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The localization of distinct landmarks plays a crucial role in encoding new spatial memories. In mammals, this function is performed by hippocampal neurons that sparsely encode an animal's location relative to surrounding objects. Similarly, the dorsolateral pallium (DL) is essential for spatial learning in teleost fish. The DL of weakly electric gymnotiform fish receives both electrosensory and visual input from the preglomerular nucleus (PG), which has been hypothesized to encode the temporal sequence of electrosensory or visual landmark/food encounters. Here, we show that DL neurons in the Apteronotid fish and in the Carassius auratus (goldfish) have a hyperpolarized resting membrane potential (RMP) combined with a high and dynamic spike threshold that increases following each spike. Current-evoked spikes in DL cells are followed by a strong small-conductance calcium-activated potassium channel (SK)-mediated after-hyperpolarizing potential (AHP). Together, these properties prevent high frequency and continuous spiking. The resulting sparseness of discharge and dynamic threshold suggest that DL neurons meet theoretical requirements for generating spatial memory engrams by decoding the landmark/food encounter sequences encoded by PG neurons. Thus, DL neurons in teleost fish may provide a promising, simple system to study the core cell and network mechanisms underlying spatial memory., (Copyright © 2019 Trinh et al.)

Understanding how distributed neuronal circuits integrate sensory information and generate behavior is a central goal of neuroscience. However, it has been difficult to study neuronal networks at single-cell resolution across the entire adult brain in vertebrates because of their size and opacity. We address this challenge here by introducing the fish Danionella translucida to neuroscience as a potential model organism. This teleost remains small and transparent even in adulthood, when neural circuits and behavior have matured. Despite having the smallest known adult vertebrate brain, D. translucida displays a rich set of complex behaviors, including courtship, shoaling, schooling, and acoustic communication. In order to carry out optical measurements and perturbations of neural activity with genetically encoded tools, we established CRISPR-Cas9 genome editing and Tol2 transgenesis techniques. These features make D. translucida a promising model organism for the study of adult vertebrate brain function at single-cell resolution.

We demonstrate how rhythmic activity can arise in neural networks from feedforward rather than recurrent circuitry and, in so doing, we provide a mechanism capable of explaining the temporal decorrelation of γ-band oscillations. We compare the spiking activity of a delayed recurrent network of inhibitory neurons with that of a feedforward network with the same neural properties and axonal delays. Paradoxically, these very different connectivities can yield very similar spike-train statistics in response to correlated input. This happens when neurons are noisy and axonal delays are short. A Taylor expansion of the feedback network's susceptibility-or frequency-dependent gain function-can then be stopped at first order to a good approximation, thus matching the feedforward net's susceptibility. The feedback network is known to display oscillations; these oscillations imply that the spiking activity of the population is felt by all neurons within the network, leading to direct spike correlations in a given neuron. On the other hand, in the output layer of the feedforward net, the interaction between the external drive and the delayed feedforward projection of this drive by the input layer causes indirect spike correlations: spikes fired by a given output layer neuron are correlated only through the activity of the input layer neurons. High noise and short delays partially bridge the gap between these two types of correlation, yielding similar spike-train statistics for both networks. This similarity is even stronger when the delay is distributed, as confirmed by linear response theory.

Object saliency is based on the relative local-to-background contrast in the physical signals that underlie perceptual experience. As such, contrast-detecting neurons (ON/OFF cells) are found in many sensory systems, responding respectively to increased or decreased intensity within their receptive field centers. This differential sensitivity suggests that ON and OFF cells initiate segregated streams of information for positive and negative sensory contrast. However, while recording in vivo from the ON and OFF cells of Apteronotus leptorhynchus, we report that the reversal of stimulus motion triggers paradoxical responses to electrosensory contrast. By considering the instantaneous firing rates of both ON and OFF cell populations, a bidirectionally symmetric representation of motion is achieved for both positive and negative contrast stimuli. Whereas the firing rates of the individual contrast detecting neurons convey scalar information, such as object distance, it is their sequential activation over longer timescales that track changes in the direction of movement.

Neural representations of a moving object's distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent's response to "looming" object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent's response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent's firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli.

The present article examines the anatomical organization of the dorsal telencephalon of two gymnotiform fish: Gymnotus sp. and Apteronotus leptorhynchus. These electric fish use elaborate electrical displays for agonistic and sexual communication. Our study emphasizes mainly pallial divisions: dorsolateral (DL), dorsodorsal (DD), and dorsocentral (DC), previously implicated in social learning dependent on electric signals. We found that the pallial cytoarchitectonics of gymnotiformes are similar to those reported for the commonly studied goldfish, except that DC is larger and better differentiated in gymnotiformes. We identified a new telencephalic region (Dx), located between DL and DC, and describe the morphological and some biochemical properties of its neurons. Most neurons in DL, DD, and DC are glutamatergic with spiny dendrites. However, the size of these cells as well as the orientation and extent of their dendrites vary systematically across these regions. In addition, both DD and DL contained numerous small GABAergic interneurons as well as well-developed GABAergic plexuses. One important and novel observation is that the dendrites of the spiny neurons within all three regions remain confined to their respective territories. We confirm that DL and DC express very high levels of NMDA receptor subunits as well as CaMKIIα, a key downstream effector of this receptor. In contrast, this enzyme is nearly absent in DD, while NMDA receptors are robustly expressed, suggesting different rules for synaptic plasticity across these regions. Remarkably, GABAergic pallial neurons do not express CaMKIIα, in agreement with previously reported results in the cortex of rats., (Copyright © 2012 Wiley Periodicals, Inc.)

Neural codes often seem tailored to the type of information they must carry. Here we contrast the encoding strategies for two different communication signals in electric fish and describe the underlying cellular and network properties that implement them. We compare an aggressive signal that needs to be quickly detected, to a courtship signal whose quality needs to be evaluated. The aggressive signal is encoded by synchronized bursts and a predictive feedback input is crucial in separating background noise from the communication signal. The courtship signal is accurately encoded through a heterogenous population response allowing the discrimination of signal differences. Most importantly we show that the same strategies are used in other systems arguing that they evolved similar solutions because they faced similar tasks., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The present study provides a detailed description of morphological and hodological aspects of the glomerular nucleus in the weakly electric fish Gymnotus sp., and explores the evolutionary and functional implications flowing from this analysis. The glomerular nucleus of Gymnotus shows numerous morphological similarities with the glomerular nucleus of percomorph fish, although cytoarchitectonically simpler. In addition, congruence of the histochemical acetylcholinesterase (AChE) distribution with cytoarchitectonic data suggests that the glomerular nucleus, together with the ventromedial cell group of the medial subdivision of the preglomerular complex (PGm-vmc) rostrally, and the subglomerular nucleus (as identified by Maler et al. [1991] J Chem Neuroanat 4:1–38) caudally, may form a distinct longitudinally organized glomerular complex. Our results show that an important source of sensory afferents to the glomerular nucleus originates in the pretectal and electrosensorius nuclei. The glomerular nucleus in turn projects to the hypothalamus (inferior lobe and anterior hypothalamus), to the anterior tuberal nucleus, and to the medial region of the preglomerular nucleus (PGm). These data suggest that visual and electrosensory information reach the glomerular nucleus and are relayed to the hypothalamus and, via PGm, to the pallium. Such connections are similar to those of the glomerular nucleus in percomorphs and the posterior pretectal nucleus in osteoglossomorph, esocids, and salmonids, where they comprise one component of a visual processing pathway. In Gymnotiform fish, however, the pretectal region that projects to the glomerular nucleus is dominated by electrosensory input (visual input is minor), which is consistent with the dominant role of electroreception in these fish.

Although serotonergic innervation of sensory brain areas is ubiquitous, its effects on sensory information processing remain poorly understood. We investigated these effects in pyramidal neurons within the electrosensory lateral line lobe (ELL) of weakly electric fish. Surprisingly, we found that 5-HT is present at different levels across the different ELL maps; the presence of 5-HT fibers was highest in the map that processes intraspecies communication signals. Electrophysiological recordings revealed that 5-HT increased excitability and burst firing through a decreased medium afterhyperpolarization resulting from reduced small-conductance calcium-activated (SK) currents as well as currents mediated by an M-type potassium channel. We next investigated how 5-HT alters responses to sensory input. 5-HT application decreased the rheobase current, increased the gain, and decreased first spike latency. Moreover, it reduced discriminability between different stimuli, as quantified by the mutual information rate. We hypothesized that 5-HT shifts pyramidal neurons into a burst-firing mode where bursts, when considered as events, can detect the presence of particular stimulus features. We verified this hypothesis using signal detection theory. Our results indeed show that serotonin-induced bursts of action potentials, when considered as events, could detect specific stimulus features that were distinct from those detected by isolated spikes. Moreover, we show the novel result that isolated spikes transmit more information after 5-HT application. Our results suggest a novel function for 5-HT in that it enables differential processing by action potential patterns in response to current injection.

Spike-frequency adaptation is a prominent aspect of neuronal dynamics that shapes a neuron's signal processing properties on timescales ranging from about 10 ms to >1 s. For integrate-and-fire model neurons spike-frequency adaptation is incorporated either as an adaptation current or as a dynamic firing threshold. Whether a physiologically observed adaptation mechanism should be modeled as an adaptation current or a dynamic threshold, however, is not known. Here we show that a dynamic threshold has a divisive effect on the onset f-I curve (the initial maximal firing rate following a step increase in an input current) measured at increasing mean threshold levels, i.e., adaptation states. In contrast, an adaptation current subtractively shifts this f-I curve to higher inputs without affecting its slope. As a consequence, an adaptation current acts essentially linearly, resulting in a high-pass filter component of the neuron's transfer function for current stimuli. With a dynamic threshold, however, the transfer function strongly depends on the input range because of the multiplicative effect on the f-I curves. Simulations of conductance-based spiking models with adaptation currents, such as afterhyperpolarization (AHP)-type, M-type, and sodium-activated potassium currents, do not show the divisive effects of a dynamic threshold, but agree with the properties of integrate-and-fire neurons with adaptation current. Notably, the effects of slow inactivation of sodium currents cannot be reproduced by either model. Our results suggest that, when lateral shifts of the onset f-I curve are seen in response to adapting inputs, adaptation should be modeled with adaptation currents and not with a dynamic threshold. In contrast, when the slope of onset f-I curves depends on the adaptation state, then adaptation should be modeled with a dynamic threshold. Further, the observation of divisively altered onset f-I curves in adapted neurons with notable variability of their spike threshold could hint to yet known biophysical mechanisms directly affecting the threshold.

Efficient sensory coding implies that populations of neurons should represent information-rich aspects of a signal with little redundancy. Recent studies have shown that neural heterogeneity in higher brain areas enhances the efficiency of encoding by reducing redundancy across the population. Here, we study how neural heterogeneity in the early stages of sensory processing influences the efficiency of population codes. Through the analysis of in vivo recordings, we contrast the encoding of two types of communication signals of electric fishes in the most peripheral sensory area of the CNS, the electrosensory lateral line lobe (ELL). We show that communication signals used during courtship (big chirps) and during aggressive encounters (small chirps) are encoded by different populations of ELL pyramidal cells, namely I-cells and E-cells, respectively. Most importantly, we show that the encoding strategy differs for the two signals and we argue that these differences allow these cell types to encode specifically information-rich features of the signals. Small chirps are detected, and their timing is accurately signaled through stereotyped spike bursts, whereas the shape of big chirps is accurately represented by variable increases in firing rate. Furthermore, we show that the heterogeneity across I-cells enhances the efficiency of the population code and thus permits the accurate discrimination of different quality courtship signals. Our study shows the importance of neural heterogeneity early in a sensory system and that it initiates the sparsification of sensory representation thereby contributing to the efficiency of the neural code.

Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

Narrowband signals have fast and slow time scales. The transmission of narrowband signal features on both times cales, by spiking neurons, is demonstrated experimentally and theoretically. The interaction of the narrowband input and the threshold nonlinearity may create out-of-band interference, hindering the transmission of signals in a low-frequency range. The resultant out-of-band signal is the "envelope," or time-varying modulation of the narrowband signal. The levels of noise and nonlinearity intrinsic to the neuron gate transmission on the slow "envelope" time scale. When a narrowband and a distinct slow signal drive the neuron, the slow signal may be poorly transmitted. Increasing intrinsic noise in an averaging network removes the envelope in favor of the slow signal, paradoxically increasing the signal-to-noise ratio. These gating effects are generic for threshold and excitable systems.

The electrosensory system is used for both spatial navigation tasks and communication. An electric organ generates a sinusoidal electric field and cutaneous electroreceptors respond to this field. Objects such as prey or rocks cause a local low-frequency modulation of the electric field; this cue is used by electric fish for navigation and prey capture. The interference of the electric fields of conspecifics produces beats, often with high frequencies, that are also sensed by the electroreceptors; furthermore, these electric fish can transiently modulate their electric discharge as a communication signal. Thus these fish must therefore detect a variety of low-intensity signals that differ greatly in their spatial extent, frequency, and duration. Behavioral studies suggest that they are highly adapted to these tasks. Experimental and theoretical analyses of the neural circuitry for the electrosense has demonstrated many commonalities with the more common senses, e.g., topographic mapping and receptive fields with On or Off centers and surround inhibition. The integration of computational and experimental analyses has demonstrated novel mechanisms that appear to optimize weak signal detection in the electrosense including: noise shaping by correlations within single spike trains, induction of oscillations by delayed feedback inhibition, the requirement for maps with differing receptive field sizes tuned for different stimulus parameters, and the role of non-plastic feedback for adaptive cancellation of redundant signals. It is likely that these mechanisms will also be operative in other sensory systems.

Spike-frequency adaptation is a prominent feature of many neurons. However, little is known about its computational role in processing behaviorally relevant natural stimuli beyond filtering out slow changes in stimulus intensity. Here, we present a more complex example in which we demonstrate how spike-frequency adaptation plays a key role in separating transient signals from slower oscillatory signals. We recorded in vivo from very rapidly adapting electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus. The firing-frequency response of electroreceptors to fast communication stimuli ("small chirps") is strongly enhanced compared with the response to slower oscillations ("beats") arising from interactions of same-sex conspecifics. We are able to accurately predict the electroreceptor afferent response to chirps and beats, using a recently proposed general model for spike-frequency adaptation. The parameters of the model are determined for each neuron individually from the responses to step stimuli. We conclude that the dynamics of the rapid spike-frequency adaptation is sufficient to explain the data. Analysis of additional data from step responses demonstrates that spike-frequency adaptation acts subtractively rather than divisively as expected from depressing synapses. Therefore, the adaptation dynamics is linear and creates a high-pass filter with a cutoff frequency of 23 Hz that separates fast signals from slower changes in input. A similar critical frequency is seen in behavioral data on the probability of a fish emitting chirps as a function of beat frequency. These results demonstrate how spike-frequency adaptation in general can facilitate extraction of signals of different time scales, specifically high-frequency signals embedded in slower oscillations.

It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.

We present results from a novel experimental paradigm to investigate the influence of spatial correlations of stimuli on electrosensory neural network dynamics. Further, a new theoretical analysis for the dynamics of a model network of stochastic leaky integrate-and-fire neurons with delayed feedback is proposed. Experiment and theory for this system both establish that spatial correlations induce a network oscillation, the strength of which is proportional to the degree of stimulus correlation at constant total stimulus power., (Copyright 2004 The American Physical Society)

Information theory is playing an increasingly important role in the analysis of neural data as it can precisely quantify the reliability of stimulus-response functions. Estimating the mutual information between a neural spike train and a time varying stimulus is, however, not trivial in practice and requires assumptions about the specific computations being performed by the neuron under study. Consequently, estimates of the mutual information depend on these assumptions and their validity must be ascertained in the particular physiological context in which experiments are carried out. Here we compare results obtained using different information measures that make different assumptions about the neural code (i.e. the way information is being encoded and decoded) and the stimulus ensemble (i.e. the set of stimuli that the animal can encounter in nature). Our comparisons are carried out in the context of spontaneously active neurons. However, some of our results are also applicable to neurons that are not spontaneously active. We first show conditions under which a single stimulus provides a good sample of the entire stimulus ensemble. Furthermore, we use a recently introduced information measure that is based on the spontaneous activity of the neuron rather than on the stimulus ensemble. This measure is compared to the Shannon information and it is shown that the two differ only by a constant. This constant is shown to represent the information that the neuron's spontaneous activity transmits about the fact that no stimulus is present in the animal's environment. As a consequence, the mutual information measure based on spontaneous activity is easily applied to stimuli that mimic those seen in nature, as it does not require a priori knowledge of the stimulus ensemble. Finally, we consider the effect of noise in the animal's environment on information transmission about sensory stimuli. Our results show that, as expected, such 'background' noise will increase the trial-to-trial variability of the neural response to repeated presentations of a stimulus. However, the same background noise can also increase the variability of the spike train and hence can lead to increased information transfer in the presence of background noise. Our study emphasizes how different assumptions can lead to different predictions for the information transmission of a neuron. Assumptions about the computations being performed by the system under study as well as the stimulus ensemble and background noise should therefore be carefully considered when applying information theory.

Accurate detection of sensory input is essential for the survival of a species. Weakly electric fish use amplitude modulations of their self-generated electric field to probe their environment. P-type electroreceptors convert these modulations into trains of action potentials. Cumulative relative refractoriness in these afferents leads to negatively correlated successive interspike intervals (ISIs). We use simple and accurate models of P-unit firing to show that these refractory effects lead to a substantial increase in the animal's ability to detect sensory stimuli. This assessment is based on two approaches, signal detection theory and information theory. The former is appropriate for low-frequency stimuli, and the latter for high-frequency stimuli. For low frequencies, we find that signal detection is dependent on differences in mean firing rate and is optimal for a counting time at which spike train variability is minimal. Furthermore, we demonstrate that this minimum arises from the presence of negative ISI correlations at short lags and of positive ISI correlations that extend out to long lags. Although ISI correlations might be expected to reduce information transfer, in fact we find that they improve information transmission about time-varying stimuli. This is attributable to the differential effect that these correlations have on the noise and baseline entropies. Furthermore, the gain in information transmission rate attributable to correlations exhibits a resonance as a function of stimulus bandwidth; the maximum occurs when the inverse of the cutoff frequency of the stimulus is of the order of the decay time constant of refractory effects. Finally, we show that the loss of potential information caused by a decrease in spike-timing resolution is smaller for low stimulus cutoff frequencies than for high ones. This suggests that a rate code is used for the encoding of low-frequency stimuli, whereas spike timing is important for the encoding of high-frequency stimuli.

Weakly electric fish use an electric sense to navigate and capture prey in the dark. Objects in the surroundings of the fish produce distortions in their self-generated electric field; these distortions form a two-dimensional Gaussian-like electric image on the skin surface. To determine the distance of an object, the peak amplitude and width of its electric image must be estimated. These sensory features are encoded by a neuronal population in the early stages of the electrosensory pathway, but are not represented with classic bell-shaped neuronal tuning curves. In contrast, bell-shaped tuning curves do characterize the neuronal responses to the location of the electric image on the body surface, such that parallel two-dimensional maps of this feature are formed. In the case of such two-dimensional maps, theoretical results suggest that the width of neural tuning should have no effect on the accuracy of a population code. Here we show that although the spatial scale of the electrosensory maps does not affect the accuracy of encoding the body surface location of the electric image, maps with narrower tuning are better for estimating image width and those with wider tuning are better for estimating image amplitude. We quantitatively evaluate a two-step algorithm for distance perception involving the sequential estimation of peak amplitude and width of the electric image. This algorithm is best implemented by two neural maps with different tuning widths. These results suggest that multiple maps of sensory features may be specialized with different tuning widths, for encoding additional sensory features that are not explicitly mapped.

The PSD-95 family of membrane-associated guanylate kinase (MAGUK) proteins are involved in the assembly and organization of neurotransmitter receptors at excitatory synapses in the vertebrate nervous system. We have isolated partial cDNAs for five PSD-95 family members from Apteronotus leptorhynchus brain RNA using a degenerate PCR method. The amino acid sequences deduced indicate that A. leptorhynchus neurons express homologues of the mammalian PSD-93, SAP-97, and SAP-102 MAGUKs and two homologues of mammalian PSD-95. In situ hybridization experiments have been carried out to localize the cellular expression of all five MAGUK mRNAs in the central nervous system of A. leptorhynchus. In the cerebellum the expression patterns are highly similar to patterns reported for mammalian cerebellum, suggesting an evolutionary conservation of the functional roles in this gene family. Cellular levels of expression of the PSD-95 MAGUK mRNAs and the NMDAR-1 mRNA were highly correlated in neurons of the dorsal forebrain but were not correlated in neurons of the electrosensory lateral line lobe (ELL) or the cerebellum. These results suggest that the expression of PSD-95 MAGUK genes in forebrain neurons may provide mechanisms for synaptic organization that are not shared by neurons in the ELL and cerebellum., (Copyright 2000 Wiley-Liss, Inc.)

The sequence for cDNA encoding the NMDA receptor subunit 1 (aptNR1) of the weakly electric fish Apteronotus leptorhynchus has been determined. The deduced amino acid sequence is approximately 88% identical to other vertebrate NR1 proteins, with sequence homology extending to the alternatively spliced cassettes N1 and C1. The fish and mammalian N1 and C1 splice cassettes are identical at 20 of 21 and 30 of 37 amino acid positions, respectively. We did not detect a C2 splice cassette in aptNR1 mRNA, but we did find two novel C-terminal alternative splice cassettes labeled C1' and C1". The relative levels of NR1 transcripts containing the N1 and C1 splice cassettes were determined by using RNase protection and in situ hybridization analysis. N1-containing mRNAs are more abundant in caudal brain regions, similar to the patterns reported for mammalian brain. In contrast, the relative levels of transcripts containing the C1 splice cassette are much lower in fish than in mammals, averaging only 9% for the whole brain. The levels of C1 splicing increased in more rostral brain regions. In situ hybridizations with N1- and C1-specific probes demonstrated that N1 cassette splicing occurs in most neurons but that C1 splicing is heterogeneous and is restricted to a subset of neuronal types in the electrosensory system.

Immunohistochemical procedures were used to investigate the distribution of galanin-like immunoreactive neuronal somata, fiber pathways and apparent termination fields in the gymnotiform brain. Immunoreactive somata were observed only in the hypothalamus and were confined to preoptic, lateral and caudal hypothalamic regions. Within these areas, positive cells tended to be most concentrated in juxtaventricular nuclei. Dense immunoreactive fiber systems originating from hypothalamic regions were seen to project in separate or coalescing fiber bundles to the basal telencephalan, thalamus/tuberal diencephalon, midbrain and brainstem. The density of positive axons and boutons was quite variable, but regions which displayed the most massive network of axons included structures within the hypothalamus itself (anterior periventricular preoptic nucleus, caudal and lateral hypothalamus), ventral telencephalon (superior and ventral subdivisions), thalamic/tuberal areas (central posterior nucleus and tuberal neuropil within the ventral territory of the prepacemaker nucleus) and brainstem nuclei (dorsal reticular nucleus and the medial paralemniscal nucleus). Within these areas axons appeared more randomly distributed and varicose than along fiber tracs, and in counterstained sections were occasionally seen in apposition to unstained neuronal cell bodies and dendrites. In addition, a system of fibers was seen in the neurointermediate lobe of the pituitary. It is concluded that galanin-like immunoreactive neurons in the gymnotiform brain have a more restricted distribution than those in mammals, and that the major fiber systems emanating from the hypothalamus resemble the diverse projections of the tuberomammillary nucleus of higher vertebrates. The anatomy of galanin-like immunoreactive systems in the apteronotid brain suggests a role in neuroendocrine regulation and an involvement with anatomical areas controlling aggressive and courtship behaviour.