CURBIO11013S0960-9822(14)00263-210.1016/j.cub.2014.03.004Elsevier LtdFigure 1Operant Learning-Induced Acceleration and Regularization of Buccal Motor Pattern Genesis and Underlying Bursting in “Decision-Making” Central Pattern-Generating Neurons(A) Experimental protocol. During 30 min training, naive (control) and contingently trained (contingent) animals received a continuous uningested food stimulus (horizontal line) to incite radula biting movements (vertical bars). In the contingent group, an additional food reward was delivered (arrowheads) in association with each spontaneous bite. Following buccal ganglia isolation, the neural correlates of biting movements were tested ≤4 hr after in vivo training.(B) Schematic of buccal central pattern generator (CPG; large gray circle) that drives protraction (Protr.), retraction (Retr.), and closure (Clos.) phases of the buccal motor pattern (BMP) for each bite cycle. Crucial network components are the electrically coupled B63/B30/B65 neurons (black circles), whose bursting initiates each BMP (electrical synapse, resistor symbol; chemical inhibition and excitation, filled circles and triangles respectively).(C) Extracellular recordings of a BMP (protraction, retraction, and closure phases recorded from motor nerves I2 n., n.2,1, and R n.) and associated bursts recorded intracellularly from B63/B30/B65 during tonic stimulation (2 Hz, 8 V) of sensory nerve 2,3 (n.2,3 Stim.). Burst onset in the three neurons preceded BMP onset (dashed vertical line). Horizontal and vertical scale bars represent 5 s and 25 mV, respectively.(D–G) Repetitive BMPs and B63/B30/B65 bursting in control (D) and contingent (E) preparations under identical n.2,3 stimulation. Horizontal and vertical scale bars represent 30 s and 20 mV, respectively. BMP cycle frequency (F) and coefficient of variation (c.v.) of inter-BMP intervals (G) were increased and decreased, respectively, in the contingent as compared with the control group. Two-tailed Mann-Whitney test: T = 202.5, p = 0.013 (F); T = 348, p = 0.007 (G).Group data show means ± SEM and individual sample sizes. ∗∗p < 0.025; ∗∗∗p < 0.01.Figure 2Learning-Induced Increase in B63/B30/B65 Membrane Excitability and Strength of Electrical Coupling(A–C) Nonsynaptic plasticity.(A) Spike threshold of a B63 cell, determined by injecting depolarizing current pulses of increasing intensity, was lower in a contingent than in a control preparation. Horizontal and vertical scale bars represent 1 s and 10 mV, respectively.(B and C) Both spike threshold (B) and input conductance (C) were significantly lower in contingent preparations for each cell type. Two-tailed Mann-Whitney test: spike threshold, B63: T = 87.5, p = 0.038; B30: T = 81, p = 0.026; B65: T = 21, p = 0.048; input conductance, B63: T = 70, p = 0.005; B30: T = 67, p = 0.04; B65: T = 47, p = 0.007.(D–F) Synaptic plasticity.(D) Learning-dependent enhancement of electrical coupling between the B63/B65 cells as revealed by the B65 response to hyperpolarizing current pulse injection (−10 nA) into prejunctional B63 in control and contingent preparations (unshaded panels). The natural junctional conductance in each case was determined by the amount of positive artificial junctional conductance that was subtracted by dynamic clamp to negate (here −2 nS and −6 nS) the B65 response to B63 hyperpolarization (shaded panels). Horizontal and vertical scale bars represent 1 s and 5 mV (B65) or 10 mV (B63), respectively.(E and F) The coupling coefficient (E) and junctional conductance (F) for B63/B65 and B63/B30 were significantly higher in the contingent than in the control groups. Two-tailed Mann-Whitney test: coupling coefficient, B63/B65: T = 66, p = 0.037; B63/B30: T = 48, p = 0.01; junctional conductance, B63/B65: T = 26, p = 0.041; B65/B30: T = 45, p = 0.004.Group data show means ± SEM and individual sample sizes. ∗p < 0.05; ∗∗∗p < 0.01.Figure 3An Artificial Increase in the Excitability of B63/B30/B65 Neurons in Naive Control Preparations Mimics the Learning-Induced Increase in Frequency, but Not the Regularity, of BMP Genesis(A) Experimental protocol and equivalent electrical circuit for the addition of a dynamic-clamp-defined leak conductance (Gleak) to the natural input conductance (Gin) of an individual neuron.(B) Introduction of an artificial Gleak of −60 nS (shaded panel) increased the excitability of a target B63 neuron (indicated by a decrease in spike threshold) compared with that arising from the natural leak conductance alone (i.e., Gleak: 0 nS). Horizontal and vertical scale bars represent 2 s and 20 mV, respectively.(C and D) Relationship between Gleak and spike threshold of B63/B30/B65 (C; data fitting, B63: r2 = 0.977, p < 0.0001; B30: r2 = 0.998, p < 0.0001; B65: r2 = 0.998, p < 0.0001) and coupling coefficients for the B63/B65 and B63/B30 cell pairs (D; data fitting, B63/B65: r2 = 0.995, p < 0.0001; B63/B30: r2 = 0.981, p < 0.0001). In (C), Gleak was introduced into one of B63, B30, or B65. In (D), Gleak was introduced into a B63 and current pulses for measuring coupling coefficients were injected into either postjunctional B30 or B65. (For details, see Figure S1.)(E) In a control preparation, the frequency, but not the regularity, of spontaneous BMP genesis and associated spike bursts in B63/B30/B65 increased in response to a dynamic-clamp-defined Gleak of −60 nS (shaded panel) introduced simultaneously into the three neurons. Horizontal and vertical scale bars represent 30 s and 25 mV, respectively.(F and G) Quantification of changes in frequency (F), but not irregularity (G), of BMP generation for different values of artificial Gleak added simultaneously to the three neurons.Group data show means ± SEM and individual sample sizes.Figure 4An Artificial Strengthening of the Electrical Synapses among B63/B30/B65 Neurons in Control Preparations Mimics the Learning-Induced Increase in Regularity, but Not the Frequency, of BMP Genesis(A) Equivalent circuit for the addition of a dynamic-clamp-defined junctional conductance (Gjunc.) to the natural junctional conductance (Gnat.) between a pair of electrically coupled cells.(B) Introduction of an artificial Gjunc. (+20 nS, shaded panel) increased the electrical coupling between a B63/B65 cell pair compared with that arising from the natural conductance alone (i.e., Gjunc.: 0 nS). Horizontal and vertical scale bars represent 2 s and 2 mV (B65) or 20 mV (B63), respectively.(C and D) Quantification of the effects of changes in Gjunc. on the coupling coefficients of the B65/B63 and B30/B63 cell pairs (C), and on the spike threshold of B63 (D).(E) The regularity, but not the frequency, of BMP genesis and associated B63/B30/B65 spike bursts increased in response to an artificial Gjunc. (+20 nS, shaded panel) introduced simultaneously into the B63/B30 and B63/B65 cell pairs. Horizontal and vertical scale bars represent 30 s and 25 mV, respectively.(F and G) Quantification of the effects of Gjunc. on the frequency (F) and coefficient of variation (G) of BMP generation.Group data show means ± SEM and individual sample sizes.Figure 5A Concurrent Experimental Increase in B63/B30/B65 Excitability and Strength of Their Electrical Synapses in Control Preparations Induces a Transition to Accelerated and Regularized BMP Genesis(A) Recurrent BMPs and underlying B63/B30/B65 bursting, which were generated at a low frequency and with irregular interpattern intervals in a control preparation, switched immediately to a rapid and regular expression under the conjoint addition of a dynamic-clamp-defined Gleak of −60 nS to the three cell types and a Gjunc. of +10 nS to the B63/B30 and B63/B65 cell pairs (shaded panel). This change in activity was immediately reversed when injection of the artificial conductances ceased (0 nS). Horizontal and vertical scale bars represent 30 s and 25 mV, respectively.(B and C) Comparisons of the frequency (B) and coefficient of variation (C) of BMP expression before (white bars) and during (black bars) simultaneously imposed changes in Gleak and Gjunc. as illustrated in (A). Two-tailed Wilcoxon test: W = 21.0, p = 0.031 and W = −21.0, p = 0.031, respectively.Group data show means ± SEM and individual sample sizes. ∗p < 0.05.Figure 6In Contingent Preparations, a Reversal of BMP Genesis to an Untrained Control Phenotype Is Produced by a Concomitant Decrease in B63/B30/B65 Excitability and Electrical Synaptic Strength(A) Frequent and regular B63/B30/B65 bursting and resultant BMP expression were reversibly transformed to a slower and irregular activity state throughout the introduction of a dynamic-clamp-defined Gleak of +60 nS to the three cell types and a Gjunc. of −10 nS to B63/B30 and B63/B65 cell pairs (shaded panel). Horizontal and vertical scale bars represent 30 s and 25 mV, respectively.(B and C) Comparisons of the frequency (B) and coefficient of variation (C) of BMP genesis before (white bars) and during (black bars) simultaneously imposed changes in Gleak and Gjunc. as illustrated in (A). Two-tailed Wilcoxon test: W = −36.0, p = 0.008 and W = 36.0, p = 0.008, respectively.Group data show means ± SEM and individual sample sizes. ∗∗∗p < 0.01.ArticleDifferential Roles of Nonsynaptic and Synaptic Plasticity in Operant Reward Learning-Induced Compulsive BehaviorFredSieling1235AlexisBédécarrats125JohnSimmers12Astrid A.Prinz4RomualdNargeot12∗romuald.nargeot@u-bordeaux2.fr1Institut de Neurosciences Cognitives et Intégratives d’Aquitaine (INCIA), Université de Bordeaux, UMR 5287, 33000 Bordeaux, France2CNRS, INCIA, UMR 5287, 33000 Bordeaux, France3Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA4Department of Biology, Emory University, Atlanta, GA 30322, USA∗Corresponding author5Co-first authorPublished: April 3, 2014SummaryBackgroundRewarding stimuli in associative learning can transform the irregularly and infrequently generated motor patterns underlying motivated behaviors into output for accelerated and stereotyped repetitive action. This transition to compulsive behavioral expression is associated with modified synaptic and membrane properties of central neurons, but establishing the causal relationships between cellular plasticity and motor adaptation has remained a challenge.ResultsWe found previously that changes in the intrinsic excitability and electrical synapses of identified neurons in Aplysia’s central pattern-generating network for feeding are correlated with a switch to compulsive-like motor output expression induced by in vivo operant conditioning. Here, we used specific computer-simulated ionic currents in vitro to selectively replicate or suppress the membrane and synaptic plasticity resulting from this learning. In naive in vitro preparations, such experimental manipulation of neuronal membrane properties alone increased the frequency but not the regularity of feeding motor output found in preparations from operantly trained animals. On the other hand, changes in synaptic strength alone switched the regularity but not the frequency of feeding output from naive to trained states. However, simultaneously imposed changes in both membrane and synaptic properties reproduced both major aspects of the motor plasticity. Conversely, in preparations from trained animals, experimental suppression of the membrane and synaptic plasticity abolished the increase in frequency and regularity of the learned motor output expression.ConclusionsThese data establish direct causality for the contributions of distinct synaptic and nonsynaptic adaptive processes to complementary facets of a compulsive behavior resulting from operant reward learning.Highlights•Operant conditioning induces compulsive behavior•The modification results from plasticity in a motor pattern-generating network•The neuronal plasticity is controllable by computer-simulated conductances•The action acceleration and regularization derive from distinct cellular changesUsing computer-simulated ionic currents in vitro, Sieling et al. dissect the contributions of distinct synaptic and nonsynaptic adaptive processes to complementary facets of a compulsive behavior resulting from operant reward learning in Aplysia.IntroductionAppetitive reward-dependent learning favors the development of stereotyped recurrent actions underlying habitual and compulsive behaviors such as those related to drug addiction and eating disorders [1, 2]. Several forms of neuronal plasticity, involving both synaptic and nonsynaptic properties and often induced by reward-triggered release of dopamine in central decision-making networks, are thought to contribute to these behavioral changes [3–5]. However, establishing a causal link between the cellular and behavioral levels has remained challenging. Thus, although an increasing amount of data indicates that alterations in neuronal excitability and synaptic strength are conferred by learning [6–8], it remains unclear how and to what extent either form of plasticity is actually responsible for the behavioral outcome. In particular, the relative contributions of operant conditioning-induced changes in identified central nervous circuitry to the transition from an infrequent and variable motivated act to accelerated, compulsive-like recurrences remain poorly understood.Feeding behavior in the mollusk Aplysia is modified by several forms of associative learning, including classical and operant conditioning, which alter the central decision-making processes underlying the selection and initiation of feeding actions [9–11]. In a form of operant conditioning, the contingent association for 30–40 min of a food reward with spontaneous biting movements of the animal’s tongue-like radula transforms for several hours the otherwise slow and erratic genesis of buccal movements into an accelerated and stereotyped rhythmic expression that closely resembles other appetitive reward-induced patterns of compulsive behavior [12]. Importantly, neural correlates of these learning-induced changes in Aplysia’s feeding-related behavior continue to be expressed by the underlying central pattern-generating (CPG) circuitry of the isolated buccal ganglia, thereby enabling electrophysiological analysis at the level of identified neurons and synaptic connectivity [13]. The behavioral adaptation to operant conditioning is associated with several forms of plasticity, including changes in membrane input conductance, excitability, and the strength of electrical coupling among a decision-making subset of buccal CPG neurons [14]. These cellular and synaptic modifications are similarly evoked in vitro by contingent electrical stimulation of a dopamine input nerve that conveys the rewarding stimulus in vivo and are blocked by dopamine receptor antagonists, thereby indicating a mediating role for dopaminergic modulation [15].Despite the correlation between cellular/circuit plasticity and operant conditioning-induced changes in Aplysia’s food seeking, a causal relationship between the two remains to be established. Here, using the dynamic-clamp technique, in which simulated membrane and synaptic currents are artificially added to or subtracted from neurons [16–18], we examined whether selective changes in single conductances governing cell excitability and electrical coupling are responsible for the associative modification of feeding circuit output and behavior. Specifically, by enhancing or diminishing neuronal excitability and coupling strengths in real time, separately or in combination, in isolated neuronal preparations from both naive and operantly trained animals, we were able to directly test causation and determine the respective contributions of synaptic and nonsynaptic processes by which associative learning leads to the frequent and repetitive expression of a behavior.ResultsLearning-Induced Acceleration and Regularization of Buccal Motor Patterns and Associated Neuronal PlasticityTo examine the causal relationship between changes in cellular properties and the operant learning-induced transition of radula biting movements from an impulsive- to compulsive-like expression, we subjected two groups of animals to different training protocols for 30 min (Figure 1A). In both groups, a continuous, noningested food stimulus was used to incite food-seeking behavior. In a control group, animals were exposed solely to this stimulus, whereas a contingent group additionally received an ingestible food reward that was contingent on each spontaneous radula bite occurrence, so as to reproduce the action/reward association of operant conditioning [12]. The neural plasticity resulting from this appetitive procedure is evident in the radula motor patterns expressed by the buccal ganglia of the two animal groups after isolation in vitro. Here, tonic electrical stimulation of the buccal nerve n.2,3 to mimic the inciting food stimulus applied in vivo [12] elicited repetitive buccal motor patterns (BMPs) recorded from buccal motor nerves (see Figures 1C–1E), with each BMP corresponding to the motor drive for a single radula bite. BMP activation depends critically on a subset of “decision-making” neurons (B30/B63/B65) that are components of the buccal CPG (Figure 1B). Neurons B30 and B65 are electrically coupled to neuron B63, but no direct connection exists between B30 and B65. Spontaneous impulse bursts in this neuronal group are the primary instigators of each movement cycle (Figure 1C) [13]. In isolated preparations from control animals, so-called “fictive” bites and associated burst discharge in the B30/B63/B65 cells are generated at irregular intervals and relatively infrequently, as typically seen in the exploratory food-seeking behavior of untrained animals (Figure 1D) [12]. In contrast, in vitro preparations from the contingent group spontaneously produced BMPs and underlying B30/B63/B65 cell bursts at a significantly higher rate (Figures 1E and 1F) and with a stereotyped rhythmic organization (Figures 1E and 1G), corresponding to the behavioral changes that occur after contingent training in vivo [12]. We established previously that noncontingently reinforced animals (i.e., animals subjected to food reward delivery uncorrelated with bite occurrences) and their neural correlates of feeding in vitro were no different from untrained controls [12, 14]; therefore, the plasticity observed here must have resulted strictly from the associative effects of training.The operant conditioning-dependent acceleration and regularization of BMP genesis is accompanied by alterations in the membrane and synaptic properties of the pattern-initiating B30/B63/B65 neurons. Neuronal excitability, as measured by the minimum amount of intrasomatic current injection necessary to reach threshold for impulse firing, similarly increased (i.e., spike threshold decreased) in all three cell types in contingent as compared with control preparations (Figures 2A and 2B). Corresponding with this excitability increase, the input conductance of these neurons decreased with operant conditioning (Figures 2C and 2D). Concomitantly, learning also enhanced the electrical synaptic coupling between B63/B30 and B63/B65 cell pairs, as illustrated in Figure 2D, where the voltage deflection of postjunctional B65 neurons in response to the same (−10 nA) hyperpolarizing current pulse injected into prejunctional B63 partners was stronger in a contingent than in a control preparation. This connectivity enhancement is further evident from group comparisons of the coupling coefficients (Figure 2E).This change in electrical coupling could have two origins: a decrease in input conductance that indirectly alters the strength of the neurons’ junctional pathway, and/or a direct increase in the junctional conductance itself [19]. To determine whether the latter mechanism, in addition to the observed input conductance decrease (see above), also contributed to the learning-derived increase in neuronal coupling, we used a dynamic-clamp procedure in which an artificial negative junctional conductance (Gjunc.) was introduced to nullify, thereby providing a measure of, the naturally existing conductance. In the control and contingent preparations of Figure 2D (shaded panels), a Gjunc. of −2 nS and −6 nS, respectively, was required to negate functional coupling between the B63/B65 neurons. Similarly, group data comparison indicated that the natural junctional conductance increased significantly in both the B63/B65 and B63/B30 synapses of contingent as compared with control preparations (Figure 2F). Thus, in addition to altering the intrinsic membrane properties of the BMP-initiating neurons, operant conditioning directly modifies the strength of their electrical connections.Increase in B30/B63/B65 Excitability Increases the Frequency of BMP GenesisTo assess the respective contributions of changes in biophysical properties and electrical synapses to the learning-induced acquisition of accelerated, regularized BMP expression, we introduced artificial conductances by dynamic clamp into the B30/B63/B65 neurons of naive control preparations in which BMP genesis was infrequent and irregular. Although the exact nature of the current (or currents) modulated by operant learning was not investigated, an increase in a neuron’s excitability associated with an input conductance decrease is most likely attributable to a decrease in membrane “leak” conductance, the mainly K+ outward current that contributes to setting neuronal resting membrane potential and excitability [20]. To test this possibility, we experimentally manipulated the natural leak conductance (Gleak) of individual B30/B63/B65 neurons with simulated Gleak by dynamic clamp (Figure 3A). In all cases, adding or subtracting artificial Gleak in parallel with the natural conductance caused a proportional increase or decrease, respectively, in the injected cell’s input conductance (see Figures S1A and S1B available online), as predicted by the rule of G value addition in a parallel electrical circuit. As expected by such changes in Gleak, the excitability of the neurons (as indicated by alterations in their spike threshold) also increased or decreased linearly with the artificial conductance’s absolute value (Figures 3B and 3C). Finally, as predicted by the theoretical relationship between the coupling coefficient and input conductance of two electrically coupled cells, artificial Gleak manipulation of a postjunctional neuron also indirectly affected the coupling with a prejunctional partner (Figure 3D; see also Figure S1C). For example, a dynamic-clamp-defined Gleak of −60 nS, which significantly decreased the input conductance and spike threshold of BMP-initiating neurons (see Figures 3B and 3C), additionally caused an increase in the coupling coefficient with the prejunctional cell of each pair (Friedman test and post hoc Newman-Keuls test for B63: input conductance, χ2 = 40.0, p < 0.001, q = 5.196, p < 0.05; spike threshold, χ2 = 38.512, p < 0.001, q = 5.367, p < 0.05; coupling coefficient B63/30, χ2 = 35.612, p < 0.001, q = 4.648, p < 0.05; coupling coefficient B63/B65, χ2 = 32, p < 0.001, q = 5.023, p < 0.05; similar data were also observed with dynamic-clamp manipulation of B30 and B65). These results therefore indicate that an experimentally imposed decrease in membrane leak conductances of the B30/B63/B65 neurons reproduces much of the cellular plasticity induced by operant conditioning, but not the observed increase in junctional conductances.To further assess the impact of these biophysical changes on actual BMP genesis, we manipulated the Gleak of B30/B63/B65 cells in control preparations during ongoing buccal CPG activity elicited by tonic stimulation of n.2,3. A concurrent dynamic-clamp-imposed reduction of Gleak in the three neurons caused an immediate increase in the frequency of BMP expression (Figure 3E). This rate increase, which persisted for the duration of Gleak introduction, varied proportionately according to the artificial conductance’s sign and value (Figure 3F). Consequently, a reduction of Gleak by −60 nS, which reproduced most aspects of the learning-induced cellular plasticity (see above), significantly increased the frequency of BMP expression from that immediately preceding Gleak manipulation (Friedman test and post hoc Newman-Keuls test: χ2 = 46.816, p < 0.001 and Gleak −60 nS versus 0 nS, q = 6.002, p < 0.05). In contrast, the imposed reduction in Gleak did not replicate the learning-induced switch to stereotyped, regular BMP generation: the coefficient of variation of inter-BMP intervals was not significantly modified by dynamic-clamp Gleak ranging from −80 to +80 nS (Figure 3G; Friedman test: χ2 = 6.019, p = 0.645). Thus, a change in the leak conductance of the three CPG cell types that drive BMP genesis reproduces important aspects of the cellular and motor plasticity induced by operant learning, but not the motor program’s regularization (cf. Figures 1D and 1E).Selective Strengthening of the Junctional Coupling between B63/B30 and B63/B65 Regularizes BMP GenesisTo test the specific effect of changes in electrical connectivity between the decision neurons on their membrane properties and BMP expression, an artificial junctional conductance (Gjunc.) was introduced via dynamic clamp to these cells in the control preparations. The injection of positive Gjunc. increased the electrical coupling of a neuron pair as indicated by the increased hyperpolarization of a postjunctional neuron in response to the same-sized negative current pulse (−10 nA) injected into its prejunctional partner (Figures 4A and 4B). The relationship between coupling coefficient and Gjunc. values fitted that predicted by the theoretical equation linking this coefficient and the conductances of two electrically coupled neurons (Figure 4C; see Experimental Procedures; B63/B65, r2 = 0.999, p < 0.0001; B63/B30i, r2 = 0.998, p < 0.0001). Specifically, the coupling coefficient increased significantly from its initial value when Gjunc. reached ≥+5 nS (Friedman test: B63/B30, χ2 = 47.778, p < 0.001; B63/B65, χ2 = 48.000, p < 0.001; post hoc Newman-Keuls test for Gjunc. = +5 nS versus 0 nS: B63/B30, q = 3.464, p < 0.05; B63/B65, q = 3.464, p < 0.05). However, in direct contrast to the outcome of operant conditioning, an increase in Gjunc. increased, not decreased, the global input conductance of a target neuron (Figures S2A and S2B). Moreover, a Gjunc. increase tended to decrease, not increase, the cell’s intrinsic excitability (Figure 4D; see also Figure S2C), although this trend was not significant when the artificial conductance was below +30 nS (Friedman test and post hoc Newman-Keuls test for Gjunc. = 30 nS versus 0 nS: B63/B30, χ2 = 23.945, p < 0.002, q = 4.661, p < 0.05; B63/B65, χ2 = 7.419, p < 0.387). These findings therefore indicate that a selective experimental increase in the junctional conductance between the BMP-initiating neurons, which reproduces the learning-induced strengthening of their electrical synapses, does not mimic the plasticity of their intrinsic biophysical behavior.The effects of such selective changes in electrical coupling on BMP genesis were tested by adding artificial Gjunc. simultaneously to both the B63/B30 and B63/B65 cell pairs in control preparations with the buccal CPG activated by tonic n.2,3 stimulation. Dynamic-clamp-imposed alterations in the natural Gjunc. were unable to alter the mean frequency of BMP production (Figures 4E and 4F; Friedman test: χ2 = 2.729, p = 0.950). In contrast, the regularity of pattern genesis, as quantified by the coefficient of variation in the durations of successive inter-BMP intervals, increased strongly and proportionately with the amount of positive artificial Gjunc. (Figures 4E and 4G; Friedman test: χ2 = 32.495, p < 0.001). Consequently, a dynamic-clamp-driven increase in junctional conductance of ≥+10 nS was sufficient to significantly decrease the coefficient of variation of inter-BMP intervals (post hoc Newman-Keuls test for Gjunc. = +10 versus 0 nS, q = 3.578, p < 0.05). These results thus indicate that a strengthening of the electrical synapses alone between prejunctional B63 and the B30 and B65 neurons is sufficient to replicate the increase in regularity, but not frequency, of BMP expression resulting from learning.Concomitant Changes in Intrinsic Properties and Electrical Synapses Reproduce All Aspects of the Learning-Induced Plasticity in BMP GenesisTo determine whether alterations in intrinsic membrane excitability and strength of electrical synapses can conjointly reproduce the cellular and motor plasticity induced by operant conditioning, we applied dynamic-clamp-defined Gjunc. and Gleak simultaneously to the B30/B63/B65 neurons (Figure S3A). Based on our earlier findings for solitary conductance manipulations in control preparations, an artificial Gleak of −60 nS that reproduced most of the learning-induced cellular plasticity was applied to each neuron, while a simulated Gjunc. of +10 nS, which replicated the learning-dependent strengthening of electrical coupling, was introduced into each of the B63/B30 and B63/B65 cell pairs. This concurrent change in Gleak and Gjunc. significantly decreased the natural input conductance of prejunctional B63 by the sum of the introduced conductances (i.e., −50 nS) (Figure S3B; Wilcoxon test: W = −21.0, p = 0.031). It also significantly increased the cell’s membrane excitability (indicated by decreased spike threshold) and increased the coupling coefficients of the B63/B30 and B63/B65 neuron pairs (Figures S3C–S3E; Wilcoxon test: spike threshold, W = −21.0, p = 0.031; coupling coefficient of either neuron pair, W = 21.0, p = 0.031). Thus, the combination of a strong decrease in leak conductance and a modest increase in junctional conductance is able to reproduce both the membrane and synaptic plasticities resulting from operant conditioning.Significantly, in control preparations that otherwise generated relatively infrequent and irregular BMPs under n.2,3 stimulation, a conjoint alteration in Gleak and Gjunc. immediately and reversibly converted the expression of buccal motor output to faster and more regular patterns (Figure 5A). The average frequency of BMP genesis increased significantly (Figure 5B; Wilcoxon test: W = 21.0, p = 0.031), while the coefficient of variation of the interpattern intervals decreased from that before the dynamic-clamp procedure (Figure 5C; Wilcoxon test: W = −21.0, p = 0.031). These results indicate that concomitant changes in the membrane and synaptic properties of the BMP-initiating neurons in naive preparations are sufficient to produce modifications in buccal motor output that are identical to those expressed in the CNS of animals previously subjected to operant conditioning.Opposite Changes in Membrane and Synaptic Properties Reverse the Cellular and Motor LearningIf the above parallel changes in membrane and synaptic properties are causal to the learning-dependent alterations in buccal CPG operation, then one might expect that this plasticity can be reversed with oppositely directed conductance manipulations. Does an experimental reduction (rather than increase) in B30/B63/B65 excitability and junctional conductance convert stereotyped BMP genesis in contingent preparations to the slower, irregular pattern expression observed in untrained controls? A dynamic-clamp-defined Gleak of +60 nS alone, which was previously found to diminish BMP-initiating neuron excitability (see Figure 3C), caused a significant decrease in the frequency of spontaneous patterns in contingent preparations, with no significant effect on pattern regularity (Figures S4A–S4C; Wilcoxon test: frequency, W = 30, p = 0.039; coefficient of variation, W = 22, p = 0.148). Conversely, in the same preparations, an imposed decrease in the strength of electrical synapses between the B63/B30 and B63/B65 cells via an imposed Gjunc. of −10 nS, significantly increased the coefficient of variation of inter-BMP intervals, but with no significant effect on pattern frequency (Figures S4D–S4F; Wilcoxon test: frequency, W = 8, p = 0.578; coefficient of variation, W = 26, p = 0.031). However, a coincident increase in the leak conductance (Gleak = +60nS) of the three neurons together with a decrease in coupling strength (Gjunc. = −10 nS) between the B63/B30 and B63/B65 cell pairs immediately and reversibly evoked a dual modification in ongoing BMP genesis (Figure 6A): the frequency of patterns decreased significantly, while the coefficient of interval variation increased for the duration of dynamic-clamp manipulation (Figures 6B and 6C; Wilcoxon test: frequency, W = −36, p = 0.008; coefficient of variation, W = 36.0, p = 0.008). Thus, in ganglion preparations from operantly conditioned animals, a simultaneous decrease in decision-making neuron excitability and the strength of their electrical synapses can indeed suppress the motor plasticity resulting from learning and restore the activity phenotype expressed by the ganglia of untrained controls.Taken together, these data provide compelling evidence for a causal relationship between the strength of electrical connectivity among the BMP-initiating neurons and the temporal regularity of buccal CPG output. A separate mechanism involving a change in membrane conductances governing neuron excitability underlies the learning-induced changes in output cycle frequency.DiscussionThe unpredictability and variability in Aplysia’s food-seeking behavior derives partly from the autonomous functioning of a CPG network that is inherently capable of specifying the timing and cycle-to-cycle recurrence of motor pattern production. In particular, this capacity depends on the functional diversity of “decision-making” neurons that through their individual intrinsic membrane properties and pattern of synaptic connectivity within the circuit are able to irregularly instigate each motor pattern occurrence [14]. By modulating these cellular properties, reward learning leads to accelerated and coordinated burst discharge in this neuronal subset, which in turn accelerates and regularizes BMP emission. Our present findings, in which individual cellular conductances were selectively manipulated in vitro, indicate that the inherent biophysical membrane and electrical synaptic properties of the B30/B63/B65 neurons are responsible for ensuring the two distinct aspects of this motor plasticity. Alterations in their intrinsic excitability essentially govern the frequency of feeding motor pattern production, while changes in their electrical interconnections regulate the pattern’s cycle-to-cycle regularity.The oscillatory membrane properties of CPG neurons responsible for rhythmic motor pattern genesis in both invertebrates and vertebrates depend on the interplay between various voltage-dependent conductances [21–23]. K+ and Na+ leak conductances which govern intrinsic excitability also influence the bursting activity of endogenous oscillatory neurons [24–27]. Although these voltage-independent conductances are not directly implicated in the actual burst-generating mechanism, they exert their effects indirectly by changing the cell’s membrane potential through activation ranges for the regenerative, voltage-dependent conductances. This voltage dependence is typically manifested in an oscillating neuron’s response to tonic depolarizing current injection, which alters the overall frequency of ongoing bursting. Such a voltage sensitivity of bursting frequency has been previously reported for the B30/B63/B65 neurons [14]. Therefore, the learning-induced acceleration of bursting in these cells and the resultant rate increase in BMP genesis is consistent with a direct effect of a decrease in Gleak on the expression of their endogenous oscillatory mechanisms. Significantly, the present data further indicate that a leak current with a reversal potential close to a pattern-initiating cell’s resting membrane potential can change the frequency of its oscillatory bursting but has no influence on this activity’s regular or irregular temporal structure.A change in leak conductance not only alters the excitability of individual neurons but also impacts indirectly on their electrical coupling. According to our present results, this latter effect would be expected to modify the regularity of burst-generating oscillations and BMP expression. However, in contrast to the effect of a direct alteration in the junctional conductance between the BMP-initiating neurons, our dynamic-clamp data showed that a Gleak decrease alone is unable to regularize otherwise irregular B30/B63/B65 bursting and pattern production. This inability may be a consequence of Gleak’s parallel effect on neuronal excitability, an increase that might reinforce the desynchronization of ongoing bursting between cells and override a weaker synchronizing and regularizing effect of an indirect enhancement of electrical coupling. Consistent with this possibility, a Gleak decrease in combination with an imposed increase in the junctional conductance itself produced sufficient coupling to regularize the CPG neuron oscillations and associated BMP production.Each neuron of the heterogeneous B30/B63/B65 cell group plays a critical role in triggering the motor patterns for radula biting. Although endogenous burst discharge of B63 alone is necessary and sufficient to initiate individual BMPs, spontaneous bursts in the B30 and B65 neurons contribute to the activating process via their electrical connection with B63. Moreover, the three cell types are each represented by a homologous pair located in the bilateral buccal ganglia. All of the ipsi- and contralateral electrical synapses among these six neurons are modulated by operant conditioning [14]. Our experiments revealed that a dynamic-clamp-induced coupling increase between just three of these neurons was sufficient to regularize BMP recurrences, although slightly higher artificial junctional conductances and coupling coefficients than the natural values observed after learning were required. This difference could be explained by the fact that we were manipulating the conductances of only 50% of the effective pattern-initiating cell population and/or by the anatomical location of the electrical synapses themselves. Although this latter aspect was not investigated here, synapses between ipsilateral neurons are likely to be located close to the prejunctional cell body in the neuropil of the ipsilateral ganglion. Synapses with contralateral partners, however, are presumably distant from the soma in the contralateral ganglionic neuropil. Due to these differences in soma-synapse distance, therefore, it is possible that intrasomatic current injections into a given cell are more effective in modifying the membrane potential of ipsilateral coupled neurons than contralateral postjunctional partners [14]. Consequently, as observed in the mollusk Lymnaea [28], a current injected into a BMP-initiating cell body that changes its somatic potential may only partially affect or even fail to influence distant, contralateral connections implicated in actual motor learning. Despite this potential limitation, however, our findings are consistent with experimental and theoretical studies illustrating that electrical synapses can promote the synchronization of neuronal firing, regulate discharge durations in bursting neurons, and confer stereotyped network rhythmicity from oscillating cell ensembles [29–36]. Our data support the idea that although not directly involved in the endogenous burst-generating mechanism of individual neurons, the junctional conductances of electrical synapses may play a fundamental role in controlling the regularity of the cells’ oscillatory behavior, and thereby of the network to which they belong.ConclusionsThe present study shows that two independent cellular properties within a subcircuit of heterogeneous CPG neurons—non-voltage-dependent membrane conductances that determine excitability and junctional conductances at electrical synapses—are separately responsible for the elevated frequency and stereotypy, respectively, of a contingent-reward-induced transition to compulsive-like repetitive action. Interestingly, recent studies employing optogenetic approaches have begun to dissect specific corticostriatal circuitry in order to decipher the neural mechanisms underlying mammalian compulsive behaviors [37, 38]. The use of dynamic-clamp manipulation to identify the cellular and network substrates and establish causation in the switch from an impulsive to a compulsively repeated act in the simpler Aplysia could therefore offer relevant insights into the neuronal basis of experience-related plasticity and the development of compulsive disorders in general.Experimental ProceduresIn Vivo Behavioral TrainingAplysia were randomly assigned to a control (naive) or a contingent (operantly trained) group (see Figure 1A and Supplemental Experimental Procedures). Individuals were placed individually in a small (8 l) transparent aquarium positioned over a mirror and filled with 5 l of fresh, aerated artificial sea water (ASW). After an initial resting period of 30 min, an animal was trained during a further 30 min period, during which it was subjected to a continuous application of a ∼1.5 cm2 piece of seaweed held with forceps to the dorsal side of the lips without allowing this stimulus to be either bitten or ingested. This arousing stimulus was used to set the occasion for food-seeking behavior, particularly involving cyclic radula biting movements. For the contingent group, an additional stimulus, constituting a consumable food reward, consisted of an intrabuccal injection of 20 μl of filtered seaweed juice obtained from the maceration (20 min) of 0.4 g of dried Ulva lactuca in 10 ml of ASW. The contingent-reward training procedure consisted of successive juice deliveries with a calibrated micropipette, which were each strictly timed to the emission of an individual radula bite cycle. This contingent-reward stimulus was introduced into the buccal cavity at the end of the protraction phase of radula movement and pulse ejected from the pipette as the lips closed. Although this training protocol was very similar to that described previously [14], a more recent study using an equivalent operant conditioning procedure in vitro found that 30 min of contingent reinforcement is sufficient to induce the contingent-dependent neuronal plasticity for several hours [15]. Thus, the duration of our in vivo training period was reduced from 40 to 30 min in the present study.In Vitro ElectrophysiologyAfter training, the buccal ganglia were isolated and desheathed. BMPs were recorded with extracellular pin wire electrodes placed on the peripheral nerves I2 (I2 n.), 2,1 (n.2,1), and radula (R n.) to monitor the protraction, retraction, and closure activity, respectively. Intracellular recordings and stimulations of the BMP-initiating neurons B63, B30, and B65 were made with capillary glass electrodes filled with 2 M KCH3CO2 (tip resistance 15–30 MΩ) (the bathing medium for in vitro preparations and the criteria used for cell identification are provided in the Supplemental Experimental Procedures). All recorded signals were amplified by Axoclamp-2B electrometers (Molecular Devices), visualized on a Tektronix 5113 oscilloscope, digitized by an analog-to-digital converter (CED 1401, Cambridge Electronic Design), and analyzed with Spike2 software (Cambridge Electronic Design).Neuronal excitability, input conductance, and electrical coupling between ipsilateral cell pairs were measured intrasomatically by two-electrode current clamp (i.e., four electrodes were used to test the electrical coupling between neuron pairs). Neuronal excitability was quantified as the minimum amount of injected current (1 s pulses by steps of +0.1 nA from 0 nA) necessary to reach the spike threshold of a cell initially held at −80 mV. Input conductance (Gin) was calculated as the ratio of the intensity of the injected current pulse (−10 nA, 2 s) over the maximum voltage deflection produced by this pulse in a cell initially held at −70 mV. The electrical coupling between a pair of ipsilateral neurons was defined by the coupling coefficient, which is the ratio of the postjunctional voltage response to a prejunctional voltage change evoked by a 2 s, −10 nA current pulse injected into the prejunctional neuron. This coefficient was calculated with the pre- and postjunctional cells held at an initial resting membrane potential of −70 mV.Dynamic-Clamp ProceduresArtificial conductances [16–18] were generated using the Real-Time eXperiment Interface (RTXI; http://www.rtxi.org/) and a National Instruments NI PCI-625 data acquisition card with update frequency at 3–5 kHz. This frequency was chosen to avoid current oscillation that sometimes occurred at higher rates and to allow current updating by variations in voltage that were faster than single spikes (10–15 ms). Each artificial leak and junctional current (Ileak, Ijunc.) injected into a neuron was computed from Ileak = Gleak(Vm1 − Eleak) and Ijunc. = Gjunc.(Vm1 − Vm2), where G is the user-defined leak or junctional conductance and Vm is the momentary membrane potential of a target cell and its coupled partner. The reversal potential for the leak current (Eleak) was arbitrarily fixed to 3 mV below the lowest natural resting membrane potential of the recorded neuron. Any G > 0 should be interpreted as an artificial conductance augmenting the natural input conductance of a neuron (Gin) additively (i.e., in parallel circuit configuration). Conversely, any G < 0 acts to counteract the natural membrane conductance as long as |G| < Gin (Figure S1B).Dynamic clamp was also used to estimate the natural junctional conductance between two neurons. This estimation was obtained from the absolute value of the added artificial Gjunc. necessary to cancel (i.e., force to zero) the coupling coefficient of a cell pair as defined by the postjunctional neuron response to the intrasomatic injection of a negative current pulse (−10 nA, 2 s) into the prejunctional neuron (Figures 2D and 2F).When introducing an artificial conductance or a combination of two distinct conductances into a single cell or a pair of electrically coupled neurons, recording of the membrane potential and injection of the dynamic-clamp-generated current (or currents) were achieved using a two-electrode current-clamp technique (see above). When a dynamic-clamp-defined conductance or a combination of conductances was applied simultaneously to three neurons, a single-electrode current-clamp procedure was used. In this procedure, the resistance and capacitance of each electrode were carefully compensated before data acquisition. Although computational methods of compensation have been described for high-resistance electrodes [39, 40], such procedures were not employed in our experiments because preliminary data comparison of our two-electrode and single-electrode setups indicated that the manual compensation was sufficient to effectively remove electrode artifacts. In all cases, the dynamic-clamp-defined value of Gleak or Gjunc. was the same for all neurons or pairs of neurons recorded in a given preparation. In experiments where different Gleak or Gjunc. values were successively applied in the same preparation to test their gradual effect on cellular properties or motor pattern genesis, and to distinguish these effects from a potential “fatigue” of the in vitro preparation, a given artificial conductance was applied by alternating positive and negative values ranging from highest to lowest values.Data AnalysisIn accordance with previous studies of operant conditioning [12, 14], data on the cellular correlates of learning and how dynamic clamp modifies these correlates and corresponding BMP generation were collected from control and contingent in vitro preparations that expressed motor patterns at irregular and regular time intervals, respectively. The data were obtained during a 30 min period of tonic n.2,3 stimulation at the end of the posttraining test period, with the analysis of buccal motor activity beginning 10 min after stimulation onset. Repetitive BMPs elicited by this stimulation were considered to be either irregularly or regularly distributed in time if the corresponding autocorrelation histogram of the interpattern intervals could not be fitted or could be fitted, respectively, with statistical significance by a sinusoidal Gabor function.Individual BMPs were defined by an initial protraction phase composed of action potential discharge in I2 n. at >0.4 Hz for >1 s, followed by retraction and closure motor activity consisting of impulses occurring above baseline levels in n.2,1 and R n., respectively [14]. Incomplete patterns composed of a burst in I2 n. without subsequent changes in n.2,1 or R n. activity were not considered in the analysis. In dynamic-clamp procedures, the mean frequency and the coefficient of variation of BMP emissions were determined from the mean duration and standard error of the time intervals between ten successive onsets of the protraction phase of each motor pattern.Cellular analyses were performed on one, two, or three cells depending on the successful localization and identification of individuals of the B63/B30/B65 neuron subset. Data obtained on input conductance, excitability, electrical coupling, or motor pattern generation were not discarded when the series of tests was prematurely interrupted by an unexpected loss of an intracellular impalement and the subsequent inability to relocate the cell (or cells). To compare a similar number of data sets in the different experimental protocols, the order of successive tests on neuronal properties was modified depending on the occurrence of the random events described above and independently of the particular experimental group. Cells that expressed an initial resting membrane potential greater than −45 mV and/or an input conductance less than 2.5 MΩ were not used in the analyses.Correlations between changes in cellular properties or BMP genesis and dynamic-clamp-added Gleak or Gjunc. values were fitted by linear or nonlinear regressions. Nonlinear regressions, such as between the coupling coefficient (CC) and Gleak or Gjunc., were computed from the following equation equivalent to the electrical circuit of two electrically coupled neurons (Figure S3A):CC=1Gin+Gleak1Gnat.+Gjunc.+1Gin+Gleak,where Gin and Gnat. are the natural postjunctional input conductance and the natural junctional conductance, respectively. Estimation of the equation’s parameters and the statistical significance of the regression were determined by SigmaPlot software (Systat). Statistical comparisons between two independent data groups were made using the two-tailed Mann-Whitney test (T). Comparisons between two dependent data groups were made using the two-tailed Wilcoxon test (W). Comparisons between three or more dependent data groups were made using the two-tailed Friedman’s analysis of variance on ranks (χ2), whereas post hoc pairwise multiple comparisons were made using the nonparametric Newman-Keuls multiple range test (q). Nonparametric tests were used because of the departure from a normal distribution and/or the heterogeneity of the variances of the sampled data.Author ContributionsF.S. implemented the dynamic-clamp technique and contributed to the design of the experiments and the writing of the manuscript. A.B. performed some experiments and contributed to the writing of the manuscript. J.S. cowrote the manuscript. A.A.P. contributed to the writing of the manuscript. R.N. contributed to the design of the experiments, performed some experiments, and cowrote the manuscript.AcknowledgmentsThis research was supported by an NSF Interdisciplinary Graduate Education and Research Program grant (F.S.), a doctoral studentship from the French Ministère de l’Enseignement Supérieur et de la Recherche (A.B.), and BRAIN grants ANR-10-LABX-43 and ANR-10-IDEX-03-02. We thank Lionel Parra-Iglesias for assistance with animal maintenance.Supplemental InformationSupplemental Information includes four figures and Supplemental Experimental Procedures and can be found with this article online at http://dx.doi.org/10.1016/j.cub.2014.03.004.Supplemental InformationDocument S1. Figures S1–S4 and Supplemental Experimental ProceduresDocument S2. Article plus Supplemental InformationReferences1B.J.EverittT.W.RobbinsNeural systems of reinforcement for drug addiction: from actions to habits to compulsionNat. 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