Lack of Pattern Separation in Sensory Inputs to the Olfactory Bulb during Perceptual Learning.

Recent studies revealed changes in odor representations in the olfactory bulb during active olfactory learning (Chu et al., 2016; Yamada et al., 2017). Specifically, mitral cell ensemble responses to very similar odorant mixtures sparsened and became more distinguishable as mice learned to discriminate the odorants over days (Chu et al., 2016). In this study, we explored whether changes in the sensory inputs to the bulb underlie the observed changes in mitral cell responses. Using two-photon calcium imaging to monitor the odor responses of the olfactory sensory neuron (OSN) axon terminals in the glomeruli of the olfactory bulb during a discrimination task, we found that OSN inputs to the bulb are stable during discrimination learning. During one week of training to discriminate between very similar odorant mixtures in a Go/No-go task, OSN responses did not show significant sparsening, and the responses to the trained similar odorants did not diverge throughout training. These results suggest that the adaptive changes of mitral cell responses during perceptual learning are ensured by mechanisms downstream of OSN input, possibly in local circuits within olfactory bulb

History-based action selection bias in posterior parietal cortex.

Making decisions based on choice-outcome history is a crucial, adaptive ability in life. However, the neural circuit mechanisms underlying history-dependent decision-making are poorly understood. In particular, history-related signals have been found in many brain areas during various decision-making tasks, but the causal involvement of these signals in guiding behavior is unclear. Here we addressed this issue utilizing behavioral modeling, two-photon calcium imaging, and optogenetic inactivation in mice. We report that a subset of neurons in the posterior parietal cortex (PPC) closely reflect the choice-outcome history and history-dependent decision biases, and PPC inactivation diminishes the history dependency of choice. Specifically, many PPC neurons show history- and bias-tuning during the inter-trial intervals (ITI), and history dependency of choice is affected by PPC inactivation during ITI and not during trial. These results indicate that PPC is a critical region mediating the subjective use of history in biasing action selection

Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning.

Motor skill learning induces long-lasting reorganization of dendritic spines, principal sites of excitatory synapses, in the motor cortex. However, mechanisms that regulate these excitatory synaptic changes remain poorly understood. Here, using in vivo two-photon imaging in awake mice, we found that learning-induced spine reorganization of layer (L) 2/3 excitatory neurons occurs in the distal branches of their apical dendrites in L1 but not in the perisomatic dendrites. This compartment-specific spine reorganization coincided with subtype-specific plasticity of local inhibitory circuits. Somatostatin-expressing inhibitory neurons (SOM-INs), which mainly inhibit distal dendrites of excitatory neurons, showed a decrease in axonal boutons immediately after the training began, whereas parvalbumin-expressing inhibitory neurons (PV-INs), which mainly inhibit perisomatic regions of excitatory neurons, exhibited a gradual increase in axonal boutons during training. Optogenetic enhancement and suppression of SOM-IN activity during training destabilized and hyperstabilized spines, respectively, and both manipulations impaired the learning of stereotyped movements. Our results identify SOM inhibition of distal dendrites as a key regulator of learning-related changes in excitatory synapses and the acquisition of motor skills

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control

Temporal Target Restriction of Olfactory Receptor Neurons by Semaphorin-1a/PlexinA-Mediated Axon-Axon Interactions

SummaryAxon-axon interactions have been implicated in neural circuit assembly, but the underlying mechanisms are poorly understood. Here, we show that in the Drosophila antennal lobe, early-arriving axons of olfactory receptor neurons (ORNs) from the antenna are required for the proper targeting of late-arriving ORN axons from the maxillary palp (MP). Semaphorin-1a is required for targeting of all MP but only half of the antennal ORN classes examined. Sema-1a acts nonautonomously to control ORN axon-axon interactions, in contrast to its cell-autonomous function in olfactory projection neurons. Phenotypic and genetic interaction analyses implicate PlexinA as the Sema-1a receptor in ORN targeting. Sema-1a on antennal ORN axons is required for correct targeting of MP axons within the antennal lobe, while interactions amongst MP axons facilitate their entry into the antennal lobe. We propose that Sema-1a/PlexinA-mediated repulsion provides a mechanism by which early-arriving ORN axons constrain the target choices of late-arriving axons

Lola regulates Drosophila olfactory projection neuron identity and targeting specificity

<p>Abstract</p> <p>Background</p> <p>Precise connections of neural circuits can be specified by genetic programming. In the <it>Drosophila </it>olfactory system, projection neurons (PNs) send dendrites to single glomeruli in the antenna lobe (AL) based upon lineage and birth order and send axons with stereotyped terminations to higher olfactory centers. These decisions are likely specified by a PN-intrinsic transcriptional code that regulates the expression of cell-surface molecules to instruct wiring specificity.</p> <p>Results</p> <p>We find that the loss of <it>longitudinals lacking </it>(<it>lola</it>), which encodes a BTB-Zn-finger transcription factor with 20 predicted splice isoforms, results in wiring defects in both axons and dendrites of all lineages of PNs. RNA <it>in situ </it>hybridization and quantitative RT-PCR suggest that most if not all <it>lola </it>isoforms are expressed in all PNs, but different isoforms are expressed at widely varying levels. Overexpression of individual <it>lola </it>isoforms fails to rescue the <it>lola </it>null phenotypes and causes additional phenotypes. Loss of <it>lola </it>also results in ectopic expression of Gal4 drivers in multiple cell types and in the loss of transcription factor gene <it>lim1 </it>expression in ventral PNs.</p> <p>Conclusion</p> <p>Our results indicate that <it>lola </it>is required for wiring of axons and dendrites of most PN classes, and suggest a need for its molecular diversity. Expression pattern changes of Gal4 drivers in <it>lola</it>^-/-clones imply that <it>lola </it>normally represses the expression of these regulatory elements in a subset of the cells surrounding the AL. We propose that Lola functions as a general transcription factor that regulates the expression of multiple genes ultimately controlling PN identity and wiring specificity.</p

FARCI: Fast and Robust Connectome Interference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling

AlphaTracker: a multi-animal tracking and behavioral analysis tool

Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics