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date: 21 February 2018

Recent Trends in Invertebrate Neuroscience

Abstract and Keywords

This article presents a selective presentation of several notable trends in invertebrate neuroscience, which are intended to illustrate the central tenant that, essentially, basic invertebrate neuroscience and basic vertebrate neuroscience are converging to a remarkable degree. That is, the basic principles of cellular, network, and behavioral neuroscience are increasingly congruent within eukaryote phyla, with the notable exceptions of work that is explicitly clinical or concerned with pest control. The historical segregation of invertebrate and vertebrate neuroscience is of decreasing relevance and utility. An increasing literature has arisen that points out common structural and mechanistic themes across the invertebrate–vertebrate (IV) boundary. Among many examples, common neural circuit motifs play a causal role in decision-making circuits responsible for activating innate social behaviors in both Drosophila melanogaster and mice. Charles Darwin said, “It is absurd to talk of one animal being higher than another.” If some cephalopods are conscious, where do we draw the line?

Keywords: invertebrate neuroscience, invertebrate–vertebrate boundary, cephalopod, consciousness, connectomics, invertebrate cognition, learning and memory, in vivo recording, genetic model systems, computational neuroscience

What follows is a selective presentation of several notable trends in the recent history of invertebrate neuroscience, which are intended to illustrate the central tenant that, essentially, basic invertebrate neuroscience and basic vertebrate neuroscience are converging to a remarkable degree. That is, the basic principles of cellular, network, and behavioral neuroscience are increasingly congruent within eukaryote phyla, with the notable exceptions of work that is explicitly clinical in nature or concerned with pest control. The historical segregation of invertebrate and vertebrate neuroscience is of decreasing relevance and utility. In the modern era, young neuroscience researchers are able to pursue their experimental and theoretical goals using experimental preparations drawn from a wide swath of the animal kingdom (North & Greenspan, 2007), from chordates (Yamamoto & Vernier, 2011) to jellyfish (Johnson & Wuensch, 1994; Katsuki & Greenspan, 2013) and cnidarians (Arendt, Tosches, & Marlow, 2016).

A modern understanding of the development of invertebrate neuroscience and its increasing convergence with vertebrate neuroscience logically starts with the publication of the monumental work by Bullock and Horridge in 1965. This critical and detailed summary of our knowledge of the neuroscience of essentially all the invertebrate phyla was a watershed event that contains numerous examples of the generality of cellular, synaptic, and network properties between invertebrate and vertebrate nervous systems.

The most dramatic example of this generality is surely the experimental dissection and quantitative modeling of the biophysical basis of action potential generation in the squid giant axon by Hodgkin, Huxley, and Katz (Hodgkin & Huxley, 1952a, 1952b, 1952c, 1952d, 1952e; Hodgkin, Huxley, & Katz, 1952), for which Hodgkin and Huxley were awarded the Nobel Prize, shared with John Eccles, in 1963. With great hindsight it is evident that the choice of a squid giant axon as the experimental preparation was fortuitous not only because of the large size of the axon, as first noted by J. Z. Young (Young, 1936a, 1936b, 1939; see also Keynes, 2005), but also because the squid axon has a limited number of types of ionic channels critical for action potential generation in its membrane. Later work in a variety of both vertebrate and invertebrate preparations revealed a remarkable diversity of types and subtypes of sodium, potassium, and calcium ion channels, which likely would have confounded the elegant work of Hodgkin and Huxley had they been present in the squid axon membrane. Remarkably, despite over 60 years of intense and detailed biophysical analysis, there are still aspects of the interactions between membrane voltage and the dynamics of sodium and potassium channel function as yet unresolved (Hoshi & Armstrong, 2015; Moore, 2015). The squid giant axon is also the most widely known exception to the neuron doctrine, as formulated in 1891 by Wilhelm Waldeyer (Shepherd, 2016), since the giant axon is formed by the fusion of the axons of several hundred neurons with their somata in the giant fiber lobe of the stellate ganglion (Young, 1936a). Interestingly the cell bodies of the neurons in the stellate ganglion contributing their axons to the formation of the giant axon contain two types of calcium currents not found in the giant axon itself (Llano & Bookman, 1986).

The biophysical studies of Hodgkin and Huxley on the mechanism of action potential production by the squid giant axon also represent a truly remarkable achievement in computational neuroscience, and they presaged the growing importance of computational approaches to problems at every level of complexity in cellular (Moore & Westerfield, 1983), network (Ermentrout, Wang, Flores, & Gelperin, 2004; Marder & Taylor, 2011) and behavioral (Brody & Hanks, 2016) neuroscience. Models of biochemical network (Hao, Yang, & Bi, 2016), biophysical network (Brembs, Baxter, & Byrne, 2004; Song, Smolen, Av-Ron, Baxter, & Byrne, 2006), and neural network dynamics (Lamb & Calabrese, 2012; Drion, O’Leary, & Marder, 2015; Marder, Goeritz, & Otopalik, 2015) provide additional examples of the informational cross-fertilization between vertebrate and invertebrate nervous systems.

Advent of Molecular and Genetic Tools

The avalanche of molecular and genetic tools unleashed by recent advances in our understanding of the molecular and genetic basis of neuron function is nothing short of spectacular. These tools are applicable to both vertebrates and invertebrates. The central reason for this is the phylogenetic universality of the genetic code, elucidated by work in several laboratories in the 1950s and 1960s (Crick, 1968; Hopfield, 1978). These advances, coupled with elucidation of the genomes of genetic model systems such as the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans, have allowed the construction of genetically encoded modifications of selected small sets of neurons, which then allows tests to be performed elucidating the roles of these sets of neurons in discrete behavioral responses. One example serves to illustrate the power of these modern genetic and molecular tools in dissecting the specific function of small groups of neurons. In Drosophila, male flies sing a species-specific song during courtship of a female (Coen, Xie, Clemens, & Murthy, 2016). Small sets of neurons in the brain and thoracic ganglia of the male fly, e.g., the P1 neurons, have been identified by genetic techniques as critical for song production (von Philipsborn et al., 2011). Localization of these song-critical neurons allows genetically encoded red light-sensitive membrane ion channels (e.g., ReaChR) to be specifically expressed in the song-critical neurons (Inagaki et al., 2014), including the P1 neurons. Illuminating a male fly expressing ReaChR (J. Y. Lin, Knutsen, Muller, Kleinfeld, & Tsien, 2013) in its P1 neurons with red light now elicits a bout of courtship song production (Coen & Murthy, 2016). The repertoire of genetically encoded indicators and controllers of neural activity is rapidly expanding (M. Z. Lin & Schnitzer, 2016), and, in fact, ReaChR itself has very recently been superseded by another red-shifted photoprotein called Chrimson, isolated after screening opsins from over 100 species of algae (Klapoetke et al., 2014).

Blurring the Invertebrate–Vertebrate Boundary

An increasing literature has arisen that points out common structural and mechanistic themes across the invertebrate–vertebrate (IV) boundary. For example, common neural circuit motifs play a causal role in decision-making circuits responsible for activating innate social behaviors in both Drosophila melanogaster and mice (Anderson, 2016). In both Mus musculus and in D. melanogaster, restricted groups of neurons have been identified that directly impact the probability of occurrence of both mating and aggression. These same small groups of neurons also have dramatic effects on the internal state variables that control the probability of occurrence of these social behaviors. Of course, these observations do not imply homology between these circuit mechanisms as these common functional motifs could be the result of convergent evolution in these two evolutionary lineages. In either event the identification of these common circuit motifs provide yet another example of functional insights that transcend the IV boundary.

Another attempt at bridging the IV divide results from recent work aimed to develop an objective framework for the study of brain states corresponding to emotions (Anderson & Adolphs, 2014). The comparative study of emotions has a long history, most notably including contributions from Charles Darwin (1872) and the foundational figures of neuroethology Vincent Dethier (1976), Niko Tinbergen (1951), and Konrad Lorenz (Lorenz & Leyhausen, 1973). Building a bridge across the IV divide requires two essential steps. First, a conceptual leap is needed to imagine that both nonhuman vertebrates and at least some invertebrates could display brain states either analogous or homologous to what in humans we label as emotions. The second step is to construct an operational definition that can be applied broadly across the IV divide. Anderson and Adolphs (2014) address this second step by considering emotions as brain states displaying general attributes they refer to as “primitives,” which may have general properties shared across wide phyletic boundaries. It should be noted in any discussion of these issues that some authors consider emotions to be essentially subjective states that cannot be usefully studied in animals (LeDoux, 2012).

Another recent attempt to draw attention to common circuit motifs spanning the IV divide takes a broad comparative approach to the analysis of reward circuits (Scaplen & Kaun, 2016). One example of functional commonality from flies to primates is the central role of dopamine, known to signal reward prediction error in rats (Schultz, 2016; Takahashi, Langdon, Niv, & Schoenbaum, 2016) and to play a critical role in Drosophila in circuits controlling satiety and appetitive memory function (Perisse et al., 2016). In rats, optogenetic stimulation of dopamine neurons in the ventral tegmental area is sufficient to elicit robust intracranial self-stimulation (Witten et al., 2011). Similarly, optogenetic activation of a small set of four paired dopamine neurons in the Drosophila larva serves as a reward signal sufficient to mediate odor-sugar associative learning (Rohwedder et al., 2016).

The same broad comparative approach has highlighted the probability of a deep homology between the arthropod central complex and the vertebrate basal ganglia, specified by an evolutionarily conserved genetic program (Strausfeld & Hirth, 2013). The similarity of the two structures extends to their downstream effects on neural activity and the use of gamma-amino-butyric acid (GABA) as an inhibitory neurotransmitter and dopamine as a neuromodulator. This IV-spanning comparative approach has recently been extended to the control of locomotor behavior (Gomez-Marin et al., 2016).

A similar circuit motif has been revealed recently in visual processing circuits between insect and primate during visual saccades (A. J. Kim, Fitzgerald, & Maimon, 2015). This visual circuit motif causes suppression of visual processing during rapid eye movements. More generally, the application of control theory has illuminated fundamental and generally applicable aspects of visual and sensory processing during flight in fruit flies (M. H. Dickinson & Muijres, 2016).

Yet another example emerged from recent studies of the genes associated with aggression in honeybees and mammals, which found that these genes are conserved across the IV divide (H. Liu, Robinson, & Jakobsson, 2016).

Computational Approaches

Models are prostheses for the imagination.

—Murray Gell-Mann

The technology of modern computation has allowed a vast expansion of the domains of neuroscience in which computational models, exhibiting a variety of levels of specificity in their incorporation of neuronal details, play a central role. The widespread use of computational models to both organize known interactions between identified structures or processes and predict the outcomes of future measurements is central to progress in neuroscience on both sides of the IV divide. As recently pointed out by Churchland and Abbott (2016) advances in theoretical neuroscience offer approaches to understanding how activity in hundreds or thousands of neurons recorded during ongoing behavior (Grover, Katsuki, & Greenspan, 2016; Nguyen et al., 2016; Venkatachalam et al., 2016) may reveal insights into causative mechanisms underlying the generation of a behavior. The quantitative and computational analysis of behavior (Egnor & Branson, 2016) is a logical starting point for a functional analysis, as shown by recent elegant studies in Drosophila adults (Coen et al., 2014; G. J. Berman, Bialek, & Shaevitz, 2016) and larvae (Gomez-Marin, Partoune, Stephens, & Louis, 2012), C. elegans (Yemini, Jucikas, Grundy, Brown, & Schafer, 2013; Broekmans, Rodgers, Ryu, & Stephens, 2016), and mice (Wiltschko et al., 2015).

The study of the organization of behaviors emitted by adult Drosophila by Schaevitz and collaborators (G. J. Berman et al., 2016) is a particularly instructive example of the synergy of advanced technology and sophisticated mathematical analysis based on information theory. High-speed imaging was used to capture the full range of isolated adult fly behaviors. From the aligned images a two-dimensional map was constructed that automatically identified stereotyped actions and postures (G. J. Berman, Choi, Bialek, & Shaevitz, 2014). Transition probabilities between the stereotyped actions or postures were then derived to provide a complete description of the sequences of movements and actions performed by the average isolated fly in the arena that was used to collect the data. The methods of information theory showed that a hierarchical representation of the fly’s actions and postures gave the best predictions of the fly’s future behavioral state based on its present state. Critically, behavioral transition probabilities were found to be controlled at varying time scales, from milliseconds to seconds. This study is a paradigmatic example of computational ethology (Anderson & Perona, 2014).

A recent review has highlighted how computational approaches can inform our understanding of how nervous systems create persistent states for memory storage and clarifies the overlap of these issues with computer science and information theory (Chaudhuri & Fiete, 2016).

Focus on Genetic Model Systems

The power of modern molecular and genetic methods for creating brain donors with anatomically defined sets of neurons containing either reporters of neural activity or controllers of neural activity has driven an increased focus on a small set of experimental subjects for which these molecular and genetic tools can be readily applied (B. H. White, 2016). This group includes mice, zebra fish, fruitflies (Venken et al., 2016), and the nematode Caenorhabditis elegans (B. H. White, 2016), although recent efforts are expanding this list to include rats (Witten et al., 2011). More generally, gene editing techniques of several types have been applied to a variety of insects (Huang, Liu, & Rong, 2016; Reid & O’brochta, 2016), including crop pests (Koutroumpa et al., 2016), locusts (Li et al., 2016), honeybees (Kohno, Suenami, Takeuchi, Sasaki, & Kubo, 2016), monarch butterflies (Markert et al., 2016; Overcash & Adelman, 2016), the silkworm Bombyx mori (Wei et al., 2014; Ling et al., 2015), mosquitos (Kistler, Vosshall, & Matthews, 2015), and crickets (Awata et al., 2015). Gene editing techniques have also been applied to the marine annelid Platynereis dumeilii, an emerging model system for the study of circalunar reproductive timing (Bannister et al., 2014) and the sea anemone Nematostella vectensis (Chiodin & Ryan, 2016). This list is growing rapidly and provides a welcome countertrend to the recent focus on a very limited number of genetic model systems.

These developments to modify and control activity in selected subsets of functionally identified neurons dovetail with related efforts to record electrically and optically from large numbers (dozens to hundreds) of neurons in minimally restrained awake behaving animals. One version of optical recording relies on voltage-sensitive membrane dyes (Frost et al., 2015) or intracellular calcium indicators applied acutely (J. J. Chang, Gelperin, & Johnson, 1974). The computational issues involved in inferring spike production and spiking rates from intracellular calcium measurements are an area of active research (Theis et al., 2016). A second means to accomplish the same goal of wide-field imaging with cellular resolution uses genetically encoded indicators of neural activity (M. Z. Lin & Schnitzer, 2016), although for reasons stated earlier this approach is limited by the number of species in which it can be applied. Recent efforts in protein engineering have greatly improved the sensitivity and applicability of genetically encoded calcium indicators (Akerboom et al., 2012), and this trend will likely accelerate.

In Vivo Recording During Behavior

Increasingly, it has become clear that to understand the neural computations underlying decision making and the neural control of behavior, it is necessary to record cellular and synaptic interactions in the awake, behaving brain while the relevant computations are being carried out. This trend to study neural processing in awake, behaving brains is occurring on both sides of the IV divide, from mammals (Buzsaki et al., 2015; Resendez et al., 2016; Sofroniew, Flickinger, King, & Svoboda, 2016) to insects (Harrison et al., 2011; Hartbauer, Kruger, & Stieglitz, 2012; Tsang et al., 2012), including 2,000 site recordings in the Drosophila brain (Paulk, Zhou, Stratton, Liu, & van Swinderen, 2013). Multisite recordings of distributed neural activity pose stringent demands on the methods used for automatic or semiautomatic sorting of spikes into single-unit classes (Fournier, Mueller, Shein-Idelson, Hemberger, & Laurent, 2016; Harris, Quiroga, Freeman, & Smith, 2016), which itself is a very active area of research.

A particularly elegant and informative method to image calcium dynamics in brain regions and small groups (N = 20) of neurons in the freely walking Drosophila has recently been introduced (Grover et al., 2016). After implanting an optical window in the dorsal region of the head, flies expressing the genetically encoded calcium indicator GCaMP6s could be imaged in an arena fitted with three cameras—one to image the behavioral arena; one to image the fly; and one, steered by a galvanometer mirror-based tracking system, used to obtain fluorescent images of the brain. Proof of concept of the imaging system was provided by collecting images from male fly brains showing odor-elicited activity in olfactory projection neurons (Stocker, Heimbeck, Gendre, & deBelle, 1997; Grabe et al., 2016; Rybak et al., 2016) and courtship-elicited activity in the P1 interneuron group (N = 20), previously shown to be activated by contact with females (Clowney, Iguchi, Bussell, Scheer, & Ruta, 2015; Kohatsu & Yamamoto, 2015) and causally related to male mating song production (Coen & Murthy, 2016). The approach introduced by Grover et al., called flyception, is particularly well suited to identifying brain regions showing activity correlated with specific aspects of sensory processing and decision making, which can then be targeted genetically and electrophysiologically using a head-fixed preparation (Clemens, Girardin, et al., 2015; A. E. B. Chang, Vaughan, & Wilson, 2016; Matsuo et al., 2016).


The goal of connectomics is to specify with subcellular detail the complete wiring diagram of a selected volume of neural tissue. The initial work in this area focused on the mammalian retina (Helmstaedter et al., 2013), whereas more recent work has extended the approach to capitalize on the compact neural centers of invertebrates such as the medicinal leech Hirudo verbena (Pipkin, Bushong, Ellisman, & Kristan, 2016) and a visual circuit in the larva of the annelid Platynereis (Randel et al., 2014). Bargmann and Marder (2013) have made clear what additional information is needed beyond the connectome to achieve functional insights into the neural computations performed by the imaged tissue, particularly the roles of neuronal dynamics and neuromodulation. They suggest, based on comparison of findings in vertebrate retina and invertebrate ganglia, that insights into the functions of large vertebrate circuits could well emerge from studies of compact invertebrate circuits.

The recent work in connectomics aiming to specify the complete wiring diagram of a functionally defined neural circuit based on serial section electron microscopy has a long and informative history (Swanson & Lichtman, 2016), starting with computer-aided reconstruction of ganglia in the medicinal leech (Macagno, Levinthal, & Sobel, 1979) and the complete structure of the nervous system of C. elegans reconstructed from serial electron microscopic images (J. G. White, Southgate, Thomson, & Brenner, 1986). The paper by White et al. may rightly be considered the origin of modern connectomic research (Emmons, 2015). The introduction of serial block-face scanning electron microscopy by Denk and Horstmann (2004) opened the way to automatic collection of image stacks of several hundred microns thickness with image registration showing minimal (few nanometers) lateral position jitter. The analysis of these very large image stacks presents a formidable computational challenge. One creative response to this challenge involves a combination of crowdsourced manual annotation of serial images and machine-learning-based volume segmentation (Helmstaedter et al., 2013). The use of creative crowdsourced image annotation, exemplified by Eyewire, an online community of citizen neuroscientists (J. S. Kim et al., 2014), is expanding to deal with the annotation and analysis challenges posed by the enormous data sets generated by serial block-face imaging. These online annotation and analysis systems use a game-like format (Roskams & Popovic, 2016) and incorporate multiple methods of cross-validation of inputs from citizen scientists (Arganda-Carreras et al., 2015). These new crowdsourcing methods are essential because the manual tracing of images for many neural circuits of interest would require from hundreds to tens of thousands of person-years of effort (Helmstaedter, Briggman, & Denk, 2008; Helmstaedter, 2013). The image analysis toolbox has recently been augmented by the addition of two-photon microscopy to the set of analysis tools (Ragan et al., 2012) and several new methods for clearing of bulk brain samples—for example, CLARITY (Chung & Deisseroth, 2013), SeeDB (Ke, Fujimoto, & Imai, 2013), and 3DISCO (Erturk et al., 2012), among others. Not surprisingly, the initial application of analytical methods for connectomics in invertebrates has focused on genetic model systems such as C. elegans (see earlier) and Drosophila (Shih et al., 2015; Ng et al., 2016; Schneider-Mizell et al., 2016).

Acoustic Communication

Acoustic communication is used by compact nervous systems for many different purposes, such as predator avoidance, exemplified by the ultrasonic hearing sensitivity of moths listening for the hunting vocalizations of bats (Roeder & Treat, 1957; Roeder, 1964). The bat-moth acoustic arms race is proceeding apace on an evolutionary time scale (ter Hofstede & Ratcliffe, 2016). Acoustic communication is also used for courtship, as discussed previously, by male Drosophila (Kamikoucji & Ishikawa, 2016). More familiar examples of acoustic signals are commonly experienced outdoors on a summer evening. These are the calling songs of crickets and katydids (V.G. Dethier, 1992) whose acoustical analysis was pioneered by G. W. Pierce (1949), some of whose insect song recordings were made by a young V. G. Dethier.

The analyses of insect acoustic communication provide very instructive examples of the application of the principles of neuroethology, by which a behavior is studied and dissected both in its natural habitat in the field and in the controlled environment in the laboratory (Roeder, 1966). As one example of this approach, K. D. Roeder first identified the potential ultrasonic sensitivity of hawkmoths when the high-frequency squeak of a cork being withdrawn from a bottle caused nearby hawkmoths to flee instantly from their feeding sites on an adjoining flowering bush (Roeder & Treat, 1970). After much painstaking exploration, the hawkmoth ultrasonic detector was localized to a modified mouthpart (Roeder, 1972). The ears of noctuid moths have only two sensory cells per ear (Roeder, 1964), but the moth in flight will only turn away from a calling bat at such low intensities of bat-generated ultrasonic pulses that only the most sensitive of the two sensory cells, the A1 cell, is responding (Payne, Roeder, & Wallman, 1966). More recent work has focused on the central synaptic processing of the A1 spike trains. An identified interneuron, neuron 501, receives monosynaptic input from the A1 sensory cells with characteristics that suggest that responses of neuron 501 may provide a mechanism for discriminating between bat-generated and non-bat-generated sound sources (Boyan & Fullard, 1988). The central neural processing of auditory information relevant to making decisions about escape behavior in the moth and other insect nervous systems has recently been reviewed (Pollack, 2015). Acoustically triggered avoidance circuits in moths, crickets, and praying mantids share a common set of design features, including steep intensity-response functions, high firing rates, and rapid spike conduction (Pollack, 2016).

As an evolutionary aside, it has been found that when parasitic mites take up residence in the ears of a moth, they only inhabit one ear, thus leaving the host moth with some ability to avoid acoustically mediated predation (Treat, 1957). The first mite to colonize a particular moth apparently leaves a chemical trail leading to one ear, which subsequent colonizing mites follow. More recently it was shown that some moths in fact create very low-level ultrasonic courtship songs (Nakano et al., 2009; Reichard & Anderson, 2015) while other moth species produce warning sounds to advertise that they are unpalatable (Dunning, 1968) or can even produce acoustic signals for sonar jamming (Corcoran & Conner, 2012). It is worth noting that these diverse uses of acoustic communication channels resulted from field observations prior to experimental dissection in the laboratory.

The acoustic behavior of crickets, both sound reception and sound production, has been the subject of extensive analysis, most particularly resulting from the work of Franz Huber (1990), a founder of the field of neuroethology, together with a large group of colleagues and students stimulated by Huber’s vision (B. Hedwig, 2016). Fittingly, Huber’s 1990 paper is dedicated to another founding figure in neuroethology, Kenneth D. Roeder, some of whose work on acoustic communication in moths was mentioned earlier. Huber stresses the need to observe and measure behavior in the field before proceeding to a mechanistic analysis in the laboratory, an approach also championed by T. H. Bullock (11984). Crickets provide a system in which neural interactions of song production and song reception (B. G. Hedwig, 2016) can be studied in the same nervous system. The mechanistic analysis of cricket auditory interneurons, in particular the omega neuron, show that increases and decreases in intracellular calcium dynamics triggered by acoustic input can account for the time course of forward masking, a time-dependent modulation of auditory sensitivity (Sobel & Tank, 1994). More recent work has highlighted mechanisms for adaptive tuning in the cricket auditory pathway (Clemens, Rau, Hennig, & Hildebrandt, 2015). Again we find that comparisons of auditory systems across the I–V divide provide synergistic insights (Albert & Kozlov, 2016). An extremely useful compendium of work on many aspects of the cricket, including development, regeneration, and behavior, has recently appeared (Horch, Mito, Popadic, Ohuchi, & Noji, 2017).

No discussion of cricket acoustic communication would be complete without mention of the cricket predators that use the songs of crickets to locate a host on which to lay an egg (Cade, 1975) or a prey item to paralyze and take home to a nest site (Gnatzy & Heusslein, 1986). Ron Hoy, another foundational figure in neuroethology and particularly in insect acoustic communication, and colleagues, have analyzed in detail the behavioral adaptations of female Ormia ochracea, a parasitic fly that uses male cricket calling songs to locate and oviposit on a cricket host (Rosen, Levin, & Hoy, 2009). Because male crickets call at night, Ormia females flying at night are exposed to bat predation. Ormia females have developed sharply tuned acoustic detection circuitry to differentiate cricket calls from bat calls and have also developed an acoustic startle response that is only operative during flight and is triggered by bat cries. Surprisingly, the directional hearing ability of tiny Ormia adults was found to display a spatial precision fully comparable to the much larger auditory system of humans (Mason, Oshinsky, & Hoy, 2001). Comparisons of two species of parasitoid flies both localizing crickets acoustically served to clarify the nature of the acoustic filters employed, based on the range of prey species localized by the flies (Lakes-Harlan & Lehmann, 2015). Some of the antipredator behaviors of the cricket in response to predator attack are triggered by wind detectors on the paired cerci at the posterior end of the cricket (Baba & Ogawa, 2017), which synapse with giant fibers in the terminal abdominal ganglion capable of triggering escape locomotion (Yono & Aonuma, 2008).

Feeding Behavior

The regulation of feeding behavior has been explored in a number of compact nervous systems, including the medicinal leach Hirudo (Gaudry & Kristen, 2012; Wagenaar, 2015), the mosquito Aedes (Matthews, McBride, DeGennaro, Despo, & Vosshall, 2016), the fruitfly Drosophila (Pool & Scott, 2014; Zhan, Liu, & Zhu, 2016), Aplysia (Svensson, Evans, & Cropper, 2016), C. elegans (Cheong, Artyukhin, You, & Avery, 2015; Dalliere et al., 2016; Dillon, Holden-Dye, O’Connor, & Hopper, 2016), the marine annelid Platynereis (E. A. Williams, Conzelmann, & Jekely, 2015), Hydra (Alzugaray, Hernandez-Martinez, & Ronderos, 2016), Rhodnius (Defferrari, Orchard, & Large, 2016), and Locusta (Dillen, Chen, & Vanden Broeck, 2016), to name a few. The goals are to identify the extrinsic and intrinsic factors that interact to determine the probability of feeding, given the current and previous nutritional states of the animal. The most complete description of the neural control system for feeding in any compact nervous system is available for Phormia regina (Gelperin, 1971b; V. G. Dethier, 1976; Thomson, 1995), augmented and extended by an increasingly detailed picture for Drosophila melanogaster.

A brief outline of the neural control of feeding in Phormia provides a useful guide for comparison with other systems. The fly is equipped with three sets of contact chemoreceptors, tarsal, labellar, and interpseudotracheal papillae, that sequentially sample a potential food source and provide sensory inputs that determine, in combination with internal satiety monitors, whether the fly will ingest the food source, conditioned by the intensity of the chemosensory inputs. The internal satiety monitors are of two types, namely, mechanoreceptors responding to peristalsis in the foregut (Gelperin, 1967) and mechanoreceptors responding to tension changes in the abdominal nerves suspended over the food storage organ, the crop (Gelperin, 1971a). Disconnection of either set of mechanoreceptors from the brain leads to a phenotype called hyperphagia. Thus, at any given moment the probability of feeding is determined by the interaction of inputs from external chemoreceptors and input from internal mechanoreceptors as conditioned by prior nutritional state (Gelperin, 1971b). The current state of the balance between these external and internal factors is conveniently measured as the tarsal sucrose threshold for the proboscis extension reflex (PER) (Tully & Hirsch, 1983; Maeda, Tamotsu, Yamaoka, & Ozaki, 2015). A very hungry fly may have a tarsal sucrose threshold of less than 0.001 M sucrose, whereas a sated fly often has a PER threshold greater than 2M sucrose (V. G. Dethier & Chadwick, 1948). The integration of these external and internal factors to achieve metabolic homeostasis by adult Phormia is summarized in Figure 1. This same framework has been applied to understanding mammalian feeding control (K. W. Williams & Elmquist, 2012) and identifying genes in Drosophila involved in metabolic homeostasis (Nelson et al., 2016). The model has also been generalized to plant-eating insects (Simpson & Raubenheimer, 1996).

Recent Trends in Invertebrate NeuroscienceClick to view larger

Figure 1 Diagram to indicate the major components and interactions controlling metabolic homeostasis and feeding behavior in the black blowfly Phormia regina. The diagram omits many details of the neural circuitry and neuromodulators involved in the control and regulation of fly feeding, which are discussed in the text.

The chemoreceptors that provide input signals by which CNS circuits assess the suitability of a potential food have been the subject of extensive analysis, triggered by the invention of the tip recording method by Hodgson, Lettvin, and Roeder in 1955. A large-tip recording electrode containing a tastant, with a bit of salt if needed for conductivity, is slipped over the end of the taste hair and recordings made relative to a ground electrode in contact with remote tissue. Action potentials are generated in taste cells with somata at the base of the taste hair and dendrites in one lumen of the hair running to the tip (Grabowski & Dethier, 1954; V. G. Dethier, 1955; Pollack & Balakrishnan, 1997). Two additional modes of recording from fly taste hairs have been developed. The sidewall recording method allows maintained access to the active chemoreceptor dendrite while solutions are applied to the tip of the hair (Morita, 1959). These and subsequent studies established that the spikes in chemoreceptor cells are generated at the base of the hair and backpropagate up the dendrite (Tateda & Morita, 1959; Wolbarsht & Hanson, 1965). More recent work has applied the whole-cell patch clamp method to chemosensory neurons isolated from the Phormia labellum to identify a cAMP-mediated transduction pathway in the sugar receptor cell (Kan, Kataoka-Shirasugi, & Amakawa, 2008). The ease of making tip recordings from fly chemoreceptors has led to an extensive analysis of the chemical specificities of the taste hair neurons (F. Ling, Dahanukar, Weiss, Kwon, & Carlson, 2014), including the enigmatic water receptor (V. G. Dethier & Goldrichrachman, 1976; Meunier, Marion-Poll, & Lucas, 2009). Strikingly, data derived from the use of an in vivo expression system for bitter receptors in Drosophila suggested a model in which coexpressed bitter receptors could show competition, inhibition, or activation, with consequent implications for the evolution of taste systems generally (Delventhal & Carlson, 2016).

The powerful genetic tools available for localizing small sets of neurons causally involved in behavioral decision making, as outlined earlier for the neural control of male Drosophila courtship song, have also been applied to finding critical neural control elements for Drosophila feeding (Pool & Scott, 2014; Wright, 2016). This includes identification of command-like neurons for the motor components of fly feeding (Gordon & Scott, 2009; Flood et al., 2013; McKellar, 2016). Activating a small cluster of hugin-containing central neurons suppressed the motor program for feeding and initiated locomotion (Schoofs et al., 2014). The neuropeptide hugin is a homolog of the mammalian neuropeptide neuromedin U (Schlegel et al., 2016). Tracing the entire connectome of hugin-containing neurons in the Drosophila larva revealed a number of striking parallels at the cellular and molecular levels between mammalian and dipteran central circuits controlling food intake (Schlegel et al., 2016).

A number of fast-acting and neuromodulatory neurotransmitters have been identified as playing a role in the Drosophila feeding control circuit, including dopamine (Inagaki et al., 2012; Marella, Mann, & Scott, 2012; Bjordal, Arquier, Kniazeff, Pin, & Leopold, 2014; Masek, Worden, Aso, Rubin, & Keene, 2015), leucokinin (Al-Anzi et al., 2010), allatostatin-A (Hergarden, Tayler, & Anderson, 2012), gamma-aminobutyric acid (GABA) (Pool et al., 2014), and serotonin (Albin et al., 2015). Dopaminergic neurons also are likely to be critical components of the circuitry involved in learning about food components (Das, Lin, & Waddell, 2016). The development of dopaminergic neuronal clusters in Drosophila has recently been described and manipulated by ablating neuroblasts with dietary hydoxyurea (Hartenstein, Cruz, Lovick, & Guo, 2017). A role for Drosophila insulin-like peptides in the regulation of feeding and metabolic homeostasis has been identified in which GABAergic inputs to the insulin-producing neurons provide a feedback loop from the fat body, which is the nutrient sensor controlling secretion of Drosophila insulin-like peptide (Rajan & Perrimon, 2012). Another part of the metabolic regulatory mechanism is carried out by Unpaired 2, a protein secreted by the fat body when the fly is in a food-sated state. Genetic perturbation of the levels of Unpaired 2 produces a mutant phenotype that can be rescued by human leptin (Rajan & Perrimon, 2012). A critical role for insulin secretion in regulating feeding in Drosophila is also indicated by the synaptic actions of so-called Taotie neurons (Zhan et al., 2016), which have dramatic effects on feeding and control the secretion of insulin. In addition to this panoply of neurochemical actors in the control of fly feeding, recent evidence indicates that six neurosecretory cells in the fly brain, each secreting diuretic hormone 44, provide an essential link in the neural pathway by which a fly selects nutritious sugars independent of their taste properties (Dus et al., 2015; Sachse & Beshel, 2016).

An elegant molecular genetic, anatomical, and behavioral study in Drosophila adults has shown how four brain neurons jointly regulate food and water intake by responding to hormonal signaling of nutrient levels and using an osmolality-sensitive ion channel to regulate water intake (Jourjine, Mullaney, Mann, & Scott, 2016). Also, antennal glutamate receptors of several distinct varieties mediate behavioral responses to humidity (Knecht et al., 2016), which may well be modulated centrally based on the fly’s current state of water balance, as signaled by the neurons studied by Jourjine et al. (2016)

Motor Pattern Generation

The most thoroughly explored neural circuit for studies of the dynamics and neuromodulation of motor pattern generation is the stomatogastric ganglion (STG) and associated inputs in decapod crustaceans (Daur, Nadim, & Bucher, 2016). The STG has provided an essential testing ground within which to understand the role of multiple neuromodulators interacting with the intrinsic biophysics and synaptic connectivity of a small (30 neuron) motor circuit with known output targets (Harris-Warrick, 2011; Marder et al., 2015; Kintos, Nusbaum, & Nadim, 2016). These studies have also clarified how the expression levels of different ion channels in single neurons are homeostatically regulated to produce consistent and reproducible firing dynamics (O’leary, Williams, Caplan, & Marder, 2013). Homeostatic regulatory mechanisms can also act on short-term synaptic plasticity to adjust muscle depolarizations to compensate for variable numbers of presynaptic motor neurons (Daur, Bryan, Garcia, & Bucher, 2012). This is but one of several mechanisms by which diversity in both biophysical and synaptic properties can compensate to produce increased regularity of overall circuit function (Gjorgjieva, Drion, & Marder, 2016). Findings obtained using the STG have substantial overlap with analyses of mammalian spinal motor networks (Kiehn, 2016).

A very general issue addressed by both experimental measurements and modeling of network function is the issue of how small discrete motor networks maintain a relative constancy of network function in the face of protein turnover and synaptically mediated perturbations, including external neuromodulation (O’leary, Williams, Franci, & Marder, 2014). A small set of rules can specify how network-level homeostasis of function can arise from local neuron-based rules governing channel expression and turnover. Experimental and theoretical studies also have clarified the role of degeneracy in contributing to robustness of motor network function, using data from the STG (P. S. Dickinson, Qu, & Stanhope, 2016; Kintos et al., 2016), the leech heartbeat central pattern generator (Calabrese, Norris, Wenning, & Wright, 2011), and the Aplysia feeding network (Cropper, Dacks, & Weiss, 2016). Perhaps not surprisingly, experimental work shows that some degeneracy in neuron function can augment overall network stability of motor paten generation.

The beauty of the crustacean stomatogastric ganglion as a testing ground for rules governing neural circuit function is nowhere better shown than in very recent work testing divergent ideas concerning general principles of wiring optimization (Otopalik et al., 2017). Although robust circuit function can be maintained with surprisingly variable biophysical properties of the constituent neurons (Calabrese et al., 2011; Marder et al., 2015), theoretical and experimental work suggests that principles of optimal wiring impose strong constraints on neuronal structure during development (Y. Kim, Sinclair, Chindapol, Kaandorp, & De Schutter, 2012; Takemura et al., 2015; I. E. Wang & Clandinin, 2016). By examining the branching patterns and detailed cable properties of four neuron types in the STG of the shore crab Cancer borealis, Otopalik et al. (2017) determined that the variability in the topology of these neurons is not consistent with the wiring optimization hypothesis. Rather, the authors conclude that macroscopic space-filling constraints trump the increased metabolic cost of what appears to be suboptimal wiring topology.

Navigation and Head Direction Cells

The discovery of head direction cells in the rat hippocampus by John O’Keefe in 1971 (O’Keefe & Dostrovsky, 1971) was recognized by the award of the 2014 Nobel Prize in Physiology or Medicine. In a truly remarkable example of experimental insights spanning the IV divide, head direction cell-like activity has now been identified in the central complex of the cockroach Blabarus discoidalis (Varga & Ritzmann, 2016). The central complex is a multisensory area previously implicated as critical for navigation (Heinze, 2015). Single neurons in the cockroach central complex were found to code for the animal’s orientation relative to distal landmarks, independent of cues from self-movement. Some of these putative cockroach head direction cells also coded the animal’s recent history of clockwise or counterclockwise rotation. As Varga and Ritzmann point out, the navigational challenges faced by the cockroach bear a striking similarity to those faced by rats, in whose hippocampi head direction cells were first described.

The central complex of Blabarus also plays a pivotal role in the control of movement (Martin, Guo, Mu, Harley, & Ritzmann, 2015), in that neurons recorded in the central complex during ongoing motor behavior emit movement-correlated activity, which depends on the pattern of movement being generated, for example, forward walking, turning, or combinations thereof. Remarkably, stimulation via the recording electrode in the central complex produced reliable epochs of forward walking or turning. When the roach walked over an obstacle, the subset of active central complex cells changed (Martin et al., 2015). The insect central complex is highly conserved across diverse insect species (Turner-Evans & Jayaraman, 2016) and has been implicated in such diverse examples of adaptive sensorimotor processing as sun-compass navigation in monarch butterflies (Reppert, Guerra, & Merlin, 2016), celestial navigation by dung beetles (Collett, Wystrach, & Graham, 2016), place learning based on visual landmarks in Drosophila (Seelig & Jayaraman, 2015), and sun compass directional navigation in locusts (Bockhorst & Homberg, 2015). Recent reports suggest that dung beetles use a snapshot mechanism to learn celestial cues (Collett et al., 2016; el Jundi et al., 2016).

A recent review aims to synthesize the rules and neural mechanisms of spatial navigation in rats and cockroaches to extract general principles that transcend the IV divide (Varga, Kathman, Martin, Guo, & Ritzmann, 2017). Whether the apparent similarities of some aspects of the neural mechanism for guiding navigation in rat and roach are based on convergent evolution or represent homologous derivatives from a remote common ancestor (Strausfeld & Hirth, 2013, 2016) remains to be determined.

No treatment of insect navigation would be complete without mention of Rudiger Wehner, another founding figure of neuroethology, and his remarkably insightful studies of ant navigation, particularly the desert ant Cataglyphis (Fleischmann, Christian, Muller, Rossler, & Wehner, 2016). A recent synthetic model for ant navigation, called Navinet, shows clearly that parallel sensory processing streams, weighted by current context and conditions and summed by a central computational network, can account for the observed navigational abilities of Cataglyphus navigation better than any single processing stream devoted to path integration, use of landmarks, or searching strategies operating alone (Wehner, Hoinville, Cruse, & Cheng, 2016). This insight has great potential to make contributions to mechanisms of animal navigation that transcend the IV divide.

Cataglyphus navigation can now be studied footstep-by-footstep using a newly designed ultralight spherical treadmill suspended on a bed of air (Dahmen, Wahl, Pfeffer, Mallot, & Wittlinger, 2017). Convincing evidence that the desert ants were indeed making normal navigational decisions while tethered on the spherical treadmill was obtained by using the device in the deserts of Tunisia while freshly collected and tethered ants were headed home with newly harvested food items. The paths produced on the treadmill corresponded exactly to the expected paths of homeward-bound ants. Furthermore, the paths on the treadmill were altered by rotating the treadmill 90°, just as free-ranging ants alter their homeward path using their sun-compass mechanism after a 90° rotation.

Adult Neurogenesis

The generation of new neurons in adult vertebrate animals, including humans (Kempermann, 2016), is no longer a controversial topic; rather, current work seeks to understand the cellular and anatomical origins of new adult-born neurons and the consequences of their integration into functional circuits. Adult neurogenesis is also known to occur in diverse invertebrates, from terrestrial slugs (Watanabe, Kirino, & Gelperin, 2008) and snails (Longley, 2011) to echinoderms (Mashanov, Zueva, & Garcia-Arraras, 2015), crickets (Scotto-Lomassese et al., 2003), cockroaches (Gu, Tsia, Chiang, & Chow, 1999), Drosophila (von Trotha, Egger, & Brand, 2009), crayfish (Y. F. Kim, Sandeman, Benton, & Beltz, 2014), and Hydra (Wenger, Buzgariu, & Galliot, 2016), among others. Although adult neurogenesis in Drosophila was initially discounted, a new lineage tracing method has provided robust evidence for adult neurogenesis in the Drosophila optic lobes (Fernandez-Hernandez, Rhiner, & Moreno, 2013). Adult neurogenesis in mammals is particularly well described as contributing new interneurons to both the dentate gyrus of the hippocampus (Opendak et al., 2016) and the olfactory bulb (Lim & Alvarez-Buylla, 2016). In yet another remarkable parallel, the descriptions of adult neurogenesis in slug, snail, cricket, and crayfish also involve addition of new neurons to the central processing networks of the olfactory system. The IV analogy is further elaborated by the demonstrated role of nitric oxide in both mammalian olfactory bulb function (Lowe, Buerk, Ma, & Gelperin, 2008) and invertebrate olfactory information processing (Gelperin, 1994) and olfactory neurogenesis (Benton, Sandeman, & Beltz, 2007). The similarities between crustaceans and mammals in adult neurogenesis have recently been reviewed (Sandeman, Bazin, & Beltz, 2011) and placed in an explicitly evolutionary perspective (Kempermann, 2016).

Remarkable recent work in analyzing the cellular dynamics in the crayfish neurogenic niche has clearly shown that cells from the crayfish innate immune system contribute to the generation of adult-born neurons (Benton et al., 2014) and that immune cells interact with neural stem cells to regulate adult neurogenesis (Leiter, Kempermann, & Walker, 2016). Naturally the suggestion has been made to examine immune system—adult neurogenesis dynamics in vertebrate, particularly mammalian, systems (Harzsch, von Bohlen, & Halbach, 2016). Perhaps the immune system–neurogenesis relationship demonstrated in crayfish (Beltz et al., 2015) will be shown to also pertain to the cellular dynamics of mammalian neurogenesis. Conversely, the effects of enriched environments and exercise in augmenting hippocampal neurogenesis in rodents (van Praag, Christie, Sejnowski, & Gage, 1999; Opendak & Gould, 2015; Garthe, Roeder, & Kempermann, 2016) strongly suggest that similar effects will be found to modulate invertebrate neurogenesis. A comparative approach may also allow resolution of the long-standing controversy about the relationship between adult neurogenesis and learning (Leuner, Gould, & Shors, 2006; DiFeo & Shors, 2017).

Adult neurogenesis in insects has been reported sporadically (Dufour & Gadenne, 2006), including a remarkable expansion of the adult mushroom bodies of Heliconius butterflies (S. H. Montgomery, Merrill, & Ott, 2016). Although Heliconius butterflies are protected from predators by internal production of cyanide-based compounds (Arias et al., 2016), they may learn to associate floral odors with pollen availability (Salcedo, 2011) as adults, augmented by their enlarged mushroom bodies. Several other species of butterflies have well-developed learning abilities; for example, the monarch Danaus plexippus (Rodrigues, Goodner, & Weiss, 2010; Blackiston, Briscoe, & Weiss, 2011; Cepero, Rosenwald, & Weiss, 2015), the Chinese Windmill Byasa alcinous (Kandori & Yamaki, 2012), and the cabbage white Pieris rapae (Snell-Rood, Davidowitz, & Papaj, 2011), among others. It would be interesting to examine these other butterfly species for adult neurogenesis in the mushroom body. Other activities than learning can impact adult neurogenesis in insects; for example, fighting has been shown to enhance neurogenesis in adult crickets (Ghosal, Gupta, & Killian, 2009). Looking across the IV divide, some have speculated that similar neurogenic mechanisms may be operative in decapod crustaceans and mammals (Schmidt & Derby, 2011). Detailed mechanistic studies will be needed to differentiate analogous from homologous mechanisms.


Sleep is another high-level brain mechanism where mechanistic insights provide a useful bridge across the IV divide, with mechanistic insights flowing both ways. The fundamental functions of sleep are still a mystery, although sleep is essentially universal among animals from a diverse array of phyla (Cirelli & Tononi, 2008). There are two major categories of speculations about the functions of sleep. One category of speculation focuses on the possible functions of sleep as performing a mainly restorative function to allow repair of cellular machinery, replenish energy supplies, and remove potentially toxic waste products (Vyazovskiy & Harris, 2013; Musiek & Holtzman, 2016). The second major hypothesis for the function of sleep is that synaptic changes during wakefulness lead to net strengthening of synaptic interactions while during sleep synaptic strengths are renormalized, as evidenced by structural studies in Drosophila (Bushey, Tononi, & Cirelli, 2011; Lamaze et al., 2017) and mice (de Vivo et al., 2017; Diering et al., 2017), among other species. In both mice and fruit flies, sleep contributes to the repair of double-stranded DNA breaks created during wakefulness, thereby enhancing the transcription of plasticity-related immediate early genes (Bellesi, Bushey, Chini, Tononi, & Cirelli, 2016).

Sleep is characterized by a state of reduced activity or immobility, and a reduction in arousal state and attention. In vertebrate animals and particularly in mammals, there are characteristic electroencephalographic criteria (Prerau, Brown, Bianchi, Ellenbogen, & Purdon, 2017) produced by oscillatory activity in cortical and subcortical structures. A distinctive form of slow-wave brain activity is also observed in crayfish during periods of reduced responsiveness to external stimuli (Ramon, Hernandez-Falcon, Nguyen, & Bullock, 2004). Sleep in Aplysia californica is characterized phenotypically as involving a specific body posture, behavioral quiescence, elevated arousal threshold, and compensatory sleep rebound after sleep deprivation (Vorster, Krishnan, Cirelli, & Lyons, 2014). In addition, recent work has clarified the effects of sleep on learning and memory consolidation in Aplysia (Krishnan et al., 2016; Levy, Levitan, & Susswein, 2016). A sleep state has also been characterized in C. elegans with molecular and functional similarities to Drosophila and mammals (Trojanowski & Raizen, 2016). Neuropeptides involved in sleep regulation in C. elegans have now been identified (Nath, Chow, Wang, Schwarz, & Sternberg, 2016).

The most thoroughly studied invertebrate sleep mechanism resides in the central nervous system of Drosophila (Donlea, Pimentel, & Miesenbock, 2014). The mechanisms controlling Drosophila sleep interface with other behavioral control circuits, including clock neurons controlling circadian rhythms of activity (S. Liu et al., 2014; Kayser, Mainwaring, Yue, & Sehgal, 2015). Temperature receptors and photoreceptors also interact with the output of clock neurons to control sleep onset time (Lamaze et al., 2017). The neural relationship between fly sleep and aggression has recently been clarified (Kayser et al., 2015), in concert with neural and genetic studies specifically focused on fly aggression itself (Kravitz & Fernandez, 2015; Trannoy, Penn, Lucey, Popovic, & Kravitz, 2016; Baxter & Dukas, 2017). Sleep control systems also interact with feeding control systems (Murphy et al., 2016) as found in many other animals.

The power of genetic and molecular tools available to dissect neural circuits in Drosophila has produced a profusion of mechanistic studies of the sleep control network. For example, fly dopamine receptors are shown to be involved in both sleep regulation (Jiang et al., 2016) and increasing memory retention by blocking memory loss due to activity in dopamine neurons (Berry, Cervantes-Sandoval, Chakraborty, & Davis, 2015). Essential protein components of central neural synapses vary with the fly’s sleep cycle, suggesting that sleep may play a role in synaptic homeostasis (Gilestro, Tononi, & Cirelli, 2009), as discussed earlier. A group of neurons projecting to the dorsally located fan-shaped body of the central complex fulfills many of the criteria for sleep command neurons (I. Kupfermann & Weiss, 1978; Pimentel et al., 2016b) in that activation of these neurons induces sleep, whereas reduced activity in these neurons causes insomnia. Activation and inactivation of three different potassium channels, including a newly identified channel protein called Sandman, resident in the membranes of sleep control neurons, and regulation of the potassium channels by dopamine, directly contribute to sleep homeostasis (Pimentel et al., 2016a).

Learning and Memory

Studies of learning and memory in the compact nervous systems of invertebrates have a long history (Lubbock, 1888), receiving an early impetus from studies of behavioral flexibility of earthworms by Charles Darwin (1881). Studies of invertebrate learning and memory accelerated in the 1950s and 1960s, stimulated by demonstrations of learning ability widely dispersed among the invertebrates (W. C. Corning, J. A. Dyal, & A. O. D. Willows, 1973a; W. C. Corning, J. A. Dyal, & A. O. D. Willows, 1973; W. C. Corning, J. A. Dyal, & A. O. D. Willows, 1973b; Thorpe & Davenport, 1965). The increasing evidence for “grade A learning” among creatures drawn from a diverse array of invertebrate phyla (Glanzman, 2007; Menzel & Benjamin, 2013; Perry, Barron, & Cheng, 2013) was accompanied by the development of a large number of new invertebrate preparations for basic studies of the biophysics, synaptic interactions, and network properties of compact behavioral control systems (Clarac & Pearlstein, 2007). Following the award of the Nobel Prize in 1973 to the ethologists Karl von Frisch, Konrad Lorenz, and Niko Tinbergen, there emerged a new discipline, neuroethology, which emphasized the critical evaluation of how activity in identified central neurons contributed to the species-typical behaviors of a given species, often but not exclusively using the technical advantages of access to identified central neurons with microelectrodes in the newly developed invertebrate preparations, for example, Tritonia diomedia (Willows, Dorsett, & Hoyle, 1973; Getting & Dekin, 1985).

The use of an invertebrate preparation to elucidate the cellular and biophysical mechanisms causally related to simple forms of learning is best exemplified by work on the gill and siphon withdrawal reflexes of the marine mollusk Aplysia californica (Castellucci, Pinsker, Kupfermann, & Kandel, 1970; I. Kupfermann, Castellucci, Pinsker, & Kandel, 1970; Pinsker, Kupfermann, Castellucci, & Kandel, 1970), which culminated in the award of the Nobel Prize to Eric Kandel for the elaboration of the Aplysia work into associative learning and the molecular mechanisms of synaptic plasticity at several time scales, from milliseconds to weeks (Kandel, 2001). More recent work has compared memory storage mechanisms in Aplysia and mammalian hippocampus (Bailey, Kandel, & Harris, 2015). The 50-year research program on the Aplysia gill, siphon, and tail withdrawal neural control systems has spawned parallel efforts in a variety of molluscan, arthropod, and other invertebrate preparations (North & Greenspan, 2007; Menzel & Benjamin, 2013).

Studies of the identified bursting neuron R15 located in the abdominal ganglion of Aplysia provide a particularly elegant and complete analysis of the biophysical basis for rhythmic bursting in a central neuron (Canavier, Clark, & Byrne, 1991; Canavier, Baxter, Clark, & Byrne, 1993, 1994), its modulation by serotonin and dopamine (Butera, Clark, Canavier, Baxter, & Byrne, 1995), and its essential role in the water regulatory mechanism of the animal (Kupfermann & Weiss, 1976). The dynamic model integrating properties of membrane ion channels, membrane pumps, and intracellular calcium uptake and storage was essential to gaining insights into the multiple modes of bursting shown by R15 in vitro and the effects of neuromodulators on the neuron’s dynamical behavior.

One approach that proved particularly productive during this renaissance of studies on invertebrate learning was to assume as a working hypothesis that if a mammalian learning mechanism evolved to solve a particular adaptive challenge, it is very likely that an invertebrate faced with the same adaptive challenge would also have a robust learning mechanism to solve the same problem. For example, rodents have a highly developed learning mechanism used to identify and avoid foods resulting in toxicosis, readily demonstrated in the laboratory as conditioned taste aversions, as first identified by John Garcia (Garcia, Ervin, & Koelling, 1966). Terrestrial slugs face the same adaptive challenge in their food foraging behavior, and they also turn out to possess a robust food aversion learning mechanism (Gelperin, 1975) which displays several higher order learning phenomena such as compound conditioning, second-order conditioning, and the Kamin blocking effect (Sahley, 1990; Watanabe et al., 2008; Gelperin, 2014). Similarly, as rats can learn to select a diet containing an essential vitamin they are lacking (Rozin, 1965, 1976), slugs also can learn to avoid an artificial diet lacking an essential amino acid and can learn to choose an artificial diet containing an essential amino acid they are lacking (Delaney & Gelperin, 1986). Food and odor learning has provided a productive avenue for studies of learning in several other invertebrates (Quinn, Harris, & Benzer, 1974; Aso & Rubin, 2016; Cho, Brueggemann, L’Etoile, & Bargmann, 2016; Das et al., 2016; Honda, Lee, Honjo, & Furukubo-Tokunaga, 2016; Simoes, Ott, & Niven, 2016; Sugimachi, Matsumoto, Mizunami, & Okada, 2016). Learning-induced taste preferences have recently been described in C. elegans (L. Wang et al., 2017).

Invertebrate Cognition

As studies of leaning and memory in compact nervous systems have proliferated and have demonstrated ever more complex computational abilities, the question naturally arises: Are invertebrates conscious? What is the definition of consciousness and what are the objective criteria used to determine if an autonomous computational device, carbon based or silicon based, is conscious? This set of questions is of interest to practitioners from several traditions (Table 1), including the new fields of cognitive ethology (Ristau, 1991; Griffin & Speck, 2004; Shettleworth, 2009), insect cognition (Sherry & Strang, 2015; Haberkern & Jayaraman, 2016), insect selective attention (Horridge, 2015; de Bivort & van Swinderen, 2016), comparative cognition (Gomez-Marin & Mainen, 2016), decision making (Riveros & Gronenberg, 2012; Koganezawa, Kimura, & Yamamoto, 2016), and the evolutionary ecology of cognition (Morand-Ferron, Cole, & Quinn, 2016).

Table 1 Types of Questions Motivating the Study of Invertebrate Learning and Cognition


Question Addressed



What is the role of learning in the natural behavior of the animal?

1, 2, 3


What neurons and circuits mediate ethologically relevant learning?

4, 5, 6


How do we investigate the logic of learning using standard testing protocols?

7. 8. 9


Can we use learned responses to stimuli to probe sensory processing?

10, 11, 12


How does learning optimize an animal’s fitness within an ecological niche?

13, 14, 15


Can we make plausible neural models to account for the observed learning?

8, 16, 17


Can we make artificial autonomous systems using bio‐inspired machine learning?

18, 19, 20, 21


What are the membrane and synaptic events that implement learning?

22, 23, 24


What biochemical events and changes account for memory storage?

23, 25, 26


What parts of the brain are loci for causative events during learning?

27, 28, 29


How does learning contribute to the evolution of the species?

30, 31, 32


How does the learning emerge during development of the organism?

33, 34, 35, 36


What neural and behavioral pathologies arise if the learning malfunctions?

37, 38, 39


What genes and genetic control networks are essential for learning?

40, 41, 42


How does the learning ability make a population more successful?

43, 44, 45


What cognitive abilities are essential to operation of learning mechanism?

46, 47, 48


Are the learning mechanisms dependent on social interactions?

49, 50, 51


What is the time course of learning and memory onset and retention?

52, 53, 54


How do drugs with defined agonist binding profiles affect learning?

55, 56, 57, 58


How does the learning relate to existing theories of learning?

59, 60, 61


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A remarkable recent study has extended our understanding of insect cognition, using an approach that can only be called inverse neuroethology. Bumblebee subjects were taught to perform a task having no obvious relationship to their natural behavior, namely, to roll a ball to a set position on a platform to obtain a food reward (Loukola, Perry, Coscos, & Chittka, 2017). Watching pretrained bumblebees augmented learning by new trainees, who often devised a more optimal strategy for performing the ball-rolling task than the solution they had directly observed. This is evidence for both observational learning and cognitive flexibility.

The preeminent examples of brain and cognitive complexity within the invertebrates are provided by studies of cephalopod mollusks (Vitti, 2013), particularly cuttlefish (Scata, Jozet-Alves, Thomasse, Josef, & Shashar, 2016), and octopi (Albertin et al., 2015). These studies include several book-length treatments focused on octopus cognition and consciousness (Darmaillacq, Dickel, & Mather, 2014; S. Montgomery, 2015; Godfrey-Smith, 2016). On January 1, 2013, research using “live cephalopods” became regulated within the European Union by Directive 2010/63/EU (Fiorito et al., 2014). The AAALAC international accrediting agency has also adopted guidelines for the care and welfare of cephalopods in research, which can be assessed at the following URL: ( As Charles Darwin expressed forcefully in his transmutation notebook “B” of 1837 (Gross, 1994), “It is absurd to talk of one animal being higher than another.” If, therefore, the expression of consciousness is present in cephalopods and exists on a continuum within the invertebrates, where should we draw the line to divide those worthy of protection from those that are not? This extremely thorny question will become an increasingly urgent matter for invertebrate neuroscientists, among other stakeholders in this issue.


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