A neural model of cross-modal association in insects

Abstract. We developed a computational model of learning in the Mushroom Body, a region of multimodal integration in the insect brain. Using realistic neural dynamics and a biologically-based learning rule (spike timing dependent plasticity), the model is tested as part of an insect brain inspired architecture within a closed loop behavioural task. Replicating in simulation an experiment carried out on bushcrickets, we show the system can successfully associate visual to auditory cues, so as to maintain a steady heading towards an intermittent sound source.

Evolving a Neural Model of Insect Path Integration

Path integration is an important navigation strategy in many animal species. We use a genetic algorithm to evolve a novel neural model of path integration, based on input from cells that encode the heading of the agent in a manner comparable to the polarization-sensitive interneurons found in insects. The home vector is encoded as a population code across a circular array of cells that integrate this input. This code can be used to control return to the home position. We demonstrate the capabilities of the network under noisy conditions in simulation and on a robot

Dietary Salt Levels Affect Salt Preference and Learning in Larval Drosophila

Drosophila larvae change from exhibiting attraction to aversion as the concentration of salt in a substrate is increased. However, some aversive concentrations appear to act as positive reinforcers, increasing attraction to an odour with which they have been paired. We test whether this surprising dissociation between the unconditioned and conditioned response depends on the larvae's experience of salt concentration in their food. We find that although the point at which a NaCl concentration becomes aversive shifts with different rearing experience, the dissociation remains evident. Testing larvae using a substrate 0.025M above the NaCl concentration on which the larvae were reared consistently results in aversive choice behaviour but appetitive reinforcement effects

Place memory in crickets

Certain insect species are known to relocate nest or food sites using landmarks, but the generality of this capability among insects, and whether insect place memory can be used in novel task settings, is not known. We tested the ability of crickets to use surrounding visual cues to relocate an invisible target in an analogue of the Morris water maze, a standard paradigm for spatial memory tests on rodents. Adult female Gryllus bimaculatus were released into an arena with a floor heated to an aversive temperature, with one hidden cool spot. Over 10 trials, the time taken to find the cool spot decreased significantly. The best performance was obtained when a natural scene was provided on the arena walls. Animals can relocate the position from novel starting points. When the scene is rotated, they preferentially approach the fictive target position corresponding to the rotation. We note that this navigational capability does not necessarily imply the animal has an internal spatial representation

Path integration using a model of e-vector orientation coding in the insect brain: reply to Vickerstaff and Di Paolo

In their response to our article (Haferlach, Wessnitzer, Mangan, & Webb, 2007), Vickerstaff and Di Paolo correctly note that the response function of input units used to evolve our network was the same cos(ha – hp) as that used by Vickerstaff and Di Paolo (2005), and resembles the POL neuron arrangement in insects (Labhart & Meyer, 2002) only in the use of three instead of two such units. However it is important to note that our evolved network structure—which maintains a population encoding of the home vector over a set of memory neurons that integrate the input coming from the direction cells—is in fact generalizable to a wide range of direction cell response functions. To demonstrate this point, we here show the results of integrating this network with a very recent model of e-Vector orientation coding in the central complex of the insect brain (Sakura, Lambrinos, & Labhart, 2008

Activity calculations using different computations give similar results for endogenous locomotion and computer-generated data (grouping codes given below the graph)

Pause-activity patterns were determined using either a speed- (left, labeled 1) or a time threshold (right, labeled 2). A. Total activity time represents the time the animal is considered active. A2 was set to be similar in the computer-generated data. B. Duration of activity periods. Inset represents the same calculation as in B2 but considers only activity bouts leading to a displacement of 1 cm or more. C. Duration of the pause periods. D. Number of pauses. Bars represent means and error bars standard errors, n = 20 in each group

Software schematics

The experimenter enters information (in red) about the fly and the platform (semi-automatically) into the tracker application (BuriTrack). The tracker saves this information along with a time stamp in an XML file. Online analysis of the video leads to the extraction of the position of the fly over time, which is directly saved to the data file. The analysis software (CeTrAn) then reads a text file indicating the path to the XML file and the fly grouping information. It then automatically imports the data, transforms it into an easily workable class of data (ltraj) and performs the analyses following different variables the experimenter can set (in red). As outputs, CeTrAn writes R workspaces (before and after the analysis), a csv file of the computed parameters and pdf files where those metrics are plotted against the group factor