Oscillations and sparsening of odor representations in the mushroom body

Decision-making and action selection in insects: inspiration from vertebrate-based theories

Effective decision-making, one of the most crucial functions of the brain, entails the analysis of sensory information and the selection of appropriate behavior in response to stimuli. Here, we consider the current state of knowledge on the mechanisms of decision-making and action selection in the insect brain, with emphasis on the olfactory processing system. Theoretical and computational models of decision-making emphasize the importance of using inhibitory connections to couple evidence-accumulating pathways; this coupling allows for effective discrimination between competing alternatives and thus enables a decision maker to reach a stable unitary decision. Theory also shows that the coupling of pathways can be implemented using a variety of different mechanisms and vastly improves the performance of decision-making systems. The vertebrate basal ganglia appear to resolve stable action selection by being a point of convergence for multiple excitatory and inhibitory inputs such that only one possible response is selected and all other alternatives are suppressed. Similar principles appear to operate within the insect brain. The insect lateral protocerebrum (LP) serves as a point of convergence for multiple excitatory and inhibitory channels of olfactory information to effect stable decision and action selection, at least for olfactory information. The LP is a rather understudied region of the insect brain, yet this premotor region may be key to effective resolution of action section. We argue that it may be beneficial to use models developed to explore the operation of the vertebrate brain as inspiration when considering action selection in the invertebrate domain. Such an approach may facilitate the proposal of new hypotheses and furthermore frame experimental studies for how decision-making and action selection might be achieved in insects.

It takes two-coincidence coding within the dual olfactory pathway of the honeybee

To rapidly process biologically relevant stimuli, sensory systems have developed a broad variety of coding mechanisms like parallel processing and coincidence detection. Parallel processing (e.g., in the visual system), increases both computational capacity and processing speed by simultaneously coding different aspects of the same stimulus. Coincidence detection is an efficient way to integrate information from different sources. Coincidence has been shown to promote associative learning and memory or stimulus feature detection (e.g., in auditory delay lines). Within the dual olfactory pathway of the honeybee both of these mechanisms might be implemented by uniglomerular projection neurons (PNs) that transfer information from the primary olfactory centers, the antennal lobe (AL), to a multimodal integration center, the mushroom body (MB). PNs from anatomically distinct tracts respond to the same stimulus space, but have different physiological properties, characteristics that are prerequisites for parallel processing of different stimulus aspects. However, the PN pathways also display mirror-imaged like anatomical trajectories that resemble neuronal coincidence detectors as known from auditory delay lines. To investigate temporal processing of olfactory information, we recorded PN odor responses simultaneously from both tracts and measured coincident activity of PNs within and between tracts. Our results show that coincidence levels are different within each of the two tracts. Coincidence also occurs between tracts, but to a minor extent compared to coincidence within tracts. Taken together our findings support the relevance of spike timing in coding of olfactory information (temporal code).

Unexpected plant odor responses in a moth pheromone system

Male moths rely on olfactory cues to find females for reproduction. Males also use volatile plant compounds (VPCs) to find food sources and might use host-plant odor cues to identify the habitat of calling females. Both the sex pheromone released by conspecific females and VPCs trigger well-described oriented flight behavior toward the odor source. Whereas detection and central processing of pheromones and VPCs have been thought for a long time to be highly separated from each other, recent studies have shown that interactions of both types of odors occur already early at the periphery of the olfactory pathway. Here we show that detection and early processing of VPCs and pheromone can overlap between the two sub-systems. Using complementary approaches, i.e., single-sensillum recording of olfactory receptor neurons, in vivo calcium imaging in the antennal lobe, intracellular recordings of neurons in the macroglomerular complex (MGC) and flight tracking in a wind tunnel, we show that some plant odorants alone, such as heptanal, activate the pheromone-specific pathway in male Agrotis ipsilon at peripheral and central levels. To our knowledge, this is the first report of a plant odorant with no chemical similarity to the molecular structure of the pheromone, acting as a partial agonist of a moth sex pheromone.

Differential combinatorial coding of pheromones in two olfactory subsystems of the honey bee brain

Neural coding of pheromones has been intensively studied in insects with a particular focus on sex pheromones. These studies favored the view that pheromone compounds are processed within specific antennal lobe glomeruli following a specialized labeled-line system. However, pheromones play crucial roles in an insect's life beyond sexual attraction, and some species use many different pheromones making such a labeled-line organization unrealistic. A combinatorial coding scheme, in which each component activates a set of broadly tuned units, appears more adapted in this case. However, this idea has not been tested thoroughly. We focused here on the honey bee Apis mellifera, a social insect that relies on a wide range of pheromones to ensure colony cohesion. Interestingly, the honey bee olfactory system harbors two central parallel pathways, whose functions remain largely unknown. Using optophysiological recordings of projection neurons, we compared the responses of these two pathways to 27 known honey bee pheromonal compounds emitted by the brood, the workers, and the queen. We show that while queen mandibular pheromone is processed by l-ALT (lateral antennal lobe tract) neurons and brood pheromone is mainly processed by m-ALT (median antennal lobe tract) neurons, worker pheromones induce redundant activity in both pathways. Moreover, all tested pheromonal compounds induce combinatorial activity from several AL glomeruli. These findings support the combinatorial coding scheme and suggest that higher-order brain centers reading out these combinatorial activity patterns may eventually classify olfactory signals according to their biological meaning.

A model for non-monotonic intensity coding

Peripheral neurons of most sensory systems increase their response with increasing stimulus intensity. Behavioural responses, however, can be specific to some intermediate intensity level whose particular value might be innate or associatively learned. Learning such a preference requires an adjustable trans- formation from a monotonic stimulus representation at the sensory periphery to a non-monotonic representation for the motor command. How do neural systems accomplish this task? We tackle this general question focusing on odour-intensity learning in the fruit fly, whose first- and second-order olfactory neurons show monotonic stimulus-response curves. Nevertheless, flies form associative memories specific to particular trained odour intensities. Thus, downstream of the first two olfactory processing layers, odour intensity must be re-coded to enable intensity-specific associative learning. We present a minimal, feed-forward, three-layer circuit, which implements the required transformation by combining excitation, inhibition, and, as a decisive third element, homeostatic plasticity. Key features of this circuit motif are consistent with the known architecture and physiology of the fly olfactory system, whereas alternative mechanisms are either not composed of simple, scalable building blocks or not compatible with physiological observations. The simplicity of the circuit and the robustness of its function under parameter changes make this computational motif an attractive candidate for tuneable non-monotonic intensity coding.

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data

BACKGROUND

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data.

NEW METHOD

In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources.

RESULTS

Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection.

COMPARISON WITH EXISTING METHODS

Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding.

CONCLUSIONS

LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection.

ϒ spike-field coherence in a population of olfactory bulb neurons differentiates between odors irrespective of associated outcome

Studies in different sensory systems indicate that short spike patterns within a spike train that carry items of sensory information can be extracted from the overall train by using field potential oscillations as a reference (Kayser et al., 2012; Panzeri et al., 2014). Here we test the hypothesis that the local field potential (LFP) provides the temporal reference frame needed to differentiate between odors regardless of associated outcome. Experiments were performed in the olfactory system of the mouse (Mus musculus) where the mitral/tufted (M/T) cell spike rate develops differential responses to rewarded and unrewarded odors as the animal learns to associate one of the odors with a reward in a go-no go behavioral task. We found that coherence of spiking in M/T cells with the ϒ LFP (65 to 95 Hz) differentiates between odors regardless of the associated behavioral outcome of odor presentation.

Trade-off between information format and capacity in the olfactory system

As information about the sensory environment passes between layers within the nervous system, the format of the information often changes. To examine how information format affects the capacity of neurons to represent stimuli, we measured the rate of information transmission in olfactory neurons in intact, awake locusts (Schistocerca americana) while pharmacologically manipulating patterns of correlated neuronal activity. Blocking the periodic inhibition underlying odor-elicited neural oscillatory synchronization increased information transmission rates. This suggests oscillatory synchrony, which serves other information processing roles, comes at a cost to the speed with which neurons can transmit information. Our results provide an example of a trade-off between benefits and costs in neural information processing.

Synapse-specific compartmentalization of signaling cascades for LTP induction in CA3 interneurons

Inhibitory interneurons with somata in strata radiatum and lacunosum-molecular (SR/L-M) of hippocampal area CA3 receive excitatory input from pyramidal cells via the recurrent collaterals (RCs), and the dentate gyrus granule cells via the mossy fibers (MFs). Here we demonstrate that Hebbian long-term potentiation (LTP) at RC synapses on SR/L-M interneurons requires the concomitant activation of calcium-impermeable AMPARs (CI-AMPARs) and N-methyl-d-aspartate receptors (NMDARs). RC LTP was prevented by voltage clamping the postsynaptic cell during high-frequency stimulation (HFS; 3 trains of 100 pulses delivered at 100 Hz every 10s), with intracellular injections of the Ca(2+) chelator BAPTA (20mM), and with the NMDAR antagonist D-AP5. In separate experiments, RC and MF inputs converging onto the same interneuron were sequentially activated. We found that RC LTP induction was blocked by inhibitors of the calcium/calmodulin-dependent protein kinase II (CaMKII; KN-62, 10 μM or KN-93, 10 μM) but MF LTP was CaMKII independent. Conversely, the application of the protein kinase A (PKA) activators forskolin/IBMX (50 μM/25 μM) potentiated MF EPSPs but not RC EPSPs. Together these data indicate that the aspiny dendrites of SR/L-M interneurons compartmentalize synapse-specific Ca(2+) signaling required for LTP induction at RC and MF synapses. We also show that the two signal transduction cascades converge to activate a common effector, protein kinase C (PKC). Specifically, LTP at RC and MF synapses on the same SR/LM interneuron was blocked by postsynaptic injections of chelerythrine (10 μM). These data indicate that both forms of LTP share a common mechanism involving PKC-dependent signaling modulation.

Hierarchical sparse coding in the sensory system of Caenorhabditis elegans

Animals with compact sensory systems face an encoding problem where a small number of sensory neurons are required to encode information about its surrounding complex environment. Using Caenorhabditis elegans worms as a model, we ask how chemical stimuli are encoded by a small and highly connected sensory system. We first generated a comprehensive library of transgenic worms where each animal expresses a genetically encoded calcium indicator in individual sensory neurons. This library includes the vast majority of the sensory system in C. elegans. Imaging from individual sensory neurons while subjecting the worms to various stimuli allowed us to compile a comprehensive functional map of the sensory system at single neuron resolution. The functional map reveals that despite the dense wiring, chemosensory neurons represent the environment using sparse codes. Moreover, although anatomically closely connected, chemo- and mechano-sensory neurons are functionally segregated. In addition, the code is hierarchical, where few neurons participate in encoding multiple cues, whereas other sensory neurons are stimulus specific. This encoding strategy may have evolved to mitigate the constraints of a compact sensory system.

Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by ∼2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection.

The neuronal architecture of the mushroom body provides a logic for associative learning

We identified the neurons comprising the Drosophila mushroom body (MB), an associative center in invertebrate brains, and provide a comprehensive map describing their potential connections. Each of the 21 MB output neuron (MBON) types elaborates segregated dendritic arbors along the parallel axons of ∼2000 Kenyon cells, forming 15 compartments that collectively tile the MB lobes. MBON axons project to five discrete neuropils outside of the MB and three MBON types form a feedforward network in the lobes. Each of the 20 dopaminergic neuron (DAN) types projects axons to one, or at most two, of the MBON compartments. Convergence of DAN axons on compartmentalized Kenyon cell-MBON synapses creates a highly ordered unit that can support learning to impose valence on sensory representations. The elucidation of the complement of neurons of the MB provides a comprehensive anatomical substrate from which one can infer a functional logic of associative olfactory learning and memory.

Multimodal sensory reliance in the nocturnal homing of the amblypygid Phrynus pseudoparvulus (Class Arachnida, Order Amblypygi)?

Like many other nocturnal arthropods, the amblypygid Phrynus pseudoparvulus is capable of homing. The environment through which these predators navigate is a dense and heterogeneous tropical forest understory and the mechanism(s) underlying their putatively complex navigational abilities are presently unknown. This study explores the sensory inputs that might facilitate nocturnal navigation in the amblypygid P. pseudoparvulus. Specifically, we use sensory system manipulations in conjunction with field displacements to examine the potential involvement of multimodal - olfactory and visual - stimuli in P. pseudoparvulus' homing behavior. In a first experiment, we deprived individuals of their olfactory capacity and displaced them to the opposite side of their home trees (<5m). We found that olfaction-intact individuals were more likely to be re-sighted in their home refuges than olfaction-deprived individuals. In a second experiment, we independently manipulated both olfactory and visual sensory capacities in conjunction with longer-distance displacements (8m) from home trees. We found that sensory-intact individuals tended to be re-sighted on their home tree more often than sensory-deprived individuals, with a stronger effect of olfactory deprivation than visual deprivation. Comparing across sensory modality manipulations, olfaction-manipulated individuals took longer to return to their home trees than vision-manipulated individuals. Together, our results indicate that olfaction is important in the nocturnal navigation of P. pseudoparvulus and suggest that vision may also play a more minor role.

Computational models to understand decision making and pattern recognition in the insect brain

Odor stimuli reaching olfactory systems of mammals and insects are characterized by remarkable non-stationary and noisy time series. Their brains have evolved to discriminate subtle changes in odor mixtures and find meaningful variations in complex spatio-temporal patterns. Insects with small brains can effectively solve two computational tasks: identify the presence of an odor type and estimate the concentration levels of the odor. Understanding the learning and decision making processes in the insect brain can not only help us to uncover general principles of information processing in the brain, but it can also provide key insights to artificial chemical sensing. Both olfactory learning and memory are dominantly organized in the Antennal Lobe (AL) and the Mushroom Bodies (MBs). Current computational models yet fail to deliver an integrated picture of the joint computational roles of the AL and MBs. This review intends to provide an integrative overview of the computational literature analyzed in the context of the problem of classification (odor discrimination) and regression (odor concentration estimation), particularly identifying key computational ingredients necessary to solve pattern recognition.

Central processing in the mushroom bodies

The mushroom bodies in the insect brain serve as a central information processing area. Here, focusing mainly on olfaction, we discuss functionally related roles the mushroom bodies play in signal gain control, response sparsening, the separation of similar signals (decorrelation), and learning and memory. In sum, the mushroom bodies assemble and format a context-appropriate representation of the insect's world.

Precise detection of direct glomerular input duration by the olfactory bulb

Sensory neuron input to the olfactory bulb (OB) was activated precisely for different durations with blue light in mice expressing channelrhodopsin-2 in olfactory sensory neurons. Behaviorally the mice discriminated differences of 10 ms in duration of direct glomerular activation. In addition, a subset of mitral/tufted cells in the OB of awake mice responded tonically therefore conveying information on stimulus duration. Our study provides evidence that duration of the input to glomeruli not synchronized to sniffing is detected. This potent cue may be used to obtain information on puffs in odor plumes.

Looking for the roots of cortical sensory computation in three-layered cortices

Despite considerable effort over a century and the benefit of remarkable technical advances in the past few decades, we are still far from understanding mammalian cerebral neocortex. With its six layers, modular architecture, canonical circuits, innumerable cell types, and computational complexity, isocortex remains a challenging mystery. In this review, we argue that identifying the structural and functional similarities between mammalian piriform cortex and reptilian dorsal cortex could help reveal common organizational and computational principles and by extension, some of the most primordial computations carried out in cortical networks.

A temporal channel for information in sparse sensory coding

BACKGROUND

Sparse codes are found in nearly every sensory system, but the role of spike timing in sparse sensory coding is unclear. Here, we use the olfactory system of awake locusts to test whether the timing of spikes in Kenyon cells, a population of neurons that responds sparsely to odors, carries sensory information to and influences the responses of follower neurons.

RESULTS

We characterized two major classes of direct followers of Kenyon cells. With paired intracellular and field potential recordings made during odor presentations, we found that these followers contain information about odor identity in the temporal patterns of their spikes rather than in the spike rate, the spike phase, or the identities of the responsive neurons. Subtly manipulating the relative timing of Kenyon cell spikes with temporally and spatially structured microstimulation reliably altered the response patterns of the followers.

CONCLUSIONS

Our results show that even remarkably sparse spiking responses can provide information through stimulus-specific variations in timing on the order of tens to hundreds of milliseconds and that these variations can determine the responses of downstream neurons. These results establish the importance of spike timing in a sparse sensory code.

Ionic channel mechanisms mediating the intrinsic excitability of Kenyon cells in the mushroom body of the cricket brain

Intrinsic neurons within the mushroom body of the insect brain, called Kenyon cells, play an important role in olfactory associative learning. In this study, we examined the ionic mechanisms mediating the intrinsic excitability of Kenyon cells in the cricket Gryllus bimaculatus. A perforated whole-cell clamp study using β-escin indicated the existence of several inward and outward currents. Three types of inward currents (INaf, INaP, and ICa) were identified. The transient sodium current (INaf) activated at -40 mV, peaked at -26 mV, and half-inactivated at -46.7 mV. The persistent sodium current (INaP) activated at -51 mV, peaked at -23 mV, and half-inactivated at -30.7 mV. Tetrodotoxin (TTX; 1 μM) completely blocked both INaf and INaP, but 10nM TTX blocked INaf more potently than INaP. Cd(2+) (50 μM) potently blocked INaP with little effect on INaf. Riluzole (>20 μM) nonselectively blocked both INaP and INaf. The voltage-dependent calcium current (ICa) activated at -30 mV, peaked at -11.3 mV, and half-inactivated at -34 mV. The Ca(2+) channel blocker verapamil (100 μM) blocked ICa in a use-dependent manner. Cell-attached patch-clamp recordings showed the presence of a large-conductance Ca(2+)-activated K(+) (BK) channel, and the activity of this channel was decreased by removing the extracellular Ca(2+) or adding verapamil or nifedipine, and increased by adding the Ca(2+) agonist Bay K8644, indicating that Ca(2+) entry via the L-type Ca(2+) channel regulates BK channel activity. Under the current-clamp condition, membrane depolarization generated membrane oscillations in the presence of 10nM TTX or 100 μM riluzole in the bath solution. These membrane oscillations disappeared with 1 μM TTX, 50 μM Cd(2+), replacement of external Na(+) with choline, and blockage of Na(+)-activated K(+) current (IKNa) with 50 μM quinidine, indicating that membrane oscillations are primarily mediated by INaP in cooperation with IKNa. The plateau potentials observed either in Ca(2+)-free medium or in the presence of verapamil were eliminated by blocking INaP with 50 μM Cd(2+). Taken together, these results indicate that INaP and IKNa participate in the generation of membrane oscillations and that INaP additionally participates in the generation of plateau potentials and initiation of spontaneous action potentials. ICa, through L-type Ca(2+) channels, was also found to play a role in the rapid membrane repolarization of action potentials by functional coupling with BK channels.

Honeybee Kenyon cells are regulated by a tonic GABA receptor conductance

The higher cognitive functions of insects are dependent on their mushroom bodies (MBs), which are particularly large in social insects such as honeybees. MB Kenyon cells (KCs) receive multisensory input and are involved in associative learning and memory. In addition to receiving sensory input via excitatory nicotinic synapses, KCs receive inhibitory GABAergic input from MB feedback neurons. Cultured honeybee KCs exhibit ionotropic GABA receptor currents, but the properties of GABA-mediated inhibition in intact MBs are currently unknown. Here, using whole cell recordings from KCs in acutely isolated honeybee brain, we show that KCs exhibit a tonic current that is inhibited by picrotoxin but not by bicuculline. Bath application of GABA (5 μM) and taurine (1 mM) activate a tonic current in KCs, but l-glutamate (0.1-0.5 mM) has no effect. The tonic current is strongly potentiated by the allosteric GABAA receptor modulator pentobarbital and is reduced by inhibition of Ca(2+) channels with Cd(2+) or nifedipine. Noise analysis of the GABA-evoked current gives a single-channel conductance value for the underlying receptors of 27 ± 3 pS, similar to that of resistant to dieldrin (RDL) receptors. The amount of injected current required to evoke action potential firing in KCs is significantly lower in the presence of picrotoxin. KCs recorded in an intact honeybee head preparation similarly exhibit a tonic GABA receptor conductance that reduces neuronal excitability, a property that is likely to contribute to the sparse coding of sensory information in insect MBs.

Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks?

Olfactory processing in behaving animals, even at early stages, is inextricable from top down influences associated with odor perception. The anatomy of the olfactory network (olfactory bulb, piriform, and entorhinal cortices) and its unique direct access to the limbic system makes it particularly attractive to study how sensory processing could be modulated by learning and memory. Moreover, olfactory structures have been early reported to exhibit oscillatory population activities easy to capture through local field potential recordings. An attractive hypothesis is that neuronal oscillations would serve to “bind” distant structures to reach a unified and coherent perception. In relation to this hypothesis, we will assess the functional relevance of different types of oscillatory activity observed in the olfactory system of behaving animals. This review will focus primarily on two types of oscillatory activities: beta (15–40 Hz) and gamma (60–100 Hz). While gamma oscillations are dominant in the olfactory system in the absence of odorant, both beta and gamma rhythms have been reported to be modulated depending on the nature of the olfactory task. Studies from the authors of the present review and other groups brought evidence for a link between these oscillations and behavioral changes induced by olfactory learning. However, differences in studies led to divergent interpretations concerning the respective role of these oscillations in olfactory processing. Based on a critical reexamination of those data, we propose hypotheses on the functional involvement of beta and gamma oscillations for odor perception and memory.

Neuronal circuits and computations: pattern decorrelation in the olfactory bulb

Neuronal circuits in the olfactory bulb transform odor-evoked activity patterns across the input channels, the olfactory glomeruli, into distributed activity patterns across the output neurons, the mitral cells. One computation associated with this transformation is a decorrelation of activity patterns representing similar odors. Such a decorrelation has various benefits for the classification and storage of information by associative networks in higher brain areas. Experimental results from adult zebrafish show that pattern decorrelation involves a redistribution of activity across the population of mitral cells. These observations imply that pattern decorrelation cannot be explained by a global scaling mechanism but that it depends on interactions between distinct subsets of neurons in the network. This article reviews insights into the network mechanism underlying pattern decorrelation and discusses recent results that link pattern decorrelation in the olfactory bulb to odor discrimination behavior.

Coding and transformations in the olfactory system

How is sensory information represented in the brain? A long-standing debate in neural coding is whether and how timing of spikes conveys information to downstream neurons. Although we know that neurons in the olfactory bulb (OB) exhibit rich temporal dynamics, the functional relevance of temporal coding remains hotly debated. Recent recording experiments in awake behaving animals have elucidated highly organized temporal structures of activity in the OB. In addition, the analysis of neural circuits in the piriform cortex (PC) demonstrated the importance of not only OB afferent inputs but also intrinsic PC neural circuits in shaping odor responses. Furthermore, new experiments involving stimulation of the OB with specific temporal patterns allowed for testing the relevance of temporal codes. Together, these studies suggest that the relative timing of neuronal activity in the OB conveys odor information and that neural circuits in the PC possess various mechanisms to decode temporal patterns of OB input.

Induction of aversive learning through thermogenetic activation of Kenyon cell ensembles in Drosophila

Drosophila represents a model organism to analyze neuronal mechanisms underlying learning and memory. Kenyon cells of the Drosophila mushroom body are required for associative odor learning and memory retrieval. But is the mushroom body sufficient to acquire and retrieve an associative memory? To answer this question we have conceived an experimental approach to bypass olfactory sensory input and to thermogenetically activate sparse and random ensembles of Kenyon cells directly. We found that if the artifical activation of Kenyon cell ensembles coincides with a salient, aversive stimulus learning was induced. The animals adjusted their behavior in a subsequent test situation and actively avoided reactivation of these Kenyon cells. Our results show that Kenyon cell activity in coincidence with a salient aversive stimulus can suffice to form an associative memory. Memory retrieval is characterized by a closed feedback loop between a behavioral action and the reactivation of sparse ensembles of Kenyon cells.

Peak shift in honey bee olfactory learning

If animals are trained with two similar stimuli such that one is rewarding (S+) and one punishing (S-), then following training animals show a greatest preference not for the S+, but for a novel stimulus that is slightly more different from the S- than the S+ is. This peak shift phenomenon has been widely reported for vertebrates and has recently been demonstrated for bumblebees and honey bees. To explore the nature of peak shift in invertebrates further, here we examined the properties of peak shift in honey bees trained in a free-flight olfactory learning assay. Hexanal and heptanol were mixed in different ratios to create a continuum of odour stimuli. Bees were trained to artificial flowers such that one odour mixture was rewarded with 2 molar sucrose (S+), and one punished with distasteful quinine (S-). After training, bees were given a non-rewarded preference test with five different mixtures of hexanal and heptanol. Following training bees' maximal preference was for an odour mixture slightly more distinct from the S- than the trained S+. This effect was not seen if bees were initially trained with two distinct odours, replicating the classic features of peak shift reported for vertebrates. We propose a conceptual model of how peak shift might occur in honey bees. We argue that peak shift does not require any higher level of processing than the known olfactory learning circuitry of the bee brain and suggest that peak shift is a very general feature of discrimination learning.

A single GABAergic neuron mediates feedback of odor-evoked signals in the mushroom body of larval Drosophila

Inhibition has a central role in defining the selectivity of the responses of higher order neurons to sensory stimuli. However, the circuit mechanisms of regulation of these responses by inhibitory neurons are still unclear. In Drosophila, the mushroom bodies (MBs) are necessary for olfactory memory, and by implication for the selectivity of learned responses to specific odors. To understand the circuitry of inhibition in the calyx (the input dendritic region) of the MBs, and its relationship with MB excitatory activity, we used the simple anatomy of the Drosophila larval olfactory system to identify any inhibitory inputs that could contribute to the selectivity of MB odor responses. We found that a single neuron accounts for all detectable GABA innervation in the calyx of the MBs, and that this neuron has pre-synaptic terminals in the calyx and post-synaptic branches in the MB lobes (output axonal area). We call this neuron the larval anterior paired lateral (APL) neuron, because of its similarity to the previously described adult APL neuron. Reconstitution of GFP partners (GRASP) suggests that the larval APL makes extensive contacts with the MB intrinsic neurons, Kenyon Cells (KCs), but few contacts with incoming projection neurons (PNs). Using calcium imaging of neuronal activity in live larvae, we show that the larval APL responds to odors, in a manner that requires output from KCs. Our data suggest that the larval APL is the sole GABAergic neuron that innervates the MB input region and carries inhibitory feedback from the MB output region, consistent with a role in modulating the olfactory selectivity of MB neurons.

Selective attention in the honeybee optic lobes precedes behavioral choices

Attention allows animals to respond selectively to competing stimuli, enabling some stimuli to evoke a behavioral response while others are ignored. How the brain does this remains mysterious, although it is increasingly evident that even animals with the smallest brains display this capacity. For example, insects respond selectively to salient visual stimuli, but it is unknown where such selectivity occurs in the insect brain, or whether neural correlates of attention might predict the visual choices made by an insect. Here, we investigate neural correlates of visual attention in behaving honeybees (Apis mellifera). Using a closed-loop paradigm that allows tethered, walking bees to actively control visual objects in a virtual reality arena, we show that behavioral fixation increases neuronal responses to flickering, frequency-tagged stimuli. Attention-like effects were reduced in the optic lobes during replay of the same visual sequences, when bees were not able to control the visual displays. When bees were presented with competing frequency-tagged visual stimuli, selectivity in the medulla (an optic ganglion) preceded behavioral selection of a stimulus, suggesting that modulation of early visual processing centers precedes eventual behavioral choices made by these insects.

Selectivity and sparseness in randomly connected balanced networks

Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the “paradoxical” effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination

Sparse coding may be a general strategy of neural systems to augment memory capacity. In Drosophila, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit show that Kenyon cells activate APL and APL inhibits Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreases the sparseness of Kenyon cell odor responses, increases inter-odor correlations, and prevents flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor-specificity of memories.

Olfactory coding in the honeybee lateral horn

Olfactory systems dynamically encode odor information in the nervous system. Insects constitute a well-established model for the study of the neural processes underlying olfactory perception. In insects, odors are detected by sensory neurons located in the antennae, whose axons project to a primary processing center, the antennal lobe. There, the olfactory message is reshaped and further conveyed to higher-order centers, the mushroom bodies and the lateral horn. Previous work has intensively analyzed the principles of olfactory processing in the antennal lobe and in the mushroom bodies. However, how the lateral horn participates in olfactory coding remains comparatively more enigmatic. We studied odor representation at the input to the lateral horn of the honeybee, a social insect that relies on both floral odors for foraging and pheromones for social communication. Using in vivo calcium imaging, we show consistent neural activity in the honeybee lateral horn upon stimulation with both floral volatiles and social pheromones. Recordings reveal odor-specific maps in this brain region as stimulations with the same odorant elicit more similar spatial activity patterns than stimulations with different odorants. Odor-similarity relationships are mostly conserved between antennal lobe and lateral horn, so that odor maps recorded in the lateral horn allow predicting bees' behavioral responses to floral odorants. In addition, a clear segregation of odorants based on pheromone type is found in both structures. The lateral horn thus contains an odor-specific map with distinct representations for the different bee pheromones, a prerequisite for eliciting specific behaviors.

Stereotyped connectivity and computations in higher-order olfactory neurons

In the first brain relay of the olfactory system, odors are encoded by combinations of glomeruli, but it is not known how glomerular signals are ultimately integrated. In Drosophila melanogaster, the majority of glomerular projections target the lateral horn. Here we show that lateral horn neurons (LHNs) receive input from sparse and stereotyped combinations of glomeruli that are coactivated by odors, and certain combinations of glomeruli are over-represented. One morphological LHN type is broadly tuned and sums input from multiple glomeruli. These neurons have a broader dynamic range than their individual glomerular inputs do. By contrast, a second morphological type is narrowly tuned and receives prominent odor-selective inhibition through both direct and indirect pathways. We show that this wiring scheme confers increased selectivity. The biased stereotyped connectivity of the lateral horn contrasts with the probabilistic wiring of the mushroom body, reflecting the distinct roles of these regions in innate as compared to learned behaviors.

An information theoretic model of information processing in the Drosophila olfactory system: the role of inhibitory neurons for system efficiency

Fruit flies (Drosophila melanogaster) rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the fly's olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons (Kenyon cells) was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the system efficiency will be substantially reduced.

Neural circuits: random design of a higher-order olfactory projection

A recent study in Drosophila has found that the connectivity between the first olfactory processing center, the antennal lobe, and one of its targets, the mushroom body, is apparently random. This supports the idea that the mushroom body is designed for learning arbitrary odor features.

Multi-unit recording with iridium oxide modified stereotrodes in Drosophila melanogaster

BACKGROUND

Drosophila is a very favorable animal model for the studies of neuroscience. However, it remains a great challenge to employ electrophysiological approaches in Drosophila to study the neuronal assembly dynamics in vivo, partially due to the small size of the Drosophila brain. Small and sensitive microelectrodes for multi-unit recordings are greatly desired.

NEW METHOD

We fabricated micro-scale stereotrodes for electrical recordings in Drosophila melanogaster. The stereotrodes were modified with iridium oxide (IrO2) under a highly controllable deposition procedure to improve their electrochemical properties. Electrical recordings were carried out using the IrO2 stereotrodes to detect spontaneous action potentials and LFPs in vivo.

RESULTS

The IrO2 electrodes exhibited significantly higher capacitance and lower impedance at 1 kHz. Electrical recording with the IrO2 stereotrodes in vivo demonstrated an average signal-to-noise ratio (SNR) of 7.3 and a significantly improved LFP sensitivity. 5 types of different neurons recorded were clearly separated. Electrophysiological responses to visual and odor stimulation were also detected, respectively.

COMPARISON WITH EXISTING METHOD(S)

The most widely used electrodes for electrical recording in Drosophila are glass microelectrode and sharpened tungsten microelectrode, which are typically used for single-unit recordings. Although tetrode technology has been used to record multi-neuronal activities from Drosophila, the fabricated IrO2 stereotrodes possess smaller geometry size but exhibited comparable recording signal-to noise ration and better sorting quality.

CONCLUSIONS

The IrO2 stereotrodes are capable to meet the requirements of multi-unit recording and spike sorting, which will be a useful tool for the electrophysiology-based researches especially in Drosophila and other small animals.

Robust active binocular vision through intrinsically motivated learning

The efficient coding hypothesis posits that sensory systems of animals strive to encode sensory signals efficiently by taking into account the redundancies in them. This principle has been very successful in explaining response properties of visual sensory neurons as adaptations to the statistics of natural images. Recently, we have begun to extend the efficient coding hypothesis to active perception through a form of intrinsically motivated learning: a sensory model learns an efficient code for the sensory signals while a reinforcement learner generates movements of the sense organs to improve the encoding of the signals. To this end, it receives an intrinsically generated reinforcement signal indicating how well the sensory model encodes the data. This approach has been tested in the context of binocular vison, leading to the autonomous development of disparity tuning and vergence control. Here we systematically investigate the robustness of the new approach in the context of a binocular vision system implemented on a robot. Robustness is an important aspect that reflects the ability of the system to deal with unmodeled disturbances or events, such as insults to the system that displace the stereo cameras. To demonstrate the robustness of our method and its ability to self-calibrate, we introduce various perturbations and test if and how the system recovers from them. We find that (1) the system can fully recover from a perturbation that can be compensated through the system's motor degrees of freedom, (2) performance degrades gracefully if the system cannot use its motor degrees of freedom to compensate for the perturbation, and (3) recovery from a perturbation is improved if both the sensory encoding and the behavior policy can adapt to the perturbation. Overall, this work demonstrates that our intrinsically motivated learning approach for efficient coding in active perception gives rise to a self-calibrating perceptual system of high robustness.

Imitation learning based on an intrinsic motivation mechanism for efficient coding

A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML) for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation.

Encoding of mixtures in a simple olfactory system

Natural odors are usually mixtures; yet, humans and animals can experience them as unitary percepts. Olfaction also enables stimulus categorization and generalization. We studied how these computations are performed with the responses of 168 locust antennal lobe projection neurons (PNs) to varying mixtures of two monomolecular odors, and of 174 PNs and 209 mushroom body Kenyon cells (KCs) to mixtures of up to eight monomolecular odors. Single-PN responses showed strong hypoadditivity and population trajectories clustered by odor concentration and mixture similarity. KC responses were much sparser on average than those of PNs and often signaled the presence of single components in mixtures. Linear classifiers could read out the responses of both populations in single time bins to perform odor identification, categorization, and generalization. Our results suggest that odor representations in the mushroom body may result from competing optimization constraints to facilitate memorization (sparseness) while enabling identification, classification, and generalization.

Natural image sequences constrain dynamic receptive fields and imply a sparse code

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input.

A spatiotemporal coding mechanism for background-invariant odor recognition

Sensory stimuli evoke neural activity that evolves over time. What features of these spatiotemporal responses allow the robust encoding of stimulus identity in a multistimulus environment? Here we examined this issue in the locust (Schistocerca americana) olfactory system. We found that sensory responses evoked by an odorant (foreground) varied when presented atop or after an ongoing stimulus (background). These inconsistent sensory inputs triggered dynamic reorganization of ensemble activity in the downstream antennal lobe. As a result, partial pattern matches between neural representations encoding the same foreground stimulus across conditions were achieved. The degree and segments of response overlaps varied; however, any overlap observed was sufficient to drive background-independent responses in the downstream neural population. Notably, recognition performance of locusts in behavioral assays correlated well with our physiological findings. Hence, our results reveal how background-independent recognition of odors can be achieved using spatiotemporal patterns of neural activity.

Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila

Sensory systems face the challenge to represent sensory inputs in a way to allow easy readout of sensory information by higher brain areas. In the olfactory system of the fly drosopohila melanogaster, projection neurons (PNs) of the antennal lobe (AL) convert a dense activation of glomeruli into a sparse, high-dimensional firing pattern of Kenyon cells (KCs) in the mushroom body (MB). Here we investigate the design principles of the olfactory system of drosophila in regard to the capabilities to discriminate odor quality from the MB representation and its robustness to different types of noise. We focus on understanding the role of highly correlated homotypic projection neurons (“sister cells”) found in the glomeruli of flies. These cells are coupled by gap-junctions and receive almost identical sensory inputs, but target randomly different KCs in MB. We show that sister cells might play a crucial role in increasing the robustness of the MB odor representation to noise. Computationally, sister cells thus might help the system to improve the generalization capabilities in face of noise without impairing the discriminability of odor quality at the same time.

Evolution of the techniques used in studying associative olfactory learning and memory in adult Drosophila in vivo: a historical and technical perspective

Drosophila melanogaster behavioral mutants have been isolated in which the ability to form associative olfactory memories has been disrupted primarily by altering cyclic adenosine monophosphate signal transduction. Unfortunately, the small size of the fruit fly and its neurons has made the application of neurobiological techniques typically used to investigate the physiology underlying these behaviors daunting. However, the realization that adult fruit flies could tolerate a window in the head capsule allowing access to the central structures thought to be involved plus the development of genetically expressed reporters of neuronal function has allowed a meteoric expansion of this field over the last decade. This review attempts to summarize the evolution of the techniques involved from the first use of a window to access these brain areas thought to be involved in associative olfactory learning and memory, the mushroom bodies and antennal lobes, to the current refinements which allow both high-resolution multiphoton imaging and patch clamping of identified neurons while applying the stimuli used in the behavioral protocols. This area of research now appears poised to reveal some very exciting mechanisms underlying behavior.

Integration of the olfactory code across dendritic claws of single mushroom body neurons

In the olfactory system, sensory inputs are arranged in different glomerular channels, which respond in combinatorial ensembles to the various chemical features of an odor. Here we investigate where and how this combinatorial code is read out deeper in the brain. We exploit the unique morphology of neurons in the mushroom body (MB), which receive input on large dendritic claws. Imaging odor responses of these dendritic claws shows that input channels with distinct odor tuning converge on individual MB neurons. We determined how these inputs interact to drive the cell to spike threshold using intracellular recordings to examine MB responses to optogenetically controlled input. Our results provide an elegant explanation for the characteristic selectivity of MB neurons: these cells receive different types of input, and require those inputs to be coactive in order to spike. These results establish the MB as an important site of integration in the fly olfactory system.

Cellular adaptation facilitates sparse and reliable coding in sensory pathways

Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.