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.

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.

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.

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.

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.

Contrast coding in the electrosensory system: parallels with visual computation.

To identify and interact with moving objects, including other members of the same species, an animal's nervous system must correctly interpret patterns of contrast in the physical signals (such as light or sound) that it receives from the environment. In weakly electric fish, the motion of objects in the environment and social interactions with other fish create complex patterns of contrast in the electric fields that they produce and detect. These contrast patterns can extend widely over space and time and represent a multitude of relevant features, as is also true for other sensory systems. Mounting evidence suggests that the computational principles underlying contrast coding in electrosensory neural networks are conserved elements of spatiotemporal processing that show strong parallels with the vertebrate visual system.

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.

Speed-invariant encoding of looming object distance requires power law spike rate adaptation.

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.

A neural code for looming and receding motion is distributed over a population of electrosensory ON and OFF contrast cells.

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.

Stimulus-induced up states in the dorsal pallium of a weakly electric fish.

We investigated the response of putative novelty-detecting neurons in the pallium of an electric fish to electrosensory and acoustic stimuli. Extracellular and whole cell patch recordings were made from neurons in the dorsal pallial nucleus (DD) of Apteronotus leptorhynchus. DD neurons were typically quiescent and exhibited hyperpolarized resting membrane potentials. Stimulation induced, with a variable long latency, rapid though transient depolarization and spike discharge. The transition between resting and depolarized/spiking states resembled the transition to Up states seen in mammalian telencephalic neurons.

Enhanced sensory sampling precedes self-initiated locomotion in an electric fish.

Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition. Preparatory neural activity is known to be associated with decision making and movement planning. Self-initiated locomotion has been linked to increased active sensory sampling; however, the precise temporal relationship between sensory acquisition and voluntary movement initiation has not been established. Based on long-term monitoring of sensory sampling activity that is readily observable in freely behaving pulse-type electric fish, we show that heightened sensory acquisition precedes spontaneous initiation of swimming. Gymnotus sp. revealed a bimodal distribution of electric organ discharge rate (EODR) demonstrating down- and up-states of sensory sampling and neural activity; movements only occurred during up-states and up-states were initiated before movement onset. EODR during voluntary swimming initiation exhibited greater trial-to-trial variability than the sound-evoked increases in EODR. The sampling variability declined after voluntary movement onset as previously observed for the neural variability associated with decision making in primates. Spontaneous movements occurred randomly without a characteristic timescale, and no significant temporal correlation was found between successive movement intervals. Using statistical analyses of spontaneous exploratory behaviours and associated preparatory sensory sampling increase, we conclude that electric fish exhibit key attributes of volitional movements, and that voluntary behaviours in vertebrates may generally be preceded by increased sensory sampling. Our results suggest that comparative studies of the neural basis of volition may therefore be possible in pulse-type electric fish, given the substantial homologies between the telencephali of teleost fish and mammals.

Learning contrast-invariant cancellation of redundant signals in neural systems.

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

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.

Dendritic SK channels convert NMDA-R-dependent LTD to burst timing-dependent plasticity.

Feedback and descending projections from higher to lower brain centers play a prominent role in all vertebrate sensory systems. Feedback might be optimized for the specific sensory processing tasks in their target brain centers, but it has been difficult to connect the properties of feedback synapses to sensory tasks. Here, we use the electrosensory system of a gymnotiform fish (Apteronotus leptorhynchus) to address this problem. Cerebellar feedback to pyramidal cells in the first central electrosensory processing region, the electrosensory lateral line lobe (ELL), is critical for canceling spatially and temporally redundant electrosensory input. The ELL contains four electrosensory maps, and we have previously analyzed the synaptic and network bases of the redundancy reduction mechanism in a map (centrolateral segment; CLS) believed to guide electrolocation behavior. In the CLS, only long-term depression was induced by pairing feedback presynaptic and pyramidal cell postsynaptic bursts. In this paper, we turn to an ELL map (lateral segment; LS) known to encode electrocommunication signals. We find remarkable differences in synaptic plasticity of the morphologically identical cerebellar feedback input to the LS. In the LS, pyramidal cell SK channels permit long-term potentiation (LTP) of feedback synapses when pre- and postsynaptic bursts occur at the same time. We hypothesize that LTP in this map is required for enhancing the encoding of weak electrocommunication signals. We conclude that feedback inputs that appear morphologically identical in sensory maps dedicated to different tasks, nevertheless display different synaptic plasticity rules contributing to differential sensory processing in these maps.

Biophysical information representation in temporally correlated spike trains.

Spike trains commonly exhibit interspike interval (ISI) correlations caused by spike-activated adaptation currents. Here we investigate how the dynamics of adaptation currents can represent spike pattern information generated from stimulus inputs. By analyzing dynamical models of stimulus-driven single neurons, we show that the activation states of the correlation-inducing adaptation current are themselves statistically independent from spike to spike. This paradoxical finding suggests a biophysically plausible means of information representation. We show that adaptation independence is elicited by input levels that produce regular, non-Poisson spiking. This adaptation-independent regime is advantageous for sensory processing because it does not require sensory inferences on the basis of multivariate conditional probabilities, reducing the computational cost of decoding. Furthermore, if the kinetics of postsynaptic activation are similar to the adaptation, the activation state information can be communicated postsynaptically with no information loss, leading to an experimental prediction that simple synaptic kinetics can decorrelate the correlated ISI sequence. The adaptation-independence regime may underly efficient weak signal detection by sensory afferents that are known to exhibit intrinsic correlated spiking, thus efficiently encoding stimulus information at the limit of physical resolution.

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.

Routing the flow of sensory signals using plastic responses to bursts and isolated spikes: experiment and theory.

Processing complex sensory environments efficiently requires a diverse array of neural coding strategies. Neural codes relying on specific temporal patterning of action potentials may offer advantages over using solely spike rate codes. In particular, stimulus-dependent burst firing may carry additional information that isolated spikes do not. We use the well characterized electrosensory system of weakly electric fish to address how stimulus-dependent burst firing can determine the flow of information in feedforward neural circuits with different forms of short-term synaptic plasticity. Pyramidal cells in the electrosensory lateral line lobe burst in response to low-frequency, local (prey) signals. We show that the ability of pyramidal cells to code for local signals in the presence of additional high-frequency, global (communication) stimuli is uncompromised, while burst firing is reduced. We developed a bursting neuron model to understand how these effects, in particular noise-induced burst suppression, arise from interplay between incoming sensory signals and intrinsic neuronal dynamics. Finally, we examined how postsynaptic target populations preferentially respond to one of the two sensory mixtures (local vs local plus global) depending on whether the populations are in receipt of facilitating or depressing synapses. This form of feedforward neural architecture may allow for efficient information flow in the same neural pathway via either isolated or burst spikes, where the mechanisms by which stimuli are encoded are adaptable and sensitive to a diverse array of stimulus and contextual mixtures.

Frequency-tuned cerebellar channels and burst-induced LTD lead to the cancellation of redundant sensory inputs.

For optimal sensory processing, neural circuits must extract novel, unpredictable signals from the redundant sensory input in which they are embedded, but the detailed cellular and network mechanisms that implement such selective cancellation are presently unknown. Using a combination of modeling and experiment, we characterize in detail a cerebellar circuit in weakly electric fish, showing how it can carry out this computation. We use a model incorporating the wide range of experimentally estimated parallel fiber feedback delays and a burst-induced LTD rule derived from in vitro experiments to explain the precise cancellation of redundant signals observed in vivo. Our model demonstrates how the backpropagation-dependent burst dynamics adjusts the temporal pairing width of the plasticity mechanism to precisely match the frequency of the redundant signal. The model also makes the prediction that this cerebellar feedback pathway must be composed of frequency-tuned channels; this prediction is subsequently verified in vivo, highlighting a novel and general capability of cerebellar circuitry.