How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity

Spikelets in pyramidal neurons: generating mechanisms, distinguishing properties, and functional implications

Spikelets are small spike-like depolarizations that are found in somatic recordings of many neuron types. Spikelets have been assigned important functions, ranging from neuronal synchronization to the regulation of synaptic plasticity, which are specific to the particular mechanism of spikelet generation. As spikelets reflect spiking activity in neuronal compartments that are electrotonically distinct from the soma, four modes of spikelet generation can be envisaged: (1) dendritic spikes or (2) axonal action potentials occurring in a single cell as well as action potentials transmitted via (3) gap junctions or (4) ephaptic coupling in pairs of neurons. In one of the best studied neuron type, cortical pyramidal neurons, the origins and functions of spikelets are still unresolved; all four potential mechanisms have been proposed, but the experimental evidence remains ambiguous. Here we attempt to reconcile the scattered experimental findings in a coherent theoretical framework. We review in detail the various mechanisms that can give rise to spikelets. For each mechanism, we present the biophysical underpinnings as well as the resulting properties of spikelets and compare these predictions to experimental data from pyramidal neurons. We also discuss the functional implications of each mechanism. On the example of pyramidal neurons, we illustrate that several independent spikelet-generating mechanisms fulfilling vastly different functions might be operating in a single cell

Double- to Single-Strand Transition Induces Forces and Motion in DNA Origami Nanostructures

The design of dynamic, reconfigurable devices is crucial for the bottom-up construction of artificial biological systems. DNA can be used as an engineering material for the de-novo design of such dynamic devices. A self-assembled DNA origami switch is presented that uses the transition from double- to single-stranded DNA and vice versa to create and annihilate an entropic force that drives a reversible conformational change inside the switch. It is distinctively demonstrated that a DNA single-strand that is extended with 0.34 nm per nucleotide - the extension this very strand has in the double-stranded configuration - exerts a contractive force on its ends leading to large-scale motion. The operation of this type of switch is demonstrated via transmission electron microscopy, DNA-PAINT super-resolution microscopy and darkfield microscopy. The work illustrates the intricate and sometimes counter-intuitive forces that act in nanoscale physical systems that operate in fluids

Genetic variability in European populations of Coregonus lavaretus (L.): an assessment based on mitochondrial ND-1 gene haplotypes

Abstract. The genetic variability of whitefish, Coregonus lavaretus (L.), was studied based on 114 individuals from nine populations inhabiting Polish lakes, including the Szczecin Lagoon, and from one population each from lakes in Austria and Switzerland. Differences within and among populations were assessed with mitochondrial ND-1 gene sequences that were PCR amplified and digested with ten restriction enzymes. The ten composite haplotypes obtained were sequenced and analyzed with MEGA4 software. The highest intrapopulation variability was noted in the whitefish populations inhabiting lakes Iñsko, Miedwie, Marianowo, Wisola, OEremskie, Morzycko, the Szczecin Lagoon, and Lake Lucerne, which possessed from two to five composite haplotypes. In contrast, populations inhabiting lakes Wooewin, Czarne, and Traunsee were fixed for the most common haplotype H2

Analysis of the intraspinal calcium dynamics and its implications on the plasticity of spiking neurons

The influx of calcium ions into the dendritic spines through the N-metyl-D-aspartate (NMDA) channels is believed to be the primary trigger for various forms of synaptic plasticity. In this paper, the authors calculate analytically the mean values of the calcium transients elicited by a spiking neuron undergoing a simple model of ionic currents and back-propagating action potentials. The relative variability of these transients, due to the stochastic nature of synaptic transmission, is further considered using a simple Markov model of NMDA receptos. One finds that both the mean value and the variability depend on the timing between pre- and postsynaptic action-potentials. These results could have implications on the expected form of synaptic-plasticity curve and can form a basis for a unified theory of spike time-dependent, and rate based plasticity.Comment: 14 pages, 10 figures. A few changes in section IV and addition of a new figur

PULSED DOPPLER FROM THE SUPRASTERNAL NOTCH SYSTEMATICALLY UNDERESTIMATES MEAN BLOOD FLOW VELOCITY IN THE ASCENDING AORTA COMPARED TO PHASE CONTRAST MRI

Background Continuous pulsed-wave Doppler readings of flow velocity in the ascending aorta from the suprasternal position (sCD) are widely used in estimating stroke volume, particularly during physiological challenge maneuvers such as head-up tilt testing. Stroke volume is derived from velocity time integrals and vessel area. We compared the sCD against an established gold standard. Methods In 12 healthy women and men, we obtained 2D cross sectional, velocity encoded phase contrast MRI of the ascending aorta (2DMRI) and sCD to measure mean blood flow velocity (Vmean) at the ascending aorta. We compared sCD insonation depth to the distance between Doppler probe and sinotubular junction measured by MRI. Within an aortic 4D-Flow dataset, allowing flow measurements in every anatomical point along the ascending aorta, Vmean was determined at the sCD measurement point for comparison. Results sCD significantly underestimated Vmean compared with 2DMRI at the sinotubular junction (Vmean 2DMRI – Vmean sCD = 24.42 cm/s ± 12.55 cm/s, p = <0.001). Moreover, sCD sampled flow velocities 21.8 mm ± 7mm (p = <0.001) or 26% off the sinotubular junction. Yet, depth and velocity differences between sCD and 2DMRI were not correlated with each other (Pearson r = -0.147; p = 0.648). When we applied 4DMRI to assess flow velocity at the sCD measurement site, the Vmean difference between methodologies was reduced to 9.1 cm/s ± 12.38 cm/s (p = 0.035). Conclusion sCD profoundly underestimates Vmean in the ascending aorta compared to 4DMRI. The methodology has important limitations in accessing the ideal position for aortic flow measurements and precise information regarding the position of data acquisition for vessel area quantification cannot be ascertained. Overall, sCD is of limited utility in measuring absolute stroke volum

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatio-temporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatio-temporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast alpha-function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with alpha-function is reduced into the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision.Comment: Accepted for publication in Phys. Rev.

An associative memory of Hodgkin-Huxley neuron networks with Willshaw-type synaptic couplings

An associative memory has been discussed of neural networks consisting of spiking N (=100) Hodgkin-Huxley (HH) neurons with time-delayed couplings, which memorize P patterns in their synaptic weights. In addition to excitatory synapses whose strengths are modified after the Willshaw-type learning rule with the 0/1 code for quiescent/active states, the network includes uniform inhibitory synapses which are introduced to reduce cross-talk noises. Our simulations of the HH neuron network for the noise-free state have shown to yield a fairly good performance with the storage capacity of $\alpha_c = P_{\rm max}/N \sim 0.4 - 2.4$ for the low neuron activity of $f \sim 0.04-0.10$. This storage capacity of our temporal-code network is comparable to that of the rate-code model with the Willshaw-type synapses. Our HH neuron network is realized not to be vulnerable to the distribution of time delays in couplings. The variability of interspace interval (ISI) of output spike trains in the process of retrieving stored patterns is also discussed.Comment: 15 pages, 3 figures, changed Titl

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures