25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Neural correlates of side-specific odour memory in mushroom body output neurons

Humans and other mammals as well as honeybees learn a unilateral association between an olfactory stimulus presented to one side and a reward. In all of them, the learned association can be behaviourally retrieved via contralateral stimulation, suggesting inter-hemispheric communication. However, the underlying neuronal circuits are largely unknown and neural correlates of across-brain-side plasticity have yet not been demonstrated. We report neural plasticity that reflects lateral integration after side-specific odour reward conditioning. Mushroom body output neurons that did not respond initially to contralateral olfactory stimulation developed a unique and stable representation of the rewarded compound stimulus (side and odour) predicting its value during memory retention. The encoding of the reward-associated compound stimulus is delayed by about 40 ms compared with unrewarded neural activity, indicating an increased computation time for the read-out after lateral integration

Recording mushroom body output neurons at the ventral vertical lobe of the honeybee from Multimodal integration and stimulus categorization in putative mushroom body output neurons of the honeybee

Preparation details to record activity from mushroom body output neurons in the honeybee brai

Recording position and additional analysis from Neural correlates of side-specific odour memory in mushroom body output neurons

S1: Recordings from mushroombody output neurons, S2: Ipsilateral induced odour reponse activity in MBONs was not significantly affected by contralateral odour reward association, S3 Spike sortin

Ensemble Response in Mushroom Body Output Neurons of the Honey Bee Outpaces Spatiotemporal Odor Processing Two Synapses Earlier in the Antennal Lobe

<div><p>Neural representations of odors are subject to computations that involve sequentially convergent and divergent anatomical connections across different areas of the brains in both mammals and insects. Furthermore, in both mammals and insects higher order brain areas are connected via feedback connections. In order to understand the transformations and interactions that this connectivity make possible, an ideal experiment would compare neural responses across different, sequential processing levels. Here we present results of recordings from a first order olfactory neuropile – the antennal lobe (AL) – and a higher order multimodal integration and learning center – the mushroom body (MB) – in the honey bee brain. We recorded projection neurons (PN) of the AL and extrinsic neurons (EN) of the MB, which provide the outputs from the two neuropils. Recordings at each level were made in different animals in some experiments and simultaneously in the same animal in others. We presented two odors and their mixture to compare odor response dynamics as well as classification speed and accuracy at each neural processing level. Surprisingly, the EN ensemble significantly starts separating odor stimuli rapidly and before the PN ensemble has reached significant separation. Furthermore the EN ensemble at the MB output reaches a maximum separation of odors between 84–120 ms after odor onset, which is 26 to 133 ms faster than the maximum separation at the AL output ensemble two synapses earlier in processing. It is likely that a subset of very fast PNs, which respond before the ENs, may initiate the rapid EN ensemble response. We suggest therefore that the timing of the EN ensemble activity would allow retroactive integration of its signal into the ongoing computation of the AL via centrifugal feedback.</p> </div

Fast PNs mediate the rapid EN ensemble response.

<p>(<b>A</b>) The latency distribution of 37 PNs (pink) and 28 ENs (blue) out of the bees in which we recorded both levels simultaneously are shown independent of odor identity. Asterisks indicate the significant distances between both distributions (significance level q>0.95). The distribution of the PN latencies starts at ∼40 ms. At this time no EN has responded. The EN latency distribution starts at ∼80 ms. In addition there are significantly more PNs starting between 100–140 ms. At around 200 ms after stimulus onset this relationship flipped and there are significantly more ENs starting their responses. (<b>B</b>) Averaged Euclidean distances calculated out of the three odor pairs (1-hexanol vs. 2-octanone, 1-hexanol vs. mixture and 2-octanone vs. mixture) for PNs (N = 37, red) and ENs (N = 28, blue) which were recorded simultaneously in 8 bees. Distances were normalized and the half maxima (grey dotted line) are drawn. Note, also in the reduced dataset of 8 bees the EN-population separates the odor stimuli faster than the PN-population (cp. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050322#pone-0050322-g006" target="_blank">Figure 6B</a>).</p

Distinct Instantaneous Firing Rate (IFR) distributions in PNs and ENs.

<p>(<b>A</b>) Examples of three typical PN-units (columns). Each column includes the dot displays for the different odors consisting of 10 repetitions (trials) were each dot corresponds to one action potential. At the very bottom of each column the mean IFR is shown (grey  = 1-hexanol, green  = 2-octanone, black  =  mixture). Odor stimulation is marked in grey. (<b>B</b>) Same as in (A), but for three typical EN-units. Both, PN- and EN-units show on and off responses, which were relatively sharp in the EN-units. In addition a tonic component appears to be very prominent in the PN-unit responses (A). (<b>C</b>) IFR distribution functions (IFRs) of all PN-unit's (N = 111) and all EN-units (N = 75) broken down by odor (color code same as in A). In both, PN-units and EN-units IFRs were significantly different (Wilcoxon rank sum test: p<0.001) for all odors. (<b>D</b>) Averaged across odors the mean IFRs clearly separate the recorded PN-ensemble from the recorded EN-ensemble (Wilcoxon rank sum test: p<0.001). Note, the IFRs of the PNs were dominated by the stimulus outlasting tonic response component, whereas EN IFRs reflecting the phasic activity around stimulus on- and off- set.</p

Principal Component Analysis (PCA) of the EN ensemble for each odor stimulus.

<p>(<b>A</b>) left: The weights of each unit (blue bars; N = 73) on the first principal component PC1 was used to order the units from positive (top) to negative weights (bottom). (<b>A</b>) right: The first and the last 20 EN-units were used to illustrate the features covered by PC1. Each line represent the false color coded IFR calculated out of the 10 repetitions per odor and unit. Stimulation is marked by red lines. PC1 contrasted odor sensitive and insensitive units. (<b>B</b>) In contrast to PNs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050322#pone-0050322-g003" target="_blank">Figure 3</a>), the response spectrum of the ENs is rather odor unspecific. Most of the 20 main contributing units were involved in the representation of at least two stimuli. For example from the 20 first main contributing units of 1-hexanol, 25% respond to 1-hexanol only, 30% to 1-hexanol and 2-octanone, 15% to 1-hexanol and the mixture, and 30% to all stimuli. The same tendency was shown by the first 20 main contributing units of 2-octanone. (<b>C</b>) The transients of the first three PCs (PC1, PC2 and PC3) were drawn. The column's separate the odors while the rows represent different time windows during stimulus presentation. Background activity of 1000 ms before odor onset is marked in black. The first row illustrates the first 1000 ms after odor onset (blue), during which time there is a rapid divergence from background to form a transient. In contrast to PNs there is no ‘fixed point’ dynamics during the following 2000 ms (second row, light blue). During the 1500 ms following the odor off set (third row, blue) the most drastic activity increase occurs for the mixture.</p

Simultaneous extracellular recordings of the input and the output of the mushroom body (MB).

<p>(<b>A</b>) Divergent-convergent olfactory processing in the mushroom body (MB). Divergence: around 800 projection neurons (PNs) send information of the Antennal lobe (AL) to the Kenyon cells (KC) of the MB lip (∼170,000 KC per MB). Convergence: at the MB output ∼400 extrinsic neurons (ENs) read out activity from the KC axons. The red arrows indicate the assumed direction of neural excitation flow. (<b>B</b>) Confocal microscope image of the recording electrode positions. One electrode was inserted into the ventral part of the alpha-lobe, the other electrode into the dorsal rim of the AL. <b>Zoom</b>; Somata of lateral antenno-cerebral tract projection neurons (l-ACT-PN-S) were back-filled with dye that coated the electrode tips <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050322#pone.0050322-Kirschner1" target="_blank">[16]</a>. Differential recording combinations from the three wires of each electrode revealed signals showing nicely defined wave shapes of different extracellularly recorded action potentials from ENs (top) as well as from PNs (bottom). (<b>C</b>) Dot displays of a simultaneous recorded PN-EN couple in response to 1-hexanol, 2-octanone and the mixture. (<b>D</b>) Upper raw: peri stimulus time histograms (PSTH's, 50 ms time bins); lower raw: mean instantaneous firing rates (IFR, in ms) of the PN-EN couple in (C) (PN = black, EN = gray). Note, the dot displays, the PSTHs and the mean IFRs reflect the same odor response dynamics.</p