Passive frame theory: A new synthesis

Passive frame theory attempts to illuminate what consciousness is, in mechanistic and functional terms; it does not address the "implementation" level of analysis (how neurons instantiate conscious states), an enigma for various disciplines. However, in response to the commentaries, we discuss how our framework provides clues regarding this enigma. In the framework, consciousness is passive albeit essential. Without consciousness, there would not be adaptive skeletomotor action.

Chemosensor-driven artificial antennal lobe transient dynamics enable fast recognition and working memory

The speed and accuracy of odor recognition in insects can hardly be resolved by the raw descriptors provided by olfactory receptors alone due to their slow time constant and high variability. The animal overcomes these barriers by means of the antennal lobe (AL) dynamics, which consolidates the classificatory information in receptor signal with a spatiotemporal code that is enriched in odor sensitivity, particularly in its transient. Inspired by this fact, we propose an easily implementable AL-like network and show that it significantly expedites and enhances the identification of odors from slow and noisy artificial polymer sensor responses. The device owes its efficiency to two intrinsic mechanisms: inhibition (which triggers a competition) and integration (due to the dynamical nature of the network). The former functions as a sharpening filter extracting the features of receptor signal that favor odor separation, whereas the latter implements a working memory by accumulating the extracted features in trajectories. This cooperation boosts the odor specificity during the receptor transient, which is essential for fast odor recognition.

Dynamics of input patterns modulate the behavior of a model of olfactory bulb function

Input patterns to the olfactory bulb are dynamic and change in an odor-specific manner as measured by selective calcium imaging of olfactory bulb input. To our knowledge, none of the published models of olfactory bulb function uses dynamic input patterns. Therefore we tested how dynamic input alters the behavior of a simple model consisting of two layers. The membrane potential of the first-layer neurons, integrate-and-fire neurons corresponding to mitral cells, was modulated with a subthreshold oscillation at respiration frequency. The membrane potential of the second-layer neurons was used to discriminate input patterns. We implemented oscillating input with amplitudes and latencies different for each mitral cell. Not only varying the input amplitudes but also de-synchronizing the input, and varying the relation between latency and input amplitude, individually changed the model's performance significantly. The discrimination time was affected more easily than the number of second-layer neurons that can differentiate an odor pair. Increasing the de-synchronization, i.e., the spread of latency values, reduced the differences in response time between strong and weak stimulus pairs without reducing the number of reacting cells. Input phase relative to the subthreshold oscillation altered the effect of de-synchronization. Thus dynamic input changes performance parameters of models of olfactory information processing that can be verified experimentally.