Modelling the signal delivered by a population of first-order neurons in a moth olfactory system

A statistical model of the population of first-order olfactory receptor neurons (ORNs) is proposed and analysed. It describes the relationship between stimulus intensity (odour concentration) and coding variables such as rate and latency of the population of several thousand sex-pheromone sensitive ORNs in male moths. Although these neurons likely express the same olfactory receptor, they exhibit, at any concentration, a relatively large heterogeneity of responses in both peak firing frequency and latency of the first action potential fired after stimulus onset. The stochastic model is defined by a multivariate distribution of six model parameters that describe the dependence of the peak firing rate and the latency on the stimulus dose. These six parameters and their mutual linear correlations were estimated from experiments in single ORNs and included in the multidimensional model distribution. The model is utilized to reconstruct the peak firing rate and latency of the message sent to the brain by the whole ORN population at different stimulus intensities and to establish their main qualitative and quantitative properties. Finally, these properties are shown to be in agreement with those found previously in a vertebrate ORN population

A biophysical model of the early olfactory system of honeybees

Experimental measurements often can only provide limited data from an animal’s sensory system. In addition, they exhibit large trial-to-trial and animal-to-animal variability. These limitations pose challenges to building mathematical models intended to make biologically relevant predictions. Here, we present a mathematical model of the early olfactory system of honeybees aiming to overcome these limitations. The model generates olfactory response patterns which conform to the statistics derived from experimental data for a variety of their properties. This allows considering the full dimensionality of the sensory input space as well as avoiding overfitting the underlying data sets. Several known biological mechanisms, including processes of chemical binding and activation of receptors, and spike generation and transmission in the antennal lobe network, are incorporated in the model at a minimal level. It can therefore be used to study how experimentally observed phenomena are shaped by these underlying biophysical processes. We verified that our model can replicate some key experimental findings that were not used when building it. Given appropriate data, our model can be generalized to the early olfactory systems of other insects. It hence provides a possible framework for future numerical and analytical studies of olfactory processing in insects

Towards a Physarum learning chip

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks

Slime mould: The fundamental mechanisms of biological cognition

© 2018 Elsevier B.V. The slime mould Physarum polycephalum has been used in developing unconventional computing devices for in which the slime mould played a role of a sensing, actuating, and computing device. These devices treated the slime mould as an active living substrate, yet it is a self-consistent living creature which evolved over millions of years and occupied most parts of the world, but in any case, that living entity did not own true cognition, just automated biochemical mechanisms. To “rehabilitate” slime mould from the rank of a purely living electronics element to a “creature of thoughts” we are analyzing the cognitive potential of P. polycephalum. We base our theory of minimal cognition of the slime mould on a bottom-up approach, from the biological and biophysical nature of the slime mould and its regulatory systems using frameworks such as Lyon's biogenic cognition, Muller, di Primio-Lengelerś modifiable pathways, Bateson's “patterns that connect” framework, Maturana's autopoietic network, or proto-consciousness and Morgan's Canon

Odour transduction in olfactory receptor neurons

International audienceThe molecular mechanisms that control the binding of odorant to olfactory receptors and transduce this signal into membrane depolarization are reviewed. They are compared in vertebrates and insects for interspecific (allelochemicals) and intraspecific (pheromones) olfactory signals. Attempts to develop quantitative models of these multistage signalling networks are presented. Computational analysis of olfactory transduction is still in its infancy and appears as a promising area for future developments

Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons

In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs), all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs) within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information—primarily the identity and intensity of the stimulus—is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency) or on the average response of many (firing rate). Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations

Low-amplitude, high-frequency electromagnetic field exposure causes delayed and reduced growth in <em>Rosa hybrida</em>

International audienceIt is now accepted that plants perceive high-frequency electromagnetic field (HF-EMF). We wondered if the HF-EMF signal is integrated further in planta as a chain of reactions leading to a modification of plant growth. We exposed whole small ligneous plants (rose bush) whose growth could be studied for several weeks. We performed exposures at two different development stages (rooted cuttings bearing an axillary bud and 5-leaf stage plants), using two high frequency (900 MHz) field amplitudes (5 and 200 V m(-1)). We achieved a tight control on the experimental conditions using a state-of-the-art stimulation device (Mode Stirred Reverberation Chamber) and specialized culture-chambers. After the exposure, we followed the shoot growth for over a one-month period. We observed no growth modification whatsoever exposure was performed on the 5-leaf stage plants. When the exposure was performed on the rooted cuttings, no growth modification was observed on Axis I (produced from the elongation of the axillary bud). Likewise, no significant modification was noted on Axis II produced at the base of Axis I, that came from pre-formed secondary axillary buds. In contrast, Axis II produced at the top of Axis I, that came from post-formed secondary buds consistently displayed a delayed and significant reduced growth (45%). The measurements of plant energy uptake from HF-EMF in this exposure condition (SAR of 7.2 10(-4) W kg(-1)) indicated that this biological response is likely not due to thermal effect. These results suggest that exposure to electromagnetic field only affected development of post-formed organs

Distributions of firing rates (top row) and latencies (bottom row) at single pheromone doses are dose-dependent.

<p>(<b>A</b>) Comparison in ORNs of raw firing rates <i>F</i>_raw (not corrected from control stimulations) for control stimulations (green) and for pheromone doses −1, 0, 1, 2, 3, 4 log ng (blue, from left to right). <i>F</i>_raw at <i>C</i> = −1 log ng not significantly different from control (Kolmogorov-Smirnov test, <i>p</i> = 0.43). (<b>B</b>) Comparison in ORNs of latencies <i>L</i> for same stimuli and doses (from right to left) as in (A). (<b>C</b>) Comparison in PNs of firing rates <i>F</i>_raw for control stimulations (green) and for pheromone doses −3, −2, −1, 0, 1 log ng (red), same representation as in (A). <i>F</i>_raw at <i>C</i> = −3 log ng not significantly different from control (Kolmogorov-Smirnov test, <i>p</i> = 0.43) but significantly different from <i>F</i>_raw at <i>C</i> = −2 (<i>p</i><10⁻⁴). (<b>D</b>) Comparison in PNs of latencies <i>L</i> for same stimuli and doses as in (C). (<b>E, G</b>) Comparison of firing rates <i>F</i> (corrected from control stimulation) in ORNs (blue) and PNs (red) at the same doses −1, 0 (in E) and 1 log ng (in G). For <i>C</i>≤1, the mean firing firing rates of ORNs is smaller than that of PNs. (<b>F–H</b>) Comparison of latencies, same representation as in (E, G). At all doses, the mean firing latency of ORNs is larger than that of PNs. At <i>C</i>≥1, the shortest ORN latencies become almost as short as the shortest PN latencies.</p

Latencies are linear functions of pheromone dose with different parameter values in each neuron.

<p>(<b>A</b>) Measured latency <i>L</i> (dots) of 3 ORNs fitted to decreasing lines (eq. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003975#pcbi.1003975.e008" target="_blank">8</a>; solid curve) showing minimum latency <i>L</i>_m and maximum latency <i>L</i>_M at threshold <i>C</i>₀ given from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003975#pcbi-1003975-g006" target="_blank">Fig. 6A</a>. (<b>B</b>) All (<i>N</i> = 38) fitted ORN dose-latency curves. (<b>C</b>) Three examples of PN latency curves. (<b>D</b>) All (<i>N</i> = 44) fitted PN dose-latency curves. (<b>E</b>) Maximum latencies <i>L</i>_M at threshold dose <i>C</i>₀ fitted to lognormal CDFs; same <i>N</i>'s as in (B, D) and representation as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003975#pcbi-1003975-g006" target="_blank">Fig. 6E</a>. (<b>F</b>) Minimum latencies <i>L</i>_m fitted to normal CDFs; same <i>N</i> and representation as in (E). A few zero latencies arise in PNs from variability on pheromone transport time <i>T</i>_t.</p