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.

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. Low levels of ammonia also attract non-biting insects such as the genetic model organism Drosophila melanogaster and several species of agricultural pests. Two families of ligand-gated ion channels function as olfactory receptors in insects, and studies have linked ammonia sensitivity to a particular olfactory receptor in Drosophila.

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.

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.

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.

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.

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.

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.

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.

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.

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.