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

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.

Ongoing habenular activity is driven by forebrain networks and modulated by olfactory stimuli

Ongoing neural activity, which represents internal brain states, is constantly modulated by the sensory information that is generated by the environment. In this study, we show that the habenular circuits act as a major brain hub integrating the structured ongoing activity of the limbic forebrain circuitry and the olfactory information. We demonstrate that ancestral homologs of amygdala and hippocampus in zebrafish forebrain are the major drivers of ongoing habenular activity. We also reveal that odor stimuli can modulate the activity of specific habenular neurons that are driven by this forebrain circuitry. Our results highlight a major role for the olfactory system in regulating the ongoing activity of the habenula and the forebrain, thereby altering brain's internal states.

Neural activity in a hippocampus-like region of the teleost pallium is associated with active sensing and navigation

Most vertebrates use active sensing strategies for perception, cognition and control of motor activity. These strategies include directed body/sensor movements or increases in discrete sensory sampling events. The weakly electric fish, Gymnotus sp., uses its active electric sense during navigation in the dark. Electric organ discharge rate undergoes transient increases during navigation to increase electrosensory sampling. Gymnotus also use stereotyped backward swimming as an important form of active sensing that brings objects toward the electroreceptor dense fovea-like head region. We wirelessly recorded neural activity from the pallium of freely swimming Gymnotus. Spiking activity was sparse and occurred only during swimming. Notably, most units tended to fire during backward swims and their activity was on average coupled to increases in sensory sampling. Our results provide the first characterization of neural activity in a hippocampal (CA3)-like region of a teleost fish brain and connects it to active sensing of spatial environmental features.

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.

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.