We calculate the quantum states of regular polygons made of 1D quantum wires treating each polygon vertex as a scatterer. The vertex scattering matrix is analytically obtained from the model of a circular bend of a given angle of a 2D nanowire. In the single mode limit the spectrum is classified in doublets of vanishing circulation, twofold split by the small vertex reflection, and singlets with circulation degeneracy. Simple analytic expressions of the energy eigenvalues are given. It is shown how each polygon is characterized by a specific spectrum.

Neurons encode and transmit information in spike sequences. However, despite the effort devoted to quantify their information content, little progress has been made in this regard. Here we use a nonlinear method of time-series analysis (known as ordinal analysis) to compare the statistics of spike sequences generated by applying an input signal to the neuronal model of Morris-Lecar. In particular we consider two different regimes for the neurons which lead to two classes of excitability: class I, where the frequency-current curve is continuous and class II, where the frequency-current curve is discontinuous. By applying ordinal analysis to sequences of inter-spike-intervals (ISIs) our goals are (1) to investigate if different neuron types can generate spike sequences which have similar symbolic properties; (2) to get deeper understanding on the effects that electrical (diffusive) and excitatory chemical (i.e., excitatory synapse) couplings have; and (3) to compare, when a small-amplitude periodic signal is applied to one of the neurons, how the signal features (amplitude and frequency) are encoded and transmitted in the generated ISI sequences for both class I and class II type neurons and electrical or chemical couplings. We find that depending on the frequency, specific combinations of neuron/class and coupling-type allow a more effective encoding, or a more effective transmission of the signal. a) ElectronicSensory neurons detect, encode and transmit information of external temporal stimuli (input signals such as visual, auditory, olfactory, etc.) in sequences of spikes, also known as action potentials. Despite decades of effort to understand how information is processed, the underlying mechanisms of neuronal encoding are still not fully understood. Different coding mechanisms have been proposed in the literature, which can be more or less effective depending on the level of environmental noise, the level of the external signal, and its frequency. Here we focus on the encoding of a small-amplitude periodic signal. We use a well-known neuron model to investigate the role of the excitability class of the neurons (either class I or class II) and of the type of coupling (electrical or chemical) to a second neuron that does not perceive the external signal. We find that the neuron can encode the signal in the form of preferred (more expressed) temporal spike patterns, regardless of the class of neuron and of the type of coupling. On the contrary, depending on the signal frequency, specific combinations of neuron-class and coupling-type allow a more effective encoding, or a more effective transmission of the signal.

We discuss a model of random segmented wire, with linear segments of two-dimensional wires joined by circular bends. The joining vertices act as scatterers on the propagating electron waves. The model leads to resonant Anderson localization when all segments are of similar length. The resonant behavior is present with one and also with several propagating modes. The probability distributions evolve from diffusive to localized regimes when increasing the number of segments in a similar way for long and short localization lengths. As a function of the energy, a finite segmented wire typically evolves from localized to diffusive to ballistic behavior in each conductance plateau.

Cortical circuits operate in a tight excitation/inhibition balance. This balance is relaxed during learning processes, but neither the mechanism nor its impact on network operations are well understood. In the present study, we combined in-vivo and in-vitro neuronal recordings with computational modelling and demonstrated that synaptic plasticity in the afferents from the entorhinal cortex (EC) to the dentate gyrus (DG), in addition to strengthening the glutamatergic inputs into granule cells (GCs), depressed perisomatic inhibition. Computational modelling revealed a functional reorganization in the inhibitory network that explained several experimental findings, including depression of the feed-forward inhibition. In vitro results confirmed a perisomatic dominance of the inhibitory regulation with important functional consequences. It favoured GCs burst firing, improved reliability of input/output transformations and enhanced separation and transmission of temporal and spatial patterns in the EC-DG-CA3 network.