Introduction to the Journal of Evolutionary Economics special issue: the product characteristics approach to innovation studies

International audienceThis special issue collects a selection of papers that were originally presented at the workshop on ‘Demand, Product Characteristics and Innovation’ organized at the Friedrich Schiller University in Jena in October 2007. The workshop was funded by the DIME (Dynamics of Innovation and Markets) Network of Excellence of the European Commission. All these papers are empirical investigations of the dynamics of innovation in particular industries, based on the ‘twin characteristics’ approach pioneered by Saviotti and Metcalfe (1984) exactly 25 years ago. Altogether, the papers contained in this special issue provide a good illustration of the variety of approaches that can be employed in detailed studies of product characteristics. They also provide a good overview of the riches of insights that can be gained using this approach. As the availability of datasets and information on product prices and technical characteristics is likely to increase in the near future, empirical studies based on product characteristics seem to represent a research trajectory that is worthwhile pursuing, especially by younger generations of researchers

Spike and burst coding in thalamocortical relay cells

<div><p>Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA) and the Event-Triggered Covariance (ETC). This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms) and showed a clear distinction between spikes (selective for fluctuations) and bursts (selective for integration). The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (<i>I</i>_<i>T</i>) and the cyclic nucleotide modulated h current (<i>I</i>_<i>h</i>). The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two currents. Finally, the model was used to investigate the more realistic “high-conductance state”, where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.</p></div