Neurobiology and Changing Ecosystems: Mechanisms Underlying Responses to Human-Generated Environmental Impacts.

Human generated environmental change profoundly affects organisms that reside across diverse ecosystems. Although nervous systems evolved to flexibly sense, respond, and adapt to environmental change, it is unclear whether the rapid rate of environmental change outpaces the adaptive capacity of complex nervous systems. Here, we explore neural systems mediating responses to, or impacted by, changing environments, such as those induced by global heating, sensory pollution, and changing habitation zones. We focus on rising temperature and accelerated changes in environments that impact sensory experience as examples of perturbations that directly or indirectly impact neural function, respectively. We also explore a mechanism involved in cross-species interactions that arises from changing habitation zones. We demonstrate that anthropogenic influences on neurons, circuits, and behaviors are widespread across taxa and require further scientific investigation to understand principles underlying neural resilience to accelerating environmental change.SIGNIFICANCE STATEMENT Neural systems evolved over hundreds of millions of years to allow organisms to sense and respond to their environments - to be receptive and responsive, yet flexible. Recent rapid, human-generated environmental changes are testing the limits of the adaptive capacity of neural systems. This presents an opportunity and an urgency to understand how neurobiological processes, including molecular, cellular, and circuit-level mechanisms, are vulnerable or resilient to changing environmental conditions. We showcase examples that range from molecular to circuit to behavioral levels of analysis across several model species, framing a broad neuroscientific approach to explore topics of neural adaptation, plasticity, and resilience. We believe this emerging scientific area is of great societal and scientific importance and will provide a unique opportunity to reexamine our understanding of neural adaptation and the mechanisms underlying neural resilience.

Gap junctions: The missing piece of the connectome.

The central pattern generator that controls flying power in Drosophila requires desynchronized firing to drive a steady wingbeat frequency. A new study reveals how gap junctions are the key to desynchronizing the motor neurons.

Connectomic analysis of the Drosophila lateral neuron clock cells reveals the synaptic basis of functional pacemaker classes.

The circadian clock orchestrates daily changes in physiology and behavior to ensure internal temporal order and optimal timing across the day. In animals, a central brain clock coordinates circadian rhythms throughout the body and is characterized by a remarkable robustness that depends on synaptic connections between constituent neurons. The clock neuron network of Drosophila, which shares network motifs with clock networks in the mammalian brain yet is built of many fewer neurons, offers a powerful model for understanding the network properties of circadian timekeeping. Here, we report an assessment of synaptic connectivity within a clock network, focusing on the critical lateral neuron (LN) clock neuron classes within the Janelia hemibrain dataset. Our results reveal that previously identified anatomical and functional subclasses of LNs represent distinct connectomic types. Moreover, we identify a small number of non-clock cell subtypes representing highly synaptically coupled nodes within the clock neuron network. This suggests that neurons lacking molecular timekeeping likely play integral roles within the circadian timekeeping network. To our knowledge, this represents the first comprehensive connectomic analysis of a circadian neuronal network.

Most organisms on Earth possess an internal timekeeping system which ensures that bodily processes such as sleep, wakefulness or digestion take place at the right time. These precise daily rhythms are kept in check by a master clock in the brain. There, thousands of neurons ­ some of which carrying an internal 'molecular clock' ­ connect to each other through structures known as synapses. Exactly how the resulting network is organised to support circadian timekeeping remains unclear. To explore this question, Shafer, Gutierrez et al. focused on fruit flies, as recent efforts have systematically mapped every neuron and synaptic connection in the brain of this model organism. Analysing available data from the hemibrain connectome project at Janelia revealed that that the neurons with the most important timekeeping roles were in fact forming the fewest synapses within the network. In addition, neurons without internal molecular clocks mediated strong synaptic connections between those that did, suggesting that 'clockless' cells still play an integral role in circadian timekeeping. With this research, Shafer, Gutierrez et al. provide unexpected insights into the organisation of the master body clock. Better understanding the networks that underpin circadian rhythms will help to grasp how and why these are disrupted in obesity, depression and Alzheimer's disease.

Nonlinear convergence boosts information coding in circuits with parallel outputs.

Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.

Population adaptation in efficient balanced networks.

Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will redistribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.

Complicating connectomes: Electrical coupling creates parallel pathways and degenerate circuit mechanisms.

Electrical coupling in circuits can produce non-intuitive circuit dynamics, as seen in both experimental work from the crustacean stomatogastric ganglion and in computational models inspired by the connectivity in this preparation. Ambiguities in interpreting the results of electrophysiological recordings can arise if sets of pre- or postsynaptic neurons are electrically coupled, or if the electrical coupling exhibits some specificity (e.g. rectifying, or voltage-dependent). Even in small circuits, electrical coupling can produce parallel pathways that can allow information to travel by monosynaptic and/or polysynaptic pathways. Consequently, similar changes in circuit dynamics can arise from entirely different underlying mechanisms. When neurons are coupled both chemically and electrically, modifying the relative strengths of the two interactions provides a mechanism for flexibility in circuit outputs. This, together with neuromodulation of gap junctions and coupled neurons is important both in developing and adult circuits. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 597-609, 2017.

Modulation of a Single Neuron Has State-Dependent Actions on Circuit Dynamics,.

When does neuromodulation of a single neuron influence the output of the entire network? We constructed a five-cell circuit in which a neuron is at the center of the circuit and the remaining neurons form two distinct oscillatory subnetworks. All neurons were modeled as modified Morris-Lecar models with a hyperpolarization-activated conductance (gh ) in addition to calcium (gCa ), potassium (gK ), and leak conductances. We determined the effects of varying gCa , gK , and gh on the frequency, amplitude, and duty cycle of a single neuron oscillator. The frequency of the single neuron was highest when the gK and gh conductances were high and gCa was moderate whereas, in the traditional Morris-Lecar model, the highest frequencies occur when both gK and gCa are high. We randomly sampled parameter space to find 143 hub oscillators with nearly identical frequencies but with disparate maximal conductance, duty cycles, and burst amplitudes, and then embedded each of these hub neurons into networks with different sets of synaptic parameters. For one set of network parameters, circuit behavior was virtually identical regardless of the underlying conductances of the hub neuron. For a different set of network parameters, circuit behavior varied with the maximal conductances of the hub neuron. This demonstrates that neuromodulation of a single target neuron may dramatically alter the performance of an entire network when the network is in one state, but have almost no effect when the circuit is in a different state.

Rectifying electrical synapses can affect the influence of synaptic modulation on output pattern robustness.

Rectifying electrical synapses are commonplace, but surprisingly little is known about how rectification alters the dynamics of neuronal networks. In this study, we use computational models to investigate how rectifying electrical synapses change the behavior of a small neuronal network that exhibits complex rhythmic output patterns. We begin with an electrically coupled circuit of three oscillatory neurons with different starting frequencies, and subsequently add two additional neurons and inhibitory chemical synapses. The five-cell model represents a pattern-generating neuronal network with two simultaneous rhythms competing for the recruitment of a hub neuron. We compare four different configurations of rectifying synapse placement and polarity, and we investigate how rectification changes the functional output of this network. Rectification can have a striking effect on the network's sensitivity to alterations of the strengths of the chemical synapses in the network. For some configurations, the rectification makes the circuit dynamics remarkably robust against changes in synaptic strength compared with the nonrectifying case. Based on our findings, we predict that modulation of rectifying electrical synapses could have functional consequences for the neuronal circuits that express them.

Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators.

Rhythmic oscillations are common features of nervous systems. One of the fundamental questions posed by these rhythms is how individual neurons or groups of neurons are recruited into different network oscillations. We modeled competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. We explore the patterns of coordination shown in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "parameterscape." The hub neuron can be switched between the fast and slow oscillators by multiple network mechanisms, indicating that a given change in network state can be achieved by degenerate cellular mechanisms. These results have importance for interpreting experiments employing optogenetic, genetic, and pharmacological manipulations to understand circuit dynamics.

Functional connectivity in a rhythmic inhibitory circuit using Granger causality.

BACKGROUND: Understanding circuit function would be greatly facilitated by methods that allow the simultaneous estimation of the functional strengths of all of the synapses in the network during ongoing network activity. Towards that end, we used Granger causality analysis on electrical recordings from the pyloric network of the crab Cancer borealis, a small rhythmic circuit with known connectivity, and known neuronal intrinsic properties. RESULTS: Granger causality analysis reported a causal relationship where there is no anatomical correlate because of the strong oscillatory behavior of the pyloric circuit. Additionally, we failed to find a direct relationship between synaptic strength and Granger causality in a set of pyloric circuit models. CONCLUSIONS: We conclude that the lack of a relationship between synaptic strength and functional connectivity occurs because Granger causality essentially collapses the direct contribution of the synapse with the intrinsic properties of the postsynaptic neuron. We suggest that the richness of the dynamical properties of most biological neurons complicates the simple interpretation of the results of functional connectivity analyses using Granger causality.

Cancer borealis stomatogastric nervous system dissection.

The stomatogastric ganglion (STG) is an excellent model for studying cellular and network interactions because it contains a relatively small number of cells (approximately 25 in C. borealis) which are well characterized. The cells in the STG exhibit a broad range of outputs and are responsible for the motor actions of the stomach. The stomach contains the gastric mill which breaks down food with three internal teeth, and the pylorus which filters the food before it reaches the midgut. The STG produces two rhythmic outputs to control the gastric mill and pylorus known as central pattern generators (CPGs). Each cell in the STG can participate in one or both of these rhythms. These CPGs allow for the study of neuromodulation, homeostasis, cellular and network variability, network development, and network recovery. The dissection of the stomatogastric nervous system (STNS) from the Jonah crab (Cancer borealis) is done in two parts; the gross and fine dissection. In the gross dissection the entire stomach is dissected from the crab. During the fine dissection the STNS is extracted from the stomach using a dissection microscope and micro-dissection tools (see figure 1). The STNS includes the STG, the oesophageal ganglion (OG), and the commissural ganglia (CoG) as well as the nerves that innervate the stomach muscles. Here, we show how to perform a complete dissection of the STNS in preparation for an electrophysiology experiment where the cells in the STG would be recorded from intracellularly and the peripheral nerves would be used for extracellular recordings. The proper technique for finding the desired nerves is shown as well as our technique of desheathing the ganglion to reveal the somata and neuropil.