A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network.

Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.

Scaling of buccal mass growth and muscle activation determine the duration of feeding behaviours in the marine mollusc Aplysia californica.

The mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviours, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e. always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should scale identically (proportional to mass2/3), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioural duration. We tested this hypothesis by measuring the duration of feeding behaviours in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as A. californica attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviours scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behaviour, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro-computed tomography (micro-CT) scans of buccal masses. Consequently, larger A. californica did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioural durations in quasistatic systems.

Intracellular neural control of an active feeding structure in Aplysia using a carbon fiber electrode array.

BACKGROUND: To study neural control of behavior, intracellular recording and stimulation of many neurons in freely moving animals would be ideal. However, current technologies limit the number of neurons that can be monitored and manipulated. A new technology has become available for intracellular recording and stimulation which we demonstrate in the tractable nervous system of Aplysia. NEW METHOD: Carbon fiber electrode arrays (whose tips are coated with platinum-iridium) were used with an in vitro feeding preparation to intracellularly record from and to control the activity of multiple neurons during feeding movements. RESULTS: In an in vitro feeding preparation, the carbon fiber electrode arrays recorded action potentials and subthreshold synaptic potentials during feeding movements. Depolarizing or hyperpolarizing currents activated or inhibited identified neurons (respectively), manipulating the movements of the feeding apparatus. COMPARISON WITH EXISTING METHOD(S): Standard glass microelectrodes that are commonly used for intracellular recording are stiff, liable to break in response to movement, and require many micromanipulators to be precisely positioned. In contrast, carbon fiber arrays are less sensitive to movement, but are capable of multiple channels of intracellular recording and stimulation. CONCLUSIONS: Carbon fiber arrays are a novel technology for intracellular recording that can be used in moving preparations. They can record both action potentials and synaptic activity in multiple neurons and can be used to stimulate multiple neurons in complex patterns.

Fabrication and Validation of Sub-Cellular Carbon Fiber Electrodes.

Multielectrode arrays for interfacing with neurons are of great interest for a wide range of medical applications. However, current electrodes cause damage over time. Ultra small carbon fibers help to address issues but controlling the electrode site geometry is difficult. Here we propose a methodology to create small, pointed fiber electrodes (SPFe). We compare the SPFe to previously made blowtorched fibers in characterization. The SPFe result in small site sizes [Formula: see text] with consistently sharp points (20.8 ± 7.64°). Additionally, these electrodes were able to record and/or stimulate neurons multiple animal models including rat cortex, mouse retina, Aplysia ganglia and octopus axial cord. In rat cortex, these electrodes recorded significantly higher peak amplitudes than the traditional blowtorched fibers. These SPFe may be applicable to a wide range of applications requiring a highly specific interface with individual neurons.

Tutorial: using NEURON for neuromechanical simulations.

The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models. Code available at: https://github.com/fietkiewicz/PointerBuilder.

Combining voltage-sensitive dye, carbon fiber array, and extracellular nerve electrodes using a 3-D printed recording chamber and manipulators.

BACKGROUND: The analyses of neuronal circuits require high-throughput technologies for stimulating and recording many neurons simultaneously with single-neuron precision. Voltage-sensitive dyes (VSDs) have enabled the monitoring of membrane potentials of many (10-100 s) neurons simultaneously. Carbon fiber electrode (CFE) arrays allow for stimulation and recording of many neurons simultaneously, including intracellularly. NEW METHOD: Combining CFE with VSD leverages the advantages of both technologies, allowing for stimulation of single neurons while recording the activity of the entire network. 3-D printing technology was used to develop a chamber to simultaneously perform VSD imaging, CFE array recording, and extracellular recording from individual glass electrodes. RESULTS: Aplysia buccal ganglia were stained with VSD and imaged while also recording using a CFE array and extracellular nerve electrodes. Coincident spiking activity was recorded by VSD, CFE, and extracellular nerve electrodes. Current injection with CFE electrodes could activate and inhibit individual neurons as detected by VSD and nerve recordings. COMPARISON TO EXISTING METHODS: The large size of traditional manipulators limits the number of electrodes used and the number of neurons recorded during an experiment. Here we present a method to build a 3-D printed recording chamber that includes a 3-axis micromanipulator to position a CFE array and eight 2-axis manipulators to position eight extracellular electrodes. CONCLUSIONS: 3-D printing technology can be used to build a custom recording chamber and micromanipulators. Combining these technologies allows for the direct modulation of the activity of neurons while recording the activity of 100 s of neurons simultaneously.

Modular Design of a Polymer-Bilayer-Based Mechanically Compliant Worm-Like Robot.

Soft earthworm-like robots that exhibit mechanical compliance can, in principle, navigate through uneven terrains and constricted spaces that are inaccessible to traditional legged and wheeled robots. However, unlike the biological originals that they mimic, most of the worm-like robots reported to date contain rigid components that limit their compliance, such as electromotors or pressure-driven actuation systems. Here, a mechanically compliant worm-like robot with a fully modular body that is based on soft polymers is reported. The robot is composed of strategically assembled, electrothermally activated polymer bilayer actuators, which are based on a semicrystalline polyurethane with an exceptionally large nonlinear thermal expansion coefficient. The segments are designed on the basis of a modified Timoshenko model, and finite element analysis simulation is used to describe their performance. Upon electrical activation of the segments with basic waveform patterns, the robot can move through repeatable peristaltic locomotion on exceptionally slippery or sticky surfaces and it can be oriented in any direction. The soft body enables the robot to wriggle through openings and tunnels that are much smaller than its cross-section.

Selective Infrared Neural Inhibition Can Be Reproduced by Resistive Heating.

OBJECTIVES: Small-diameter afferent axons carry various sensory signals that are critical for vital physiological conditions but sometimes contribute to pathologies. Infrared (IR) neural inhibition (INI) can induce selective heat block of small-diameter axons, which holds potential for translational applications such as pain management. Previous research suggested that IR-heating-induced acceleration of voltage-gated potassium channel kinetics is the mechanism for INI. Therefore, we hypothesized that other heating methods, such as resistive heating (RH) in a cuff, could reproduce the selective inhibition observed in INI. MATERIALS AND METHODS: We conducted ex vivo nerve-heating experiments on pleural-abdominal connective nerves of Aplysia californica using both IR and RH. We fabricated a transparent silicone nerve cuff for simultaneous IR heating, RH, and temperature measurements. Temperature elevations (ΔT) on the nerve surface were recorded for both heating modalities, which were tested over a range of power levels that cover a similar ΔT range. We recorded electrically evoked compound action potentials (CAPs) and segmented them into fast and slow subcomponents on the basis of conduction velocity differences between the large and small-diameter axonal subpopulations. We calculated the normalized inhibition strength and inhibition selectivity index on the basis of the rectified area under the curve of each subpopulation. RESULTS: INI and RH showed a similar selective inhibition effect on CAP subcomponents for slow-conducting axons, confirmed by the inhibition probability vs ΔT dose-response curve based on approximately 2000 CAP measurements. The inhibition selectivity indexes of the two heating modalities were similar across six nerves. RH only required half the total electrical power required by INI to achieve a similar ΔT. SIGNIFICANCE: We show that selective INI can be reproduced by other heating modalities such as RH. RH, because of its high energy efficiency and simple design, can be a good candidate for future implantable neural interface designs.

Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators.

This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model's stability can be predicted by trends in the CPG's phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion.

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems.

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al. in J Comput Neurosci 38(1):25-51, 2015; Lyttle et al. in Biol Cybern 111(1):25-47, 2017). We established in (Wang et al. in SIAM J Appl Dyn Syst 20(2):701-744, 2021. https://doi.org/10.1137/20M1344974 ) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates' hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

Analyzing Modeled Torque Profiles to Understand Scale-Dependent Active Muscle Responses in the Hip Joint.

Animal locomotion is influenced by a combination of constituent joint torques (e.g., due to limb inertia and passive viscoelasticity), which determine the necessary muscular response to move the limb. Across animal size-scales, the relative contributions of these constituent joint torques affect the muscular response in different ways. We used a multi-muscle biomechanical model to analyze how passive torque components change due to an animal's size-scale during locomotion. By changing the size-scale of the model, we characterized emergent muscular responses at the hip as a result of the changing constituent torque profile. Specifically, we found that activation phases between extensor and flexor torques to be opposite between small and large sizes for the same kinematic motion. These results suggest general principles of how animal size affects neural control strategies. Our modeled torque profiles show a strong agreement with documented hindlimb torque during locomotion and can provide insights into the neural organization and muscle activation behavior of animals whose motion has not been extensively documented.

Use of an invertebrate animal model (Aplysia californica) to develop novel neural interfaces for neuromodulation.

New tools for monitoring and manipulating neural activity have been developed with steadily improving functionality, specificity, and reliability, which are critical both for mapping neural circuits and treating neurological diseases. This review focuses on the use of an invertebrate animal, the marine mollusk Aplysia californica, in the development of novel neurotechniques. We review the basic physiological properties of Aplysia neurons and discuss the specific aspects that make it advantageous for developing novel neural interfaces: First, Aplysia nerves consist only of unmyelinated axons with various diameters, providing a particularly useful model of the unmyelinated C fibers in vertebrates that are known to carry important sensory information, including those that signal pain. Second, Aplysia's neural tissues can last for a long period in an ex vivo experimental setup. This allows comprehensive tests such as the exploration of parameter space on the same nerve to avoid variability between animals and minimize animal use. Third, nerves in large Aplysia can be many centimeters in length, making it possible to easily discriminate axons with different diameters based on their conduction velocities. Aplysia nerves are a particularly good approximation of the unmyelinated C fibers, which are hard to stimulate, record, and differentiate from other nerve fibers in vertebrate animal models using epineural electrodes. Fourth, neurons in Aplysia are large, uniquely identifiable, and electrically compact. For decades, researchers have used Aplysia for the development of many novel neurotechnologies. Examples include high-frequency alternating current (HFAC), focused ultrasound (FUS), optical neural stimulation, recording, and inhibition, microelectrode arrays, diamond electrodes, carbon fiber microelectrodes, microscopic magnetic stimulation and magnetic resonance electrical impedance tomography (MREIT). We also review a specific example that illustrates the power of Aplysia for accelerating technology development: selective infrared neural inhibition of small-diameter unmyelinated axons, which may lead to a translationally useful treatment in the future. Generally, Aplysia is suitable for testing modalities whose mechanism involves basic biophysics that is likely to be similar across species. As a tractable experimental system, Aplysia californica can help the rapid development of novel neuromodulation technologies.

Carbon fiber electrodes for intracellular recording and stimulation.

Objective.To understand neural circuit dynamics, it is critical to manipulate and record many individual neurons. Traditional recording methods, such as glass microelectrodes, can only control a small number of neurons. More recently, devices with high electrode density have been developed, but few of them can be used for intracellular recording or stimulation in intact nervous systems. Carbon fiber electrodes (CFEs) are 8µm-diameter electrodes that can be assembled into dense arrays (pitches ⩾ 80µm). They have good signal-to-noise ratios (SNRs) and provide stable extracellular recordings both acutely and chronically in neural tissuein vivo(e.g. rat motor cortex). The small fiber size suggests that arrays could be used for intracellular stimulation.Approach.We tested CFEs for intracellular stimulation using the large identified and electrically compact neurons of the marine molluskAplysia californica. Neuron cell bodies inAplysiarange from 30µm to over 250µm. We compared the efficacy of CFEs to glass microelectrodes by impaling the same neuron's cell body with both electrodes and connecting them to a DC coupled amplifier.Main results.We observed that intracellular waveforms were essentially identical, but the amplitude and SNR in the CFE were lower than in the glass microelectrode. CFE arrays could record from 3 to 8 neurons simultaneously for many hours, and many of these recordings were intracellular, as shown by simultaneous glass microelectrode recordings. CFEs coated with platinum-iridium could stimulate and had stable impedances over many hours. CFEs not within neurons could record local extracellular activity. Despite the lower SNR, the CFEs could record synaptic potentials. CFEs were less sensitive to mechanical perturbations than glass microelectrodes.Significance.The ability to do stable multi-channel recording while stimulating and recording intracellularly make CFEs a powerful new technology for studying neural circuit dynamics.

Optimizing thermal block length during infrared neural inhibition to minimize temperature thresholds.

Objective. Infrared neural inhibition (INI) is a method of blocking the generation or propagation of neural action potentials through laser heating with wavelengths strongly absorbed by water. Recent work has identified that the distance heated along axons, the block length (BL), modulates the temperature needed for inhibition; however, this relationship has not been characterized. This study explores how BL during INI can be optimized towards minimizing its temperature threshold.Approach. To understand the relationship between BL and the temperature required for INI, excised nerves fromAplysia californicawere laser-heated over different lengths of axon during electrical stimulation of compound action potentials. INI was provided by irradiation (λ= 1470 nm) from a custom probe (n= 6 nerves), and subsequent validation was performed by providing heat block using perfused hot media over nerves (n= 5 nerves).Main Results. Two BL regimes were identified. Short BLs (thermal full width at half maximum (tFWHM) = 0.81-1.13 mm) demonstrated that increasing the tFWHM resulted in lower temperature thresholds for INI (p< 0.0125), while longer BLs (tFWHM = 1.13-3.03 mm) showed no significant change between the temperature threshold and tFWHM (p> 0.0125). Validation of this longer regime was performed using perfused hot media over different lengths of nerves. This secondary heating method similarly showed no significant change (p> 0.025) in the temperature threshold (tFWHM = 1.25-4.42 mm).Significance. This work characterized how the temperature threshold for neural heat block varies with BL and identified an optimal BL around tFWHM = 1.13 mm which minimizes both the maximum temperature applied to tissue and the volume of tissue heated during INI. Understanding how to optimally target lengths of nerve to minimize temperature during INI can help inform the design of devices for longitudinal animal studies and human implementation.

Isotonic ion replacement can lower the threshold for selective infrared neural inhibition.

Significance: Infrared (IR) inhibition can selectively block peripheral sensory nerve fibers, a potential treatment for autonomic-dysfunction-related diseases (e.g., neuropathic pain and interstitial cystitis). Lowering the IR inhibition threshold can increase its translational potentials. Aim: Infrared induces inhibition by enhancing potassium channel activation. We hypothesized that the IR dose threshold could be reduced by combining it with isotonic ion replacement. Approach: We tested the IR inhibition threshold on the pleural-abdominal connective of Aplysia californica. Using a customized chamber system, the IR inhibition was applied either in normal saline or in isotonic ion-replaced saline, which could be high glucose saline, high choline saline, or high glucose/high choline saline. Each modified saline was at a subthreshold concentration for inhibiting neural conduction. Results: We showed that isotonically replacing ions in saline with glucose and/or choline can reduce the IR threshold and temperature threshold of neural inhibition. Furthermore, the size selectivity of IR inhibition was preserved when combined with high glucose/high choline saline. Conclusions: The present work of IR inhibition combined with isotonic ion replacement will guide further development of a more effective size-selective IR inhibition modality for future research and translational applications.

Shape versus timing: linear responses of a limit cycle with hard boundaries under instantaneous and static perturbation.

When dynamical systems that produce rhythmic behaviors operate within hard limits, they may exhibit limit cycles with sliding components, that is, closed isolated periodic orbits that make and break contact with a constraint surface. Examples include heel-ground interaction in locomotion, firing rate rectification in neural networks, and stick-slip oscillators. In many rhythmic systems, robustness against external perturbations involves response of both the shape and the timing of the limit cycle trajectory. The existing methods of infinitesimal phase response curve (iPRC) and variational analysis are well established for quantifying changes in timing and shape, respectively, for smooth systems. These tools have recently been extended to nonsmooth dynamics with transversal crossing boundaries. In this work, we further extend the iPRC method to nonsmooth systems with sliding components, which enables us to make predictions about the synchronization properties of weakly coupled stick-slip oscillators. We observe a new feature of the isochrons in a planar limit cycle with hard sliding boundaries: a nonsmooth kink in the asymptotic phase function, originating from the point at which the limit cycle smoothly departs the constraint surface, and propagating away from the hard boundary into the interior of the domain. Moreover, the classical variational analysis neglects timing information and is restricted to instantaneous perturbations. By defining the "infinitesimal shape response curve" (iSRC), we incorporate timing sensitivity of an oscillator to describe the shape response of this oscillator to parametric perturbations. In order to extract timing information, we also develop a "local timing response curve" (lTRC) that measures the timing sensitivity of a limit cycle within any given region. We demonstrate in a specific example that taking into account local timing sensitivity in a nonsmooth system greatly improves the accuracy of the iSRC over global timing analysis given by the iPRC.

An Analysis of Peristaltic Locomotion for Maximizing Velocity or Minimizing Cost of Transport of Earthworm-Like Robots.

Earthworm-like peristaltic locomotion has been implemented in >50 robots, with many potential applications in otherwise inaccessible terrain. Design guidelines for peristaltic locomotion have come from observations of biology, but robots have empirically explored different structures, actuators, and control waveform shapes than those observed in biological organisms. In this study, we suggest a template analysis based on simplified segments undergoing beam deformations. This analysis enables calculation of the minimum power required by the structure for locomotion and maximum speed of locomotion. Thus, design relationships are shown that apply to peristaltic robots and potentially to earthworms. Specifically, although speed is maximized by moving as many segments as possible, cost of transport (COT) is optimized by moving fewer segments. Furthermore, either soft or relatively stiff segments are possible, but the anisotropy of the stiffnesses is important. Experimentally, we show on our earthworm robot that this method predicts which control waveforms (equivalent to different gaits) correspond to least input power or to maximum velocity. We extend our analysis to 150 segments (similar to that of earthworms) to show that reducing COT is an alternate explanation for why earthworms have so few moving segments. The mathematical relationships developed here between structural properties, actuation power, and waveform shape will enable the design of future robots with more segments and limited onboard power.

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.

Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.

Multiple Local Synaptic Modifications at Specific Sensorimotor Connections after Learning Are Associated with Behavioral Adaptations That Are Components of a Global Response Change.

Learning causes local changes in synaptic connectivity and coordinated, global changes affecting many aspects of behavior. How do local synaptic changes produce global behavioral changes? In the hermaphroditic mollusc Aplysia, after learning that food is inedible, memory is expressed as bias to reject a food and to reduce responses to that food. We now show that memory is also expressed as an increased bias to reject even a nonfood object. The increased bias to rejection is partially explained by changes in synaptic connections from primary mechanoafferents to five follower neurons with well defined roles in producing different feeding behaviors. Previously, these mechanoafferents had been shown to play a role in memory consolidation. Connectivity changes differed for each follower neuron: the probability that cells were connected changed; excitation changed to inhibition and vice versa; and connection amplitude changed. Thus, multiple neural changes at different sites underlie specific aspects of a coordinated behavioral change. Changes in the connectivity between mechanoafferents and their followers cannot account for all of the behavioral changes expressed after learning, indicating that additional synaptic sites are also changed. Access to the circuit controlling feeding can help determine the logic and cellular mechanisms by which multiple local synaptic changes produce an integrated, global change in behavior.SIGNIFICANCE STATEMENT How do local changes in synapses affect global behavior? Studies on invertebrate preparations usually examine synaptic changes at specific neural sites, producing a specific behavioral change. However, memory may be expressed by multiple behavioral changes. We report that a change in behavior after learning in Aplysia is accomplished, in part, by regulating connections between mechanoafferents and their synaptic followers. For some followers, the connection probabilities change; for others, the connection signs are reversed; in others, the connection strength is modified. Thus, learning produces changes in connectivity at multiple sites, via multiple synaptic mechanisms that are consistent with the observed behavioral change.