Both the type and location of synaptic contacts can have profound functional consequences for cortical pyramidal neurons. Axo-spinous synapses integrate evoked potentials in a linear fashion and axo-shaft ones summate responses sublinearly. Using modeling and simulation, we accurately replicate such type-dependent synaptic integration and examine one of the possible underlying mechanisms. Moreover, the integrative properties of distal spines in pyramidal neurons have a distinct impact on the neuronal output. Our results demonstrate that distal axo-shaft synapses can gate nonlinear dendritic computing with higher threshold and drive somatic potential with higher gain. The implications these results have for the computational capacity of pyramidal neurons are discussed.

In this paper we investigate the consequences of biologically plausible constraints on predictions of the Berger-Levy energy efficient neuron model. As new constraints we consider i) a peak power constraint, ii) peak energy expenditure per ISI constraint, iii) a lower bound on the value of inter spike interval (ISI), and iv) lower and upper bounds on the excitatory postsynaptic potential (EPSP) intensity, λ. Our analysis shows that considering these constraints of the capacity per unit cost maximization problem changes the shape of probability distribution function (PDF) of λ and the ISIs. We show, using numerical solutions of the optimization problem, that the new constraints change the PDFs of λ and the ISIs in term of their shape and location of the peak value. We also derive predictions for how the coefficient of variation (CV) of the ISI is changed, which is easier to characterize experimentally than the full PDF.

Most previous studies treated spikes as all-or-none events, and considered their duration and magnitude as negligible. Action potential (AP) duration varies across neuron types, but its consequences on synaptic plasticity remain largely unexplored. Here we study the effects of AP-duration on spike-timing dependent synaptic plasticity (STDP) by negatively shifting the temporal window, potentiating synapses with presy-naptic EPSPs occurring both before and during a postsynaptic AP. With this interpretation, we demonstrate that AP-duration controls the shape of weight distribution and the temporal fluctuations of the weights after the distribution reaches a steady-state.

Out-of-body experiences (OBEs) are illusions, where people experience themselves as being located outside their physical body and often flying or floating at an elevated location. Here, we propose that the flying and floating in OBEs can be explained as the result of a Bayesian inference, where ambiguous bottom-up signals from the otholiths in a supine position are integrated with a top-down prior for the upright position, which is not appropriate for the current supine position. We also measure these ecologically valid priors for the upright position as the empirical probabilities in natural head movements. Our results suggest a simple interpretation of some aspects of OBEs in terms of a mislead sensory inference and suggests new ways of experimentally inducing OBE-like experiences by manipulating sensory signals and top-down prior information.

One major role of primary visual cortex (V1) in vision is the encoding of the orientation of lines and contours. The role of the local recurrent network in these computations is, however, still a matter of debate. To address this issue, we analyze intracellular recording data of cat V1, which combine measuring the tuning of a range of neuronal properties with a precise localization of the recording sites in the orientation preference map. For the analysis, we consider a network model of Hodgkin-Huxley type neurons arranged according to a biologically plausible two-dimensional topographic orientation preference map. We then systematically vary the strength of the recurrent excitation and inhibition relative to the strength of the afferent input. Each parametrization gives rise to a different model instance for which the tuning of model neurons at different locations of the orientation map is compared to the experimentally measured orientation tuning of membrane potential, spike output, excitatory, and inhibitory conductances. A quantitative analysis shows that the data provides strong evidence for a network model in which the afferent input is dominated by strong, balanced contributions of recurrent excitation and inhibition. This recurrent regime is close to a regime of 'instability', where strong, self-sustained activity of the network occurs. The firing rate of neurons in the best-fitting network is particularly sensitive to small modulations of model parameters, which could be one of the functional benefits of a network operating in this particular regime.

We start from the observation that sensory coding is not a static mapping but a process and focus on this temporal aspect. We propose that first extracting the salient features of a stimulus and then the details is an efficient coding strategy in terms of information processing when the network bandwidth increases with time. The authors: (i) show by using a simple transfer function that modulating the coding strategy from an initially highly competitive to a less competitive mapping is efficient for information encoding in time; and (ii) present simulation results of a detailed computational model of a cortical hypercolumn in order to demonstrate that this strategy could be implemented in the primary visual cortex. They propose a dynamic cortical amplifier model and suggest fast depressing synapses at the excitatory lateral connections as a possible mechanism to modulate the level competition on a short timescale.