Active sensing: Eavesdropping on your neighbor to locate prey.

When animals using active sensing, e.g., sonar or an electric organ discharge, cooperate while foraging, the emitted sound or electric field is available to neighboring conspecifics. Experimental and modelling studies have shown that an electric fish can use the discharge of neighbors to extend their own electrosensory prey detection range.

Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model.

Hilar mossy cells (hMCs) in the dentate gyrus (DG) receive inputs from DG granule cells (GCs), CA3 pyramidal cells and inhibitory interneurons, and provide feedback input to GCs. Behavioural and in vivo recording experiments implicate hMCs in pattern separation, navigation and spatial learning. Our experiments link hMC intrinsic excitability to their synaptically evoked in vivo spiking outputs. We performed electrophysiological recordings from DG neurons and found that hMCs displayed an adaptative spike threshold that increased both in proportion to the intensity of injected currents, and in response to spiking itself, returning to baseline over a long time scale, thereby instantaneously limiting their firing rate responses. The hMC activity is additionally limited by a prominent medium after-hyperpolarizing potential (AHP) generated by small conductance K+ channels. We hypothesize that these intrinsic hMC properties are responsible for their low in vivo firing rates. Our findings extend previous studies that compare hMCs, CA3 pyramidal cells and hilar inhibitory cells and provide novel quantitative data that contrast the intrinsic properties of these cell types. We developed a phenomenological exponential integrate-and-fire model that closely reproduces the hMC adaptive threshold nonlinearities with respect to their threshold dependence on input current intensity, evoked spike latency and long-lasting spike-induced increase in spike threshold. Our robust and computationally efficient model is amenable to incorporation into large network models of the DG that will deepen our understanding of the neural bases of pattern separation, spatial navigation and learning. KEY POINTS: Previous studies have shown that hilar mossy cells (hMCs) are implicated in pattern separation and the formation of spatial memory, but how their intrinsic properties relate to their in vivo spiking patterns is still unknown. Here we show that the hMCs display electrophysiological properties that distinguish them from the other hilar cell types including a highly adaptive spike threshold that decays slowly. The spike-dependent increase in threshold combined with an after-hyperpolarizing potential mediated by a slow K+ conductance is hypothesized to be responsible for the low-firing rate of the hMC observed in vivo. The hMC's features are well captured by a modified stochastic exponential integrate-and-fire model that has the unique feature of a threshold intrinsically dependant on both the stimulus intensity and the spiking history. This computational model will allow future work to study how the hMCs can contribute to spatial memory formation and navigation.

Active sensing: How to eliminate self-generated noise.

Animals use active sensing to investigate their environment. The active sense inputs must be discriminated from those arising independently from environmental signals. An experimental and modelling study has revealed how precise control of dendritic spike backpropagation contributes to such discrimination.

Finding food in the dark: how trajectories of a gymnotiform fish change with spatial learning.

We analyzed the trajectories of freely foraging Gymnotus sp., a pulse-type gymnotiform weakly electric fish, swimming in a dark arena. For each fish, we compared the its initial behavior as it learned the relative location of landmarks and food with its behavior after learning was complete, i.e. after time/distance to locate food had reached a minimal asymptotic level. During initial exploration when the fish did not know the arena layout, trajectories included many sharp angle head turns that occurred at nearly completely random intervals. After spatial learning was complete, head turns became far smoother. Interestingly, the fish still did not take a stereotyped direct route to the food but instead took smooth but variable curved trajectories. We also measured the fish's heading angle error (heading angle - heading angle towards food). After spatial learning, the fish's initial heading angle errors were strongly biased to zero, i.e. the fish mostly turned towards the food. As the fish approached closer to the food, they switched to a random search strategy with a more uniform distribution of heading angle errors.

Mixed selectivity coding of sensory and motor social signals in the thalamus of a weakly electric fish.

High-level neural activity often exhibits mixed selectivity to multivariate signals. How such representations arise and modulate natural behavior is poorly understood. We addressed this question in weakly electric fish, whose social behavior is relatively low dimensional and can be easily reproduced in the laboratory. We report that the preglomerular complex, a thalamic region exclusively connecting midbrain with pallium, implements a mixed selectivity strategy to encode interactions related to courtship and rivalry. We discuss how this code enables the pallial recurrent networks to control social behavior, including dominance in male-male competition and female mate selection. Notably, response latency analysis and computational modeling suggest that corollary discharge from premotor regions is implicated in flagging outgoing communications and thereby disambiguating self- versus non-self-generated signals. These findings provide new insights into the neural substrates of social behavior, multi-dimensional neural representation, and its role in perception and decision making.

Linking active sensing and spatial learning in weakly electric fish.

Weakly electric fish can learn the spatial layout of their environment using only their short-range electric sense. During spatial learning, active sensing motions are used to memorize landmark locations so that they can serve as anchors for idiothetic-based navigation. A hindbrain feedback circuit selectively amplifies the electrosensory input arising from these motions. The ascending electrolocation pathway preferentially transmits this information to the pallial regions involved in spatial learning and navigation. Similarities in both behavioral patterns and hindbrain circuitry of gymnotiform and mormyrid fish, two families that independently evolved their electrosense, suggest that amplification and transmission of active sensing motion inputs are fundamental mechanisms for spatial memory acquisition.

Distribution of the cholinergic nuclei in the brain of the weakly electric fish, Apteronotus leptorhynchus: Implications for sensory processing.

Acetylcholine acts as a neurotransmitter/neuromodulator of many central nervous system processes such as learning and memory, attention, motor control, and sensory processing. The present study describes the spatial distribution of cholinergic neurons throughout the brain of the weakly electric fish, Apteronotus leptorhynchus, using in situ hybridization of choline acetyltransferase mRNA. Distinct groups of cholinergic cells were observed in the telencephalon, diencephalon, mesencephalon, and hindbrain. These included cholinergic cell groups typically identified in other vertebrate brains, for example, motor neurons. Using both in vitro and ex vivo neuronal tracing methods, we identified two new cholinergic connections leading to novel hypotheses on their functional significance. Projections to the nucleus praeeminentialis (nP) arise from isthmic nuclei, possibly including the nucleus lateralis valvulae (nLV) and the isthmic nucleus (nI). The nP is a central component of all electrosensory feedback pathways to the electrosensory lateral line lobe (ELL). We have previously shown that some neurons in nP, TS, and tectum express muscarinic receptors. We hypothesize that, based on nLV/nI cell responses in other teleosts and isthmic connectivity in A. leptorhynchus, the isthmic connections to nP, TS, and tectum modulate responses to electrosensory and/or visual motion and, in particular, to looming/receding stimuli. In addition, we found that the octavolateral efferent (OE) nucleus is the likely source of cholinergic fibers innervating the ELL. In other teleosts, OE inhibits octavolateral hair cells during locomotion. In gymnotiform fish, OE may also act on the first central processing stage and, we hypothesize, implement corollary discharge modulation of electrosensory processing during locomotion.

Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals.

Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We analyze a phenomenon in these models where such statistically dependent ISI sequences arise in tandem with quasi-statistically independent and identically distributed (quasi-IID) adaptation variable sequences. The sequences of adaptation states and resulting ISIs are linked by a nonlinear decorrelating transformation. We establish general conditions on a family of stochastic spiking models that guarantee this quasi-IID property and establish bounds on the resulting baseline ISI correlations. Inputs that elicit weak firing rate changes in samples with many spikes are known to be more detectible when negative ISI correlations are present because they reduce spike count variance; this defines a variance-reduced firing rate coding benchmark. We performed a Fisher information analysis on these adapting models exhibiting ISI correlations to show that a spike pattern code based on the quasi-IID property achieves the upper bound of detection performance, surpassing rate codes with the same mean rate-including the variance-reduced rate code benchmark-by 20% to 30%. The information loss in rate codes arises because the benefits of reduced spike count variance cannot compensate for the lower firing rate gain due to adaptation. Since adaptation states have similar dynamics to synaptic responses, the quasi-IID decorrelation transformation of the spike train is plausibly implemented by downstream neurons through matched postsynaptic kinetics. This provides an explanation for observed coding performance in sensory systems that cannot be accounted for by rate coding, for example, at the detection threshold where rate changes can be insignificant.

Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals.

Artificial multi-layer networks can learn difficult tasks, such as recognizing faces, but their architecture and learning rules appear to be very different from those of biological neural networks. Experimental and computational studies of a two-layered biological neural network have revealed how the learning rules used in artificial neural networks can be efficiently implemented by neurons with complex dynamics and precisely organized connectivity.

Cellular and Network Mechanisms May Generate Sparse Coding of Sequential Object Encounters in Hippocampal-Like Circuits.

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.

Publisher Correction: Transparent Danionella translucida as a genetically tractable vertebrate brain model.

The version of this paper originally published contained errors in reference citations: in the first paragraph of the Results section, the text "This extent of optical clarity probably results from the absence of skull above the brain22. In our specimens, Nissl-stained coronal sections through the head showed that the skull surrounds the brain only laterally and ventrally" should have read "This extent of optical clarity probably results from the absence of skull above the brain21. In our specimens, Nissl-stained coronal sections through the head22 showed that the skull surrounds the brain only laterally and ventrally." In addition, the unit abbreviation "µm" was incorrectly divided at a line break in the third paragraph of the Discussion, which might have led to some confusion. These errors have been corrected in the PDF and HTML versions of the article.

Transparent Danionella translucida as a genetically tractable vertebrate brain model.

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.

Brain Evolution: Intelligence without a Cortex.

Birds are capable of high level cognition even though their telencephalon is organized into nuclear groups rather than a six-layered cortex as in the mammalian brain. New data show that, despite their different macroscale organization, the circuitries of avian and mammalian telencephalon are fundamentally similar and may therefore carry out equivalent computations.

Feedback Synthesizes Neural Codes for Motion.

In senses as diverse as vision, hearing, touch, and the electrosense, sensory neurons receive bottom-up input from the environment, as well as top-down input from feedback loops involving higher brain regions [1-4]. Through connectivity with local inhibitory interneurons, these feedback loops can exert both positive and negative control over fundamental aspects of neural coding, including bursting [5, 6] and synchronous population activity [7, 8]. Here we show that a prominent midbrain feedback loop synthesizes a neural code for motion reversal in the hindbrain electrosensory ON- and OFF-type pyramidal cells. This top-down mechanism generates an accurate bidirectional encoding of object position, despite the inability of the electrosensory afferents to generate a consistent bottom-up representation [9, 10]. The net positive activity of this midbrain feedback is additionally regulated through a hindbrain feedback loop, which reduces stimulus-induced bursting and also dampens the ON and OFF cell responses to interfering sensory input [11]. We demonstrate that synthesis of motion representations and cancellation of distracting signals are mediated simultaneously by feedback, satisfying an accepted definition of spatial attention [12]. The balance of excitatory and inhibitory feedback establishes a "focal" distance for optimized neural coding, whose connection to a classic motion-tracking behavior provides new insight into the computational roles of feedback and active dendrites in spatial localization [13, 14].

Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States.

The neural basis of spontaneous movement generation is a fascinating open question. Long-term monitoring of fish, swimming freely in a constant sensory environment, has revealed a sequence of behavioral states that alternate randomly and spontaneously between periods of activity and inactivity. We show that key dynamical features of this sequence are captured by a 1-D diffusion process evolving in a nonlinear double well energy landscape, in which a slow variable modulates the relative depth of the wells. This combination of stochasticity, nonlinearity, and nonstationary forcing correctly captures the vastly different timescales of fluctuations observed in the data (∼1 to ∼1000 s), and yields long-tailed residence time distributions (RTDs) also consistent with the data. In fact, our model provides a simple mechanism for the emergence of long-tailed distributions in spontaneous animal behavior. We interpret the stochastic variable of this dynamical model as a decision-like variable that, upon reaching a threshold, triggers the transition between states. Our main finding is thus the identification of a threshold crossing process as the mechanism governing spontaneous movement initiation and termination, and to infer the presence of underlying nonstationary agents. Another important outcome of our work is a dimensionality reduction scheme that allows similar segments of data to be grouped together. This is done by first extracting geometrical features in the dataset and then applying principal component analysis over the feature space. Our study is novel in its ability to model nonstationary behavioral data over a wide range of timescales.

Hippocampal-like circuitry in the pallium of an electric fish: Possible substrates for recursive pattern separation and completion.

Teleost fish are capable of complex behaviors, including social and spatial learning; lesion studies show that these abilities require dorsal telencephalon (pallium). The teleost telencephalon has subpallial and pallial components. The subpallium is well described and highly conserved. In contrast, the teleost pallium is not well understood and its relation to that of other vertebrates remains controversial. Here we analyze the connectivity of the subdivisions of dorsal pallium (DD) of an electric gymnotiform fish, Apteronotus leptorhynchus: superficial (DDs), intermediate (DDi) and magnocellular (DDmg) components. The major pathways are recursive: the dorsolateral pallium (DL) projects strongly to DDi, with lesser inputs to DDs and DDmg. DDi in turn projects strongly to DDmg, which then feeds back diffusely to DL. Our quantitative analysis of DDi connectivity demonstrates that it is a global recurrent network. In addition, we show that the DD subnuclei have complex reciprocal connections with subpallial regions. Specifically, both DDi and DDmg are reciprocally connected to pallial interneurons within the misnamed rostral entopeduncular nucleus (Er). Based on DD connectivity, we illustrate the close similarity, and possible homology, between hippocampal and DD/DL circuitry. We hypothesize that DD/DL circuitry can implement the same pattern separation and completion computations ascribed to the hippocampal dentate gyrus and CA3 fields. We further contend that the DL to DDi to DDmg to DL feedback loop makes the pattern separation/completion operations recursive. We discuss our results with respect to recent studies on fear avoidance conditioning in zebrafish and attention and spatial learning in a pulse gymnotiform fish. J. Comp. Neurol. 525:8-46, 2017. © 2016 Wiley Periodicals, Inc.

Subsecond Sensory Modulation of Serotonin Levels in a Primary Sensory Area and Its Relation to Ongoing Communication Behavior in a Weakly Electric Fish.

Serotonergic neurons of the raphe nuclei of vertebrates project to most regions of the brain and are known to significantly affect sensory processing. The subsecond dynamics of sensory modulation of serotonin levels and its relation to behavior, however, remain unknown. We used fast-scan cyclic voltammetry to measure serotonin release in the electrosensory system of weakly electric fish, Apteronotus leptorhynchus. These fish use an electric organ to generate a quasi-sinusoidal electric field for communicating with conspecifics. In response to conspecific signals, they frequently produce signal modulations called chirps. We measured changes in serotonin concentration in the hindbrain electrosensory lobe (ELL) with a resolution of 0.1 s concurrently with chirping behavior evoked by mimics of conspecific electric signals. We show that serotonin release can occur phase locked to stimulus onset as well as spontaneously in the ELL region responsible for processing these signals. Intense auditory stimuli, on the other hand, do not modulate serotonin levels in this region, suggesting modality specificity. We found no significant correlation between serotonin release and chirp production on a trial-by-trial basis. However, on average, in the trials where the fish chirped, there was a reduction in serotonin release in response to stimuli mimicking similar-sized same-sex conspecifics. We hypothesize that the serotonergic system is part of an intricate sensory-motor loop: serotonin release in a sensory area is triggered by sensory input, giving rise to motor output, which can in turn affect serotonin release at the timescale of the ongoing sensory experience and in a context-dependent manner.

Active sensing associated with spatial learning reveals memory-based attention in an electric fish.

Active sensing behaviors reveal what an animal is attending to and how it changes with learning. Gymnotus sp, a gymnotiform weakly electric fish, generates an electric organ discharge (EOD) as discrete pulses to actively sense its surroundings. We monitored freely behaving gymnotid fish in a large dark "maze" and extracted their trajectories and EOD pulse pattern and rate while they learned to find food with electrically detectable landmarks as cues. After training, they more rapidly found food using shorter, more stereotyped trajectories and spent more time near the food location. We observed three forms of active sensing: sustained high EOD rates per unit distance (sampling density), transient large increases in EOD rate (E-scans) and stereotyped scanning movements (B-scans) were initially strong at landmarks and food, but, after learning, intensified only at the food location. During probe (no food) trials, after learning, the fish's search area and intense active sampling was still centered on the missing food location, but now also increased near landmarks. We hypothesize that active sensing is a behavioral manifestation of attention and essential for spatial learning; the fish use spatial memory of landmarks and path integration to reach the expected food location and confine their attention to this region.

Weak signal amplification and detection by higher-order sensory neurons.

Sensory systems must extract behaviorally relevant information and therefore often exhibit a very high sensitivity. How the nervous system reaches such high sensitivity levels is an outstanding question in neuroscience. Weakly electric fish (Apteronotus leptorhynchus/albifrons) are an excellent model system to address this question because detailed background knowledge is available regarding their behavioral performance and its underlying neuronal substrate. Apteronotus use their electrosense to detect prey objects. Therefore, they must be able to detect electrical signals as low as 1 µV while using a sensory integration time of <200 ms. How these very weak signals are extracted and amplified by the nervous system is not yet understood. We studied the responses of cells in the early sensory processing areas, namely, the electroreceptor afferents (EAs) and pyramidal cells (PCs) of the electrosensory lobe (ELL), the first-order electrosensory processing area. In agreement with previous work we found that EAs cannot encode very weak signals with a spike count code. However, PCs can encode prey mimic signals by their firing rate, revealing a huge signal amplification between EAs and PCs and also suggesting differences in their stimulus encoding properties. Using a simple leaky integrate-and-fire (LIF) model we predict that the target neurons of PCs in the midbrain torus semicircularis (TS) are able to detect very weak signals. In particular, TS neurons could do so by assuming biologically plausible convergence rates as well as very simple decoding strategies such as temporal integration, threshold crossing, and combining the inputs of PCs.

Balanced ionotropic receptor dynamics support signal estimation via voltage-dependent membrane noise.

Encoding behaviorally relevant stimuli in a noisy background is critical for animals to survive in their natural environment. We identify core biophysical and synaptic mechanisms that permit the encoding of low-frequency signals in pyramidal neurons of the weakly electric fish Apteronotus leptorhynchus, an animal that can accurately encode even miniscule amplitude modulations of its self-generated electric field. We demonstrate that slow NMDA receptor (NMDA-R)-mediated excitatory postsynaptic potentials (EPSPs) are able to summate over many interspike intervals (ISIs) of the primary electrosensory afferents (EAs), effectively eliminating the baseline EA ISI correlations from the pyramidal cell input. Together with a dynamic balance of NMDA-R and GABA-A-R currents, this permits stimulus-evoked changes in EA spiking to be transmitted efficiently to target electrosensory lobe (ELL) pyramidal cells, for encoding low-frequency signals. Interestingly, AMPA-R activity is depressed and appears to play a negligible role in the generation of action potentials. Instead, we hypothesize that cell-intrinsic voltage-dependent membrane noise supports the encoding of perithreshold sensory input; this noise drives a significant proportion of pyramidal cell spikes. Together, these mechanisms may be sufficient for the ELL to encode signals near the threshold of behavioral detection.