset obtained from one experiment comprises recordings from ∼10–xx neurons, which would yield rather high dimensional graphs.

We then proceeded to model the scaling coefficient itself as a role of the number of stimulus pulses northward as follows:

where α and β were adamant using linear least-squares fitting.

A "canonical ventral response," $\vec{f}$ _Five, was analogously constructed and used to model trials in which only P₅ cells were stimulated.

These aforementioned canonical response vectors were as well used to model trials in which a P_D cell received n stimulus pulses while the P₅ ^c cell simultaneously received one thousand pulses. The but divergence was that we now modeled the response as follows:

and the scaling coefficients as follows:

and

respectively. It is important to note that, despite the more than complex stimulus conditions in these trials, the scaling of the responses was still modeled with only two gratis parameters, α and β.

Quantifying "dorsality" of responses.

To quantify to what degree responses in a sure experimental condition are more than "similar" responses to pure dorsal or pure ventral stimulation, we introduced a "dorsality" index past adapting Fisher's linear discriminant analysis (LDA; Fisher, 1936).

We started from the average number of spikes f _c ^D(t) in cell c, latency bin t, post-obit pure P_D stimulation in a given ganglion (the "P_D response"), the trial-to-trial covariance of this response, Σ _c,c′ ^D(t), and the analogous quantities for pure P_V ^c stimulation. Fisher'southward LDA introduces a separability:

in which Δ _c ^DV(t) ≡ f _c ^D(t) − f _c ^V(t) and Σ(t) = Σ^D(t) + Σ^V(t). This separability quantifies how well responses to P_D stimuli can exist distinguished from responses to P_V ^c stimuli. In Fisher's LDA, responses tin and so be classified as either belonging to the P_D grade or the P_V ^c class depending on whether the discriminant

where f _c ⁰(t) = 1 2(f _c ^D(t) + f _c ^V(t)), is positive or negative for a trial with recorded firing rates f_c′ (t).

Although powerful and useful in many situations (e.thousand., Briggman et al., 2005), this formalism has two shortcomings for our application. The first is a purely computational one: Computing the full covariance matrices is numerically unstable when the number of cells is large. We overcome this past approximating the total matrix by a diagonal matrix with estimated per-cell variances based on assuming approximately Poissonian firing: (σ _c ^D(t))² = f _c ^D(t)/K, where K is the number of trials. (And analogously for P _Five ^c stimulation.) This simplifies the separability to the following:

A 2d, more than primal, shortcoming is that S(t) is not divisional and depends strongly on the number of cells recorded. Although it makes sense that response classes are more separable when we accept more detailed information nigh those responses, a measure of the actual difference between the classes should not depend on such experimental details. We therefore introduced the following mensurate of separation between dorsal and ventral responses:

where C is the number of cells recorded from in a given experiment. This separation ranges from 0 to 1: S* = 0 meaning that P_D and P₅ ^c responses are indistinguishable; S* = 1, meaning that some jail cell fired infinitely fast following P_D stimulation and not at all following ventral stimulation, or vice versa. And under reasonable assumptions of statistical properties, S* does non depend on the number of cells recorded in a particular experiment. In practice, S* values between 0.five and 0.7 were unremarkably observed soon after stimulation (run into Fig. 7).

Stimulus responses can be overridden by secondary stimulation. A , Example raster plots of DE-3 cell responses to stimulation of either: merely the ipsilateral P_D neuron; just the contralateral P₅ neuron; both simultaneously; P_D followed past P_V ^c; P_V ^c followed by P_D. B , "Dorsality" (encounter Materials and Methods) of vector responses to simultaneous or sequential stimulation as a function of fourth dimension. Lines and area stand for mean ± SE. Red areas point significantly higher dorsality than in simultaneous instance. Blue areas indicate significantly lower dorsality than in simultaneous case. Vertical colored lines bespeak timing of stimuli. Rightmost column represents separation (see Materials and Methods). C , Dorsality of responses in specific cell types: DE3 jail cell ipsilateral (peak) and contralateral (bottom) to the stimulated P_D neuron. D , Number of spikes contributed in the beginning 0.five s by P_D stimulation with or without preceding P_V ^c stimulation (left) and vice versa (right) in Due north = 26 experiments.

Given observed average number of spikes f _c ^X(t) in prison cell c, fourth dimension bin t, in trials of stimulus status X, we and then calculated the dorsality index for condition Ten equally follows:

Assuming perfect separation between P_D and P_Five ^c responses, this index would approach one if the responses in condition 10 were identical to responses to pure P_D stimulation and −1 if the responses in status X were identical to responses to pure P_Five ^c stimulation. If the responses in condition Ten were equally similar to those following pure P_D and pure P_V ^c stimulation, or in time bins where separation is depression, the dorsality index approaches 0.

Equally introduced in a higher place, D(t) is divers as a unmarried number for the whole population of motor neurons recorded in a given experiment, merely it can equally be divers for an individual neuron: simply by not summing over cells.

Results

Stimulating a P cell intracellularly with trains of 5 pulses at 10 Hz reliably evoked precisely 5 action potentials in that cell. Stimulating P cells in this fashion resulted in responses in many motor neurons (Fig. four). This was true when only one P cell was stimulated at a fourth dimension (left two columns), and also when ii diametrically opposed P cells were stimulated simultaneously (middle column) or sequentially (right 2 columns). In all cases, the response outlasted the stimulus itself by several seconds. Responses lasting up to x south were common (data not shown here, but see Fig. v).

Motor neuron responses to P-cell stimulation. Left to right, Columns show unmarried examples of responses to stimulating a P_D neuron, the contralateral P₅ neuron (P_V ^c ), both simultaneously, or one followed past the other. Top to bottom, Rows show intracellular traces in the P_D and P_V ^c neurons followed by extracellular traces from four different nerves, all recorded simultaneously (meet Fig. 1). Superscript "i": ipsilateral to P_D; superscript "c": contralateral to P_D. Plot symbols represent spikes and their cluster assignments. Scale: 500 ms.

Although Figure 4 suggests that responses differ significantly depending on which P cell(south) was/were stimulated (and raster plots, not shown, ostend this), an analysis of the responses of all recorded units recorded from 35 experiments actually revealed that the vast majority of motor neurons responded to both P_D and P_V ^c stimulation by increasing their firing rates (Fig. 5 A). Notable exceptions were the dorsal exciter neurons DE-three. DE-3 neurons ipsilateral to the stimulated P_D prison cell (Fig. 5 A, red dots, mostly in lower right quadrant) exhibited a potent increment in firing rate following P_D stimulation, whereas following P_V ^c stimulation (i.due east., stimulation to a P₅ cell contralateral to the DE-3 neuron), they either decreased their firing rates slightly or were unaffected. DE-3 neurons contralateral to the stimulated P_D cell (Fig. 5 A, blueish dots) exhibited mixed responses following P_D stimulation, whereas following P_V ^c stimulation (i.east., stimulation to a P_V prison cell ipsilateral to the DE-3 neuron), they mostly decreased their firing rates slightly.

The leech is justly famous for the high degree of stereotypy of its ganglia, then one might reasonably hope that recorded units could exist identified with specific known neurons, based, for example, on their spike waveforms and projection patterns through the various nerves (Ort et al., 1974). Indeed, positively identifying the DE-3 neurons was straightforward using these criteria. Naturally, we attempted to cluster the other units (both manually and semiautomatically) and then as to obtain a mapping between units and known neurons. To our surprise, nonetheless, we found that the variability between ganglia was such that nosotros were unable to exercise then with the desired degree of confidence. Ultimately, we had to take that it was safest to perform the rest of the assay based on "units," without making specific claim to neuronal identities.

Of those units, then, that responded to both P_D and P_V ^c stimuli with a significant change in firing rate, the vast majority responded positively to both (gray bars in Fig. five B), whereas merely a small minority responded in opposite directions (reddish and bluish bars). That is not to say that most cells practice not distinguish between P_D and P_V ^c stimulation: if we consider all units that responded with an increase in firing rates to at least ane kind of stimulus, most exhibited a strong preference either for P_D stimulation (Fig. 5 C, red and yellow bars) or for P_V ^c stimulation (blue and green confined), whereas only a small fraction responded approximately equally strongly to the ii (gray and pale colored bars).

Thus far, nosotros have discussed motor neuron output qualitatively. Is a curtailed quantitative clarification as a office of the stimulus input likewise possible? Using stimuli of 2–10 pulses delivered in a 500 ms train, we beginning asked whether the activity of an arbitrarily selected pair of motor neurons scaled proportionally as stimulus forcefulness (number of pulses delivered to the P cells) was increased. Nosotros found that this was the case (Fig. half dozen A). In other words, the relative strength of the responses of motor neurons was unaffected by stimulus strength, equally long as the stimulus site (which P cell was stimulated) remained the aforementioned. As a event, nosotros could limited responses to dissimilar strengths of stimulation at a given site as scalar multiples of a "approved response vector" for that stimulus site (Fig. 6 B; encounter Materials and Methods). Nosotros found that the response magnitude (vector length of firing rate vector) every bit a office of stimulus forcefulness (again, number of stimulus pulses) obeyed a simple, gently sublinear scaling police force: ||f|| ∼ n/(one + βn), where f is the response vector, n is the number of stimulus pulses, and β = 0.06 ± 0.04 for dorsal and β = 0.12 ± 0.07 for ventral stimulation. These numbers were not significantly dissimilar from each other. What is more, this scaling law could too be used to predict responses to mixed stimuli (where both P_D and P₅ ^c received a number of stimulus pulses; Fig. 6 C), again with fit parameters α and β that were not significantly dissimilar. This simple epitome explained the not bad bulk of the variance in the responses (Fig. 6 D).

Motor neuron responses scale predictably equally a office of stimulus strength. A , Responses of 2 arbitrarily selected cells ("A" and "B") from a typical experiment calibration nearly equally with increasing stimulus strength (color coded). Dotted line indicates equal scaling. B , Scaling coefficient of vector responses as a part of stimulus strength, for responses to simple P_D stimulation (disks and solid best-fit line) and for simple P_V stimulation (circles and dotted best-fit line). Each marker (at a given stimulus strength) represents data from one preparation; n = 7 preparations with 15 ± half dozen isolated units each. Markers were displaced by a slight amount in the horizontal management for increased visual clarity. C , Ventral scaling coefficient of vector responses to combined P_D and P_V ^c stimuli equally a function of forcefulness of stimulation to the P_D neuron (x-centrality) and the P_V ^c neuron (colors) with all-time-fit traces. As in B , each mark (at a given stimulus force) represents an private preparation, and markers were displaced horizontally for clarity. D , Residuals of response vector length after model fitting. Markers displaced for clarity.

These results so far indicated that the local bend responses to multiple stimuli could be described as a simple linear combination of the responses to private stimuli (with slightly sublinear scaling coefficients). Is this really true in all stimulus weather? To test this, nosotros delivered trains of 5 pulses (at 10 Hz) either simultaneously to P_D and P_V ^c or with a 500 ms delay. We compared responses to temporally displaced stimuli (P_D before P_V ^c or vice versa, as in the last two columns of Fig. four) with responses to simultaneous stimuli. Raster plots obtained from an example neuron (Fig. 7 A) propose that the system does indeed differentiate between these cases. To further investigate, we introduced a "dorsality index" (see Materials and Methods), which indicates to what extent the response at a certain latency after an arbitrary stimulus is "similar" the response at the same latency following a pure P_D stimulus or whether it is more like the response to a pure P₅ stimulus. This metric could be applied either to the complete (vector) response of all units in a recording (Fig. 7 B) or to individual units (Fig. 7 C).

We found that in either case, responses to simultaneous stimuli had about-aught dorsality, indicating that these responses were approximately every bit like (or dissimilar) to pure P_D responses and pure P_Five ^c responses (Fig. 7 B,C, left cavalcade). The fact that the dorsality was more oftentimes slightly negative than slightly positive may be due to sampling bias: our recordings independent more than signals from neurons that innervated ventral muscles than dorsal muscles.

Strikingly different results were obtained from nonsimultaneous stimuli. When the P_D cell was stimulated offset (second column), the initial response (earlier the P_Five ^c received its stimulus) had very high dorsality. So far, no surprise: up to that indicate the stimulus essentially was a pure P_D stimulus. But post-obit the 2nd stimulus, the responses had significantly more negative dorsality than observed after simultaneous stimulation. Conversely, when the P_V ^c stimulus preceded the P_D stimulus, dorsality was initially very negative (again, unsurprising), but after the P_D stimulus arrived became significantly more positive than observed following simultaneous stimulation. This could not be attributed to sensitization, in which the start stimulus primes the arrangement to respond more than strongly to a second stimulus (Lockery and Kristan, 1991): A ventral stimulus following a dorsal stimulus contributed only 0.62 ± 0.02 times equally much to the full firing rate as a ventral stimulus did in isolation, and a dorsal stimulus post-obit a ventral stimulus contributed 0.65 ± 0.03 as much equally a dorsal stimulus in isolation: all the data points in Figure vii D lie below the y = x line. Instead, these results bespeak that for sequential stimulation, the second stimulus to some degree "overrides" the first stimulus.

Discussion

Information technology has long been known that one grade of sensory neurons, the P cells, is largely responsible for triggering the medicinal leech's local curve response (Kristan, 1982). Since then, the local bend reponse has been studied using many modalities, including videorecorded responses, electromyography, and voltage-sensitive dye recordings (e.g., Lewis and Kristan, 1998b; Baca et al., 2005, 2008). The circuitry underlying the response has been probed in detail by stimulating P cells either individually or in neighboring pairs (Lockery and Kristan, 1990a, b), but at the level of private motor neurons, the response to stimuli that activate diagonally opposed P cells was considered besides variable for assay (Lockery and Kristan, 1990a).

By recording simultaneously from large numbers of motor neurons in a single ganglion using up to four nerve suction electrodes, we were, for the first time, able to analyze in detail the local bend response to stimuli that activate diametrically opposite P cells. In dissimilarity to adjacent P cells, diametrically reverse P cells tin can never be coactivated past a single localized concrete stimulus because their receptive fields are well separated (Fig. 1 A) (Nicholls and Baylor, 1968). Appropriately, although it makes sense for a leech to interpret coactivation of adjacent P cells as a single physical stimulus to a location betwixt these cells' receptive fields, coactivation of opposite P cells allows no such estimation. Instead, i possible interpretation might be as a compression, such as a leech might feel when a predator tries to grab it, which might warrant an escape response quite distinct from a local curve.

So ane could, a priori, look whatsoever number of responses to bilateral stimuli: The response could be a linear combination of the responses to individual stimuli. This happens in maybe the nearly simple form in the McGurk outcome in humans, in which logically unconnected visual and auditory cues nevertheless are experienced as connected (cited in Stein, 1998). The homo encephalon performs a more sophisticated form of linear combination in visuo-vestibular conflict processing (a putative underlying crusade of motion sickness) (Warwick-Evans et al., 1998), which was successfully modeled as a procedure of Bayesian integration (Butler et al., 2010).

Alternatively, 1 or the other stimulus might boss the response, maybe as a function of relative stimulus strength or salience. Such an event could exist considered in the context of behavioral choice (e.g., Shaw and Kristan, 1997; Briggman et al., 2005; Körding and Wolpert, 2006).

Lastly, the response could exist completely different from the responses to individual stimuli. This might, for example, make sense when responding to the detection of two predators approaching from reverse directions and then that fleeing directly away from either ane would not be adaptive. This outcome could also exist considered in the context of behavioral choice.

Existing piece of work on the local bend response in the leech has pointed at a continuous rather than a categorical encoding of stimulus location (Lewis and Kristan, 1998c), which would predict the first of these iii outcomes. All the same, those results were obtained using a congruent set of stimuli. When diametrically opposed stimuli were applied, outcomes were "variable between preparations, resembling most unmarried PD stimulation" (Lewis and Kristan, 1998c), which might predict the second of the iii outcomes. When considering the sequential stimulation (Figs. 4 and ​ 7), the literature provides less guidance to our expectations considering previous piece of work (Thomson and Kristan, 2006) focused on the timing of responses rather than the timing of stimuli. However, in sure other cases where multiple behaviors compete for expression, a hierarchy has been observed such that certain behaviors always override certain others (Gaudry and Kristan, 2010). In notwithstanding other cases, perchance when the competing behaviors were more at par, information technology was found that, every bit the first stimulus had more time to bring the system to its corresponding country, a 2d stimulus had an increasingly difficult time driving the system, suggesting the presence of attractor dynamics (Briggman et al., 2005).

The reality we observed for simultaneous stimuli was the simplest possibility: we found that the propensity of the local bend system to integrate simultaneous stimuli across neighboring receptive fields also extends to opposing receptive fields, fitting well the model proposed by Lewis and Kristan (1998a): the organisation treats such plain conflicting stimuli just like it does nonconflict stimuli and responses to simultaneous stimuli could be modeled with a unproblematic linear model that used identical parameter values used to model the nonconflict situation, and no additional parameters. The fact that a single scaling parameter describes the circuitous responses is very well in line with key inhibition governing the proceeds of the circuit (Baca et al., 2008). In a future report, information technology would exist of great interest to compare individual neurons between preparations and quantify the variability reported earlier.

In dissimilarity to what we found for simultaneous stimuli, responses to sequential pairs of stimuli could not be predicted by a uncomplicated linear combination of the isolated responses to the constituent stimuli. In this situation, the 2nd stimulus of the pair had the greatest influence on the ultimate response, in dissimilarity to what ane might expect from a simple reflex pathway in which an initial stimulus sets in movement a response sequence that is impervious to subsequent stimuli (possibly through a mechanism akin to the attractor dynamics proposed by Briggman et al., 2005). This is specially notable considering the local bend response lasts considerably longer than the stimulus that triggers it, implying that the underlying circuit must feature some form of positive feedback loop to sustain it. (Our nowadays results exercise not let the states to determine whether this feedback is implemented as a synaptic loop or equally a cellular mechanism in the local bend interneurons.) The fact that the second of a pair of consecutive stimuli had greater influence on the ultimate response implies that this positive feedback is non strong enough to lock the organisation in a item response contained of subsequent input. From an ethological perspective, this makes sense because it is surely adaptive for an fauna to update its behavioral output when conditions alter.

Sensory conflicts have been a classic subject in philosophy since long earlier neuroscience emerged equally a separate discipline: a version of the familiar paradox of the donkey stuck midway betwixt a stack of hay and a pail of h2o because it is as driven by hunger and thirst dates back to Aristotle (cited in Rescher, 2005). The connected issue of how the brain determines whether or non two simultaneous sensory events relate to the same concrete object, the so-called binding problem, has also been studied extensively by neuroscientists and psycholigists alike (east.thou., Treisman, 1998).

The leech may not be an ideal animal to study the binding problem in its total celebrity, simply because its perceptual state space is probably too modest. Regardless, our extensive characterization of the local bend response to alien, or at least largely incongruent stimuli, opens up a new avenue of research into how nervous organization process sensory conflicts. Information technology provides a new model to study, for example, the role of interneuronal network dynamics, in a context where inputs can be precisely controlled and outputs exhaustively measured. Meanwhile, it builds on the extensive knowledge and anatomical particular we already have from the study of the local bend circuit in the context of other questions. Finally, with increasingly loftier dimensional recordings ever more common throughout neuroscience, the analytical methods presented in this paper may observe broader awarding in describing and understanding complex sensory processing in terms of the constituent stimuli.

Footnotes

This work was supported by the Broad Foundations. D.A.W. is the recipient of a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. Nosotros give thanks an anonymous reviewer for many valuable comments and in particular for the suggestion to consider bilateral pressure in the context of being pinched by a predator.

The authors declare no competing financial interests.

This is an Open Access commodity distributed under the terms of the Artistic Commons Attribution License (http://creativecommons.org/licenses/past/3.0), which permits unrestricted utilize, distribution and reproduction in any medium provided that the original work is properly attributed.

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Articles from The Journal of Neuroscience are provided hither courtesy of Guild for Neuroscience

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323524/

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