Correlations and Synchrony in Threshold Neuron Models

We study how threshold model neurons transfer temporal and interneuronal input correlations to correlations of spikes. We find that the low common input regime is governed by firing rate dependent spike correlations which are sensitive to the detailed structure of input correlation functions. In the high common input regime the spike correlations are insensitive to the firing rate and exhibit a universal peak shape independent of input correlations. Rate heterogeneous pairs driven by common inputs in general exhibit asymmetric spike correlations. All predictions are confirmed in in vitro experiments with cortical neurons driven by synthesized fluctuating input currents.Comment: 5 pages, 10 figure

Amplitude modulated photostimulation for probing neuronal network dynamics

Sensory input arrives in the cortex in the form of dynamic synaptic currents to populations of neurons. How cortical neurons encode and transmit these inputs ultimately determines the cognitive and behavioral response of the animal. Therefore, a number of theoretical studies have attempted to explain the cortical population response in model neuronal networks [1]. Yet, there are few experimental platforms for studying the dynamical rate responses in large living networks that match the manipulability of computational models. As a result, most experimental studies examining cortical input response properties are confined to independent or single neurons, e.g

In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules

The brain’s connectome provides the scaffold for canonical neural computations. However, a comparison of connectivity studies in the mouse primary visual cortex (V1) reveals that the average number and strength of connections between specific neuron types can vary. Can variability in V1 connectivity measurements coexist with canonical neural computations? We developed a theory-driven approach to deduce V1 network connectivity from visual responses in mouse V1 and visual thalamus (dLGN). Our method revealed that the same recorded visual responses were captured by multiple connectivity configurations. Remarkably, the magnitude and selectivity of connectivity weights followed a specific order across most of the inferred connectivity configurations. We argue that this order stems from the specific shapes of the recorded contrast response functions and contrast invariance of orientation tuning. Remarkably, despite variability across connectivity studies, connectivity weights computed from individual published connectivity reports followed the order we identified with our method, suggesting that the relations between the weights, rather than their magnitudes, represent a connectivity motif supporting canonical V1 computations

Representation of Dynamical Stimuli in Populations of Threshold Neurons

Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework

Linear response to mean and variance modulations in a population of independent threshold neurons.

<p>(A) Normalized amplitude vs. in response to mean current modulations, simulations (circles) and analytical results in Eq. 21 (solid line). (B) vs. in response to current variance modulations, simulations (circles) and analytical results in Eq. 24 (solid lines). Regimes of high-pass and low-pass behavior for linear response function for mean (C) and variance modulations (D). Note, vector strength in (A) and (B) is proportional to the linear response , see Eq. 53.</p

Computational role of mean-encoded signals.

<p>(Top) Representation of periodic mean stimuli in the population rate of noisy, independent neurons. (Left) Representation of step-like mean signals in the population rate of noisy, independent neurons. (Bottom) Common fluctuating currents from presynaptic partners represent a common mean signal that leads to pairwise spike correlation function . (Right) The average voltage before a spike is shaped by the linear mean response. denotes the input current correlation function. The role of the linear response function is indicated by a dashed green line. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002239#s2" target="_blank">Results</a> obtained in the alternative threshold model are discussed in the indicated Sections of this manuscript.</p