862 research outputs found
Decorrelation of neural-network activity by inhibitory feedback
Correlations in spike-train ensembles can seriously impair the encoding of
information by their spatio-temporal structure. An inevitable source of
correlation in finite neural networks is common presynaptic input to pairs of
neurons. Recent theoretical and experimental studies demonstrate that spike
correlations in recurrent neural networks are considerably smaller than
expected based on the amount of shared presynaptic input. By means of a linear
network model and simulations of networks of leaky integrate-and-fire neurons,
we show that shared-input correlations are efficiently suppressed by inhibitory
feedback. To elucidate the effect of feedback, we compare the responses of the
intact recurrent network and systems where the statistics of the feedback
channel is perturbed. The suppression of spike-train correlations and
population-rate fluctuations by inhibitory feedback can be observed both in
purely inhibitory and in excitatory-inhibitory networks. The effect is fully
understood by a linear theory and becomes already apparent at the macroscopic
level of the population averaged activity. At the microscopic level,
shared-input correlations are suppressed by spike-train correlations: In purely
inhibitory networks, they are canceled by negative spike-train correlations. In
excitatory-inhibitory networks, spike-train correlations are typically
positive. Here, the suppression of input correlations is not a result of the
mere existence of correlations between excitatory (E) and inhibitory (I)
neurons, but a consequence of a particular structure of correlations among the
three possible pairings (EE, EI, II)
Frequency dependence of signal power and spatial reach of the local field potential
The first recording of electrical potential from brain activity was reported
already in 1875, but still the interpretation of the signal is debated. To take
full advantage of the new generation of microelectrodes with hundreds or even
thousands of electrode contacts, an accurate quantitative link between what is
measured and the underlying neural circuit activity is needed. Here we address
the question of how the observed frequency dependence of recorded local field
potentials (LFPs) should be interpreted. By use of a well-established
biophysical modeling scheme, combined with detailed reconstructed neuronal
morphologies, we find that correlations in the synaptic inputs onto a
population of pyramidal cells may significantly boost the low-frequency
components of the generated LFP. We further find that these low-frequency
components may be less `local' than the high-frequency LFP components in the
sense that (1) the size of signal-generation region of the LFP recorded at an
electrode is larger and (2) that the LFP generated by a synaptically activated
population spreads further outside the population edge due to volume
conduction
On identifying the neutron star that was born in the supernova that placed 60Fe onto the Earth
Recently, 60Fe was found in the Earth crust formed in a nearby recent
supernova (SN). If the distance to the SN and mass of the progenitor of that SN
was known, then one could constrain SN models. Knowing the positions, proper
motions, and distances of dozens of young nearby neutron stars, we can
determine their past flight paths and possible kinematic origin. Once the birth
place of a neutron star in a SN is found, we would have determined the distance
of the SN and the mass of the SN progenitor star.Comment: refereed NPA5 conference proceedings, in pres
Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong Synapses
Firing-rate models provide a practical tool for studying the dynamics of trial- or population-averaged neuronal signals. A wealth of theoretical and experimental studies has been dedicated to the derivation or extraction of such models by investigating the firing-rate response characteristics of ensembles of neurons. The majority of these studies assumes that neurons receive input spikes at a high rate through weak synapses (diffusion approximation). For many biological neural systems, however, this assumption cannot be justified. So far, it is unclear how time-varying presynaptic firing rates are transmitted by a population of neurons if the diffusion assumption is dropped. Here, we numerically investigate the stationary and non-stationary firing-rate response properties of leaky integrate-and-fire neurons receiving input spikes through excitatory synapses with alpha-function shaped postsynaptic currents for strong synaptic weights. Input spike trains are modeled by inhomogeneous Poisson point processes with sinusoidal rate. Average rates, modulation amplitudes, and phases of the period-averaged spike responses are measured for a broad range of stimulus, synapse, and neuron parameters. Across wide parameter regions, the resulting transfer functions can be approximated by a linear first-order low-pass filter. Below a critical synaptic weight, the cutoff frequencies are approximately constant and determined by the synaptic time constants. Only for synapses with unrealistically strong weights are the cutoff frequencies significantly increased. To account for stimuli with larger modulation depths, we combine the measured linear transfer function with the nonlinear response characteristics obtained for stationary inputs. The resulting linear–nonlinear model accurately predicts the population response for a variety of non-sinusoidal stimuli
- …