scholarly journals Circuits for integrating learned and innate valences in the insect brain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Claire Eschbach ◽  
Akira Fushiki ◽  
Michael Winding ◽  
Bruno Afonso ◽  
Ingrid V Andrade ◽  
...  
Keyword(s):  
Animal Behavior ◽  
Mushroom Body ◽  
Excitatory Input ◽  
Insect Brain ◽  
Inhibitory Input ◽  
Lateral Horn ◽  
Negative Valence ◽  
Output Neurons ◽  
Positive Valence ◽  
Patterns Of Interaction

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in Drosophila larva. The connectome revealed multiple convergence neuron types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate to modify behavior.

scholarly journals Circuits for integrating learnt and innate valences in the fly brain

2020 ◽  
Author(s):  
Claire Eschbach ◽  
Akira Fushiki ◽  
Michael Winding ◽  
Bruno Afonso ◽  
Ingrid V Andrade ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Functional Imaging ◽  
Animal Behavior ◽  
Mushroom Body ◽  
Individual Experience ◽  
Lateral Horn ◽  
Olfactory Pathways ◽  
Sensory Cue ◽  
Output Neurons ◽  
Direct Input

AbstractAnimal behavior is shaped both by evolution and by individual experience. In many species parallel brain pathways are thought to encode innate and learnt behavior drives and as a result may link the same sensory cue to different actions if innate and learnt drives are in opposition. How these opposing drives are integrated into a single coherent action is not well understood. In insects, the Mushroom Body Output Neurons (MBONs) and the Lateral Horn Neurons (LHNs) are thought to provide the learnt and innate drives, respectively. However their patterns of convergence and the mechanisms by which their outputs are used to select actions are not well understood. We used electron microscopy reconstruction to comprehensively map the downstream targets of all MBONs in Drosophila larva and characterise their patterns of convergence with LHNs. We discovered convergence neurons that receive direct input from MBONs and LHNs and compare opposite behaviour drives. Functional imaging and optogenetic manipulation suggest these convergence neurons compute the overall predicted value of approaching or avoiding an odor and mediate action selection. Our study describes the circuit mechanisms allowing integration of opposing drives from parallel olfactory pathways.

scholarly journals Multimodal integration and stimulus categorization in putative mushroom body output neurons of the honeybee

2018 ◽  
Vol 5 (2) ◽  
pp. 171785 ◽  
Author(s):  
Martin F. Strube-Bloss ◽  
Wolfgang Rössler
Keyword(s):  
Visual Information ◽  
Mushroom Body ◽  
Visual Cues ◽  
Sensory Modality ◽  
Sensory Information ◽  
Insect Brain ◽  
Pollinating Insects ◽  
Output Neurons ◽  
Electrophysiological Characterization ◽  
Nonlinear Integration

Flowers attract pollinating insects like honeybees by sophisticated compositions of olfactory and visual cues. Using honeybees as a model to study olfactory–visual integration at the neuronal level, we focused on mushroom body (MB) output neurons (MBON). From a neuronal circuit perspective, MBONs represent a prominent level of sensory-modality convergence in the insect brain. We established an experimental design allowing electrophysiological characterization of olfactory, visual, as well as olfactory–visual induced activation of individual MBONs. Despite the obvious convergence of olfactory and visual pathways in the MB, we found numerous unimodal MBONs. However, a substantial proportion of MBONs (32%) responded to both modalities and thus integrated olfactory–visual information across MB input layers. In these neurons, representation of the olfactory–visual compound was significantly increased compared with that of single components, suggesting an additive, but nonlinear integration. Population analyses of olfactory–visual MBONs revealed three categories: (i) olfactory, (ii) visual and (iii) olfactory–visual compound stimuli. Interestingly, no significant differentiation was apparent regarding different stimulus qualities within these categories. We conclude that encoding of stimulus quality within a modality is largely completed at the level of MB input, and information at the MB output is integrated across modalities to efficiently categorize sensory information for downstream behavioural decision processing.

Mushroom Body of the Honeybee

2017 ◽  
pp. 353-360
Author(s):  
Jürgen Rybak ◽  
Randolf Menzel
Keyword(s):  
Mushroom Body ◽  
Higher Order ◽  
Insect Species ◽  
Insect Brain ◽  
Projection Neurons ◽  
Kenyon Cells ◽  
Output Neurons ◽  
The Brain

The mushroom body (MB) in the insect brain is composed of a large number of densely packed neurons called Kenyon cells (KCs) (Drosophila, 2200; honeybee, 170,000). In most insect species, the MB consists of two caplike dorsal structures, the calyces, which contain the dendrites of KCs, and two to four lobes formed by collaterals of branching KC axons. Although the MB receives input and provides output throughout its whole structure, the neuropil part of the calyx receives predominantly multimodal input from sensory projection neurons (PNs) of second or a higher order, and the lobes send output neurons to many other parts of the brain, including recurrent neurons to the MB calyx. Widely branching, supposedly modulatory neurons (serotonergic, octopaminergic) innervate the MB at all levels (calyx, peduncle, and lobes), including the somata of KCs in the calyx (dopamine).

scholarly journals Multilevel feedback architecture for adaptive regulation of learning in the insect brain

2019 ◽  
Author(s):  
Claire Eschbach ◽  
Akira Fushiki ◽  
Michael Winding ◽  
Casey M. Schneider-Mizell ◽  
Mei Shao ◽  
...  
Keyword(s):  
Associative Learning ◽  
Large Population ◽  
Memory Systems ◽  
Excitatory Input ◽  
Insect Brain ◽  
Inhibitory Input ◽  
Afferent Pathways ◽  
Computational Performance ◽  
Brain Center ◽  
Predicted Values

Modulatory (e.g. dopaminergic) neurons provide “teaching signals” that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of theDrosophilalarva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one- and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

scholarly journals The complete connectome of a learning and memory center in an insect brain

2017 ◽  
Author(s):  
Katharina Eichler ◽  
Feng Li ◽  
Ashok Litwin-Kumar ◽  
Youngser Park ◽  
Ingrid Andrade ◽  
...  
Keyword(s):  
Learning And Memory ◽  
Mushroom Body ◽  
Negative Reinforcement ◽  
Insect Brain ◽  
Projection Neurons ◽  
Functional Studies ◽  
Output Neuron ◽  
Kenyon Cells ◽  
Output Neurons ◽  
Complete Circuit

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higherorder circuit supporting associative memory has not been previously available. We reconstructed one such circuit at synaptic resolution, theDrosophilalarval mushroom body, and found that most Kenyon cells integrate random combinations of inputs but a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections between output neurons could enhance the selection of learned responses. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory center.

scholarly journals An opposing self-reinforced odor pre-exposure memory produces latent inhibition in Drosophila

2021 ◽  
Author(s):  
Pedro F. Jacob ◽  
Paola Vargas-Gutierrez ◽  
Zeynep Okray ◽  
Stefania Vietti-Michelina ◽  
Johannes Felsenberg ◽  
...  
Keyword(s):  
Latent Inhibition ◽  
Dopaminergic Neurons ◽  
Mushroom Body ◽  
Prior Experience ◽  
Memory Performance ◽  
Memory Testing ◽  
Negative Valence ◽  
Behavioral Expression ◽  
Output Neurons ◽  
Body Compartments

AbstractPrior experience of a stimulus can inhibit subsequent acquisition or expression of a learned association of that stimulus. However, the neuronal manifestations of this learning effect, named latent inhibition (LI), are poorly understood. Here we show that odor pre-exposure produces LI of appetitive olfactory memory performance in Drosophila. Behavioral expression of LI requires that the context during memory testing resembles that during the odor pre-exposures. Odor pre-exposure forms an aversive memory that requires dopaminergic neurons that innervate the γ2α′1 and α3 mushroom body compartments - those to α3 exhibit increasing odor-driven activity with successive pre-exposures. In contrast, odor-specific responses of the corresponding mushroom body output neurons are suppressed. Odor pre-exposure therefore recruits specific dopaminergic neurons that provide teaching signals that attach negative valence to the odor itself. LI of Drosophila appetitive memory consequently results from a temporary and context-dependent retrieval deficit imposed by competition with this short-lived aversive memory.

scholarly journals EXPRESS: When Symmetric and Curved Visual Contour Meet Intentional Instructions: Hedonic Value and Preference

2021 ◽  
pp. 174702182110215
Author(s):  
Erick G. Chuquichambi Apaza ◽  
Guido B. Corradi ◽  
Enric Munar ◽  
Jaume Rosselló-Mir
Keyword(s):  
Cognitive Processing ◽  
Experimental Session ◽  
Hedonic Tone ◽  
Stimulus Response ◽  
Negative Valence ◽  
Visual Contour ◽  
Positive Valence ◽  
Positive And Negative Valence ◽  
Implicit And Explicit ◽  
Explicit Measures

Symmetry and contour take part in shaping visual preference. However, less is known about their combined contribution to preference. We examined the hedonic tone and preference triggered by the interaction of symmetry and contour. Symmetric/curved, symmetric/sharp-angled, asymmetric/curved, and asymmetric/sharp-angled stimuli were presented in an implicit and explicit task. The implicit task consisted of an affective stimulus-response compatibility task where participants matched the stimuli with positive and negative valence response cues. The explicit task recorded liking ratings from the same stimuli. We used instructed mindset to induce participants to focus on symmetry or contour in different parts of the experimental session. We found an implicit compatibility of symmetry and curvature with positive hedonic tone. Explicit results showed preference for symmetry and curvature. In both tasks, symmetry and curvature showed a cumulative interaction, with a larger contribution of symmetry to the overall effect. While symmetric and asymmetric stimuli contributed to the implicit positive valence of symmetry, the effect of curvature was mainly caused by inclination toward curved contours rather than rejection of sharp-angled contours. We did not find any correlation between implicit and explicit measures, suggesting that they may involve different cognitive processing.

Emotionality effects in ambiguous word recognition: The crucial role of the affective congruence between distinct meanings of ambiguous words

2021 ◽  
pp. 174702182199000
Author(s):  
Pilar Ferré ◽  
Juan Haro ◽  
Daniel Huete-Pérez ◽  
Isabel Fraga
Keyword(s):  
Word Processing ◽  
Reaction Times ◽  
Ambiguous Word ◽  
Semantic Ambiguity ◽  
Negative Valence ◽  
Ambiguous Words ◽  
Positive Valence ◽  
Affective Meanings ◽  
Valence Effect ◽  
Affective Word

There is substantial evidence that affectively charged words (e.g., party or gun) are processed differently from neutral words (e.g., pen), although there are also inconsistent findings in the field. Some lexical or semantic variables might explain such inconsistencies, due to the possible modulation of affective word processing by these variables. The aim of the present study was to examine the extent to which affective word processing is modulated by semantic ambiguity. We conducted a large lexical decision study including semantically ambiguous words (e.g., cataract) and semantically unambiguous words (e.g., terrorism), analysing the extent to which reaction times (RTs) were influenced by their affective properties. The findings revealed a valence effect in which positive valence made RTs faster, whereas negative valence slowed them. The valence effect diminished as the semantic ambiguity of words increased. This decrease did not affect all ambiguous words, but was observed mainly in ambiguous words with incongruent affective meanings. These results highlight the need to consider the affective properties of the distinct meanings of ambiguous words in research on affective word processing.

scholarly journals Predictive olfactory learning in Drosophila

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chang Zhao ◽  
Yves F. Widmer ◽  
Sören Diegelmann ◽  
Mihai A. Petrovici ◽  
Simon G. Sprecher ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Dopaminergic Neurons ◽  
Mushroom Body ◽  
Trace Conditioning ◽  
Fruit Fly ◽  
Olfactory Learning ◽  
Shock Strength ◽  
Behavioral Experiments ◽  
Kenyon Cells ◽  
Output Neurons

AbstractOlfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

scholarly journals Intrinsic Dynamics in Neuronal Networks. I. Theory

2000 ◽  
Vol 83 (2) ◽  
pp. 808-827 ◽  
Author(s):  
P. E. Latham ◽  
B. J. Richmond ◽  
P. G. Nelson ◽  
S. Nirenberg
Keyword(s):  
Firing Rate ◽  
Firing Pattern ◽  
Neuronal Networks ◽  
Network Connectivity ◽  
Excitatory Input ◽  
High Rate ◽  
Inhibitory Neurons ◽  
Inhibitory Input ◽  
Dynamic Interactions ◽  
Firing Patterns

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

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