Sensory neuron population expansion enhances odor tracking without sensitizing projection neurons.

The evolutionary expansion of sensory neuron populations detecting important environmental cues is widespread, but functionally enigmatic. We investigated this phenomenon through comparison of homologous neural pathways of Drosophila melanogaster and its close relative Drosophila sechellia , an extreme specialist for Morinda citrifolia noni fruit. D. sechellia has evolved species-specific expansions in select, noni-detecting olfactory sensory neuron (OSN) populations, through multigenic changes. Activation and inhibition of defined proportions of neurons demonstrate that OSN population increases contribute to stronger, more persistent, noni-odor tracking behavior. These sensory neuron expansions result in increased synaptic connections with their projection neuron (PN) partners, which are conserved in number between species. Surprisingly, having more OSNs does not lead to greater odor-evoked PN sensitivity or reliability. Rather, pathways with increased sensory pooling exhibit reduced PN adaptation, likely through weakened lateral inhibition. Our work reveals an unexpected functional impact of sensory neuron expansions to explain ecologically-relevant, species-specific behavior.

Local inhibition modulates learning-dependent song encoding in the songbird auditory cortex.

Changes in inhibition during development are well documented, but the role of inhibition in adult learning-related plasticity is not understood. In songbirds, vocal recognition learning alters the neural representation of songs across the auditory forebrain, including the caudomedial nidopallium (NCM), a region analogous to mammalian secondary auditory cortices. Here, we block local inhibition with the iontophoretic application of gabazine, while simultaneously measuring song-evoked spiking activity in NCM of European starlings trained to recognize sets of conspecific songs. We find that local inhibition differentially suppresses the responses to learned and unfamiliar songs and enhances spike-rate differences between learned categories of songs. These learning-dependent response patterns emerge, in part, through inhibitory modulation of selectivity for song components and the masking of responses to specific acoustic features without altering spectrotemporal tuning. The results describe a novel form of inhibitory modulation of the encoding of learned categories and demonstrate that inhibition plays a central role in shaping the responses of neurons to learned, natural signals.

Emergence of learned categorical representations within an auditory forebrain circuit.

Many learned behaviors are thought to require the activity of high-level neurons that represent categories of complex signals, such as familiar faces or native speech sounds. How these complex, experience-dependent neural responses emerge within the brain's circuitry is not well understood. The caudomedial mesopallium (CMM), a secondary auditory region in the songbird brain, contains neurons that respond to specific combinations of song components and respond preferentially to the songs that birds have learned to recognize. Here, we examine the transformation of these learned responses across a broader forebrain circuit that includes the caudolateral mesopallium (CLM), an auditory region that provides input to CMM. We recorded extracellular single-unit activity in CLM and CMM in European starlings trained to recognize sets of conspecific songs and compared multiple encoding properties of neurons between these regions. We find that the responses of CMM neurons are more selective between song components, convey more information about song components, and are more variable over repeated components than the responses of CLM neurons. While learning enhances neural encoding of song components in both regions, CMM neurons encode more information about the learned categories associated with songs than do CLM neurons. Collectively, these data suggest that CLM and CMM are part of a functional sensory hierarchy that is modified by learning to yield representations of natural vocal signals that are increasingly informative with respect to behavior.

Connectomic features underlying diverse synaptic connection strengths and subcellular computation.

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.

Two-Photon Optogenetic Stimulation of Drosophila Neurons.

Optogenetics enables experimental control over neural activity using light. Channelrhodopsin and its variants are typically activated using visible light excitation but can also be activated using infrared two-photon excitation. Two-photon excitation can improve the spatial precision of stimulation in scattering tissue but has several practical limitations that need to be considered before use. Here we describe the methodology and best practices for using two-photon optogenetic stimulation of neurons within the brain of the fruit fly, Drosophila melanogaster, with an emphasis on projection neurons of the antennal lobe.

Decision making: An analogue implementation of a drift-diffusion computation in the Drosophila mushroom body.

A new study combines electrophysiology, optogenetics, and behavior to investigate a decision-making circuit in the fly brain, revealing all the major features predicted by drift-diffusion models. Strikingly, much of this computation takes place subthreshold, independent of action potentials.

An ammonium transporter is a non-canonical olfactory receptor for ammonia.

Numerous hematophagous insects are attracted to ammonia, a volatile released in human sweat and breath.1-3 Low levels of ammonia also attract non-biting insects such as the genetic model organism Drosophila melanogaster and several species of agricultural pests.4,5 Two families of ligand-gated ion channels function as olfactory receptors in insects,6-10 and studies have linked ammonia sensitivity to a particular olfactory receptor in Drosophila.5,11,12 Given the widespread importance of ammonia to insect behavior, it is surprising that the genomes of most insects lack an ortholog of this gene.6 Here, we show that canonical olfactory receptors are not necessary for responses to ammonia in Drosophila. Instead, we demonstrate that a member of the ancient electrogenic ammonium transporter family, Amt, is likely a new type of olfactory receptor. We report two hitherto unidentified olfactory neuron populations that mediate neuronal and behavioral responses to ammonia in Drosophila. Their endogenous ammonia responses are lost in Amt mutant flies, and ectopic expression of either Drosophila or Anopheles Amt confers ammonia sensitivity. These results suggest that Amt is the first transporter known to function as an olfactory receptor in animals and that its function may be conserved across insect species.

Connectomics: Bringing Fly Neural Circuits into Focus.

Three new studies use a whole adult brain electron microscopy volume to reveal new long-range connectivity maps of complete populations of neurons in olfactory, thermosensory, hygrosensory, and memory systems in the fly Drosophila melanogaster.

Estimation of parameters in nonlinear systems using balanced synchronization.

Using synchronization between observations and a model with undetermined parameters is a natural way to complete the specification of the model. The quality of the synchronization, a cost function to be minimized, typically is evaluated by a least squares difference between the data time series and the model time series. If the coupling between the data and the model is too strong, this cost function is small for any data and any model and the variation of the cost function with respect to the parameters of interest is too small to permit selection of an optimal value of the parameters. We introduce two methods for balancing the competing desires of a small cost function for the quality of the synchronization and the numerical ability to determine parameters accurately. One method of "balanced" synchronization adds to the synchronization cost function a requirement that the conditional Lyapunov exponent of the model system, conditioned on being driven by the data remain negative but small in magnitude. The other method allows the coupling between the data and the model to vary in time according to the error in synchronization. This method succeeds because the data and the model exhibit generalized synchronization in the region where the parameters of the model are well determined. Examples are explored which have deterministic chaos with and without noise in the data signal.

The Organization of Projections from Olfactory Glomeruli onto Higher-Order Neurons.

Each odorant receptor corresponds to a unique glomerulus in the brain. Projections from different glomeruli then converge in higher brain regions, but we do not understand the logic governing which glomeruli converge and which do not. Here, we use two-photon optogenetics to map glomerular connections onto neurons in the lateral horn, the region of the Drosophila brain that receives the majority of olfactory projections. We identify 39 morphological types of lateral horn neurons (LHNs) and show that different types receive input from different combinations of glomeruli. We find that different LHN types do not have independent inputs; rather, certain combinations of glomeruli converge onto many of the same LHNs and so are over-represented. Notably, many over-represented combinations are composed of glomeruli that prefer chemically dissimilar ligands whose co-occurrence indicates a behaviorally relevant "odor scene." The pattern of glomerulus-LHN connections thus represents a prediction of what ligand combinations will be most salient.

Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus based on a single neuron's spike train. The first transformation implements averaging, and it improves peak detection accuracy but not speed; the second transformation implements coincidence detection, and it improves speed but not peak accuracy. In each case, the integration time and threshold of the postsynaptic cell are matched to the statistics of convergent spike trains.

Implementation of linear sensory signaling via multiple coordinated mechanisms at central vestibular nerve synapses.

Signal transfer in neural circuits is dynamically modified by the recent history of neuronal activity. Short-term plasticity endows synapses with nonlinear transmission properties, yet synapses in sensory and motor circuits are capable of signaling linearly over a wide range of presynaptic firing rates. How do such synapses achieve rate-invariant transmission despite history-dependent nonlinearities? Here, ultrastructural, biophysical, and computational analyses demonstrate that concerted molecular, anatomical, and physiological refinements are required for central vestibular nerve synapses to linearly transmit rate-coded sensory signals. Vestibular synapses operate in a physiological regime of steady-state depression imposed by tonic firing. Rate-invariant transmission relies on brief presynaptic action potentials that delimit calcium influx, large pools of rapidly mobilized vesicles, multiple low-probability release sites, robust postsynaptic receptor sensitivity, and efficient transmitter clearance. Broadband linear synaptic filtering of head motion signals is thus achieved by coordinately tuned synaptic machinery that maintains physiological operation within inherent cell biological limitations.

Associative learning enhances population coding by inverting interneuronal correlation patterns.

Learning-dependent cortical encoding has been well described in single neurons. But behaviorally relevant sensory signals drive the coordinated activity of millions of cortical neurons; whether learning produces stimulus-specific changes in population codes is unknown. Because the pattern of firing rate correlations between neurons--an emergent property of neural populations--can significantly impact encoding fidelity, we hypothesize that it is a target for learning. Using an associative learning procedure, we manipulated the behavioral relevance of natural acoustic signals and examined the evoked spiking activity in auditory cortical neurons in songbirds. We show that learning produces stimulus-specific changes in the pattern of interneuronal correlations that enhance the ability of neural populations to recognize signals relevant for behavior. This learning-dependent enhancement increases with population size. The results identify the pattern of interneuronal correlation in neural populations as a target of learning that can selectively enhance the representations of specific sensory signals.