11,867 research outputs found
The Effect of synchronized inputs at the single neuron level
It is commonly assumed that temporal synchronization of excitatory synaptic inputs onto a single neuron increases its firing rate. We investigate here the role of synaptic synchronization for the leaky integrate-and-fire neuron as well as for a biophysically and anatomically detailed compartmental model of a cortical pyramidal cell. We find that if the number of excitatory inputs, N, is on the same order as the number of fully synchronized inputs necessary to trigger a single action potential, N_t, synchronization always increases the firing rate (for both constant and Poisson-distributed input). However, for large values of N compared to N_t, ''overcrowding'' occurs and temporal synchronization is detrimental to firing frequency. This behavior is caused by the conflicting influence of the low-pass nature of the passive dendritic membrane on the one hand and the refractory period on the other. If both temporal synchronization as well as the fraction of synchronized inputs (Murthy and Fetz 1993) is varied, synchronization is only advantageous if either N or the average input frequency, Ć’(in), are small enough
Cell assembly dynamics of sparsely-connected inhibitory networks: a simple model for the collective activity of striatal projection neurons
Striatal projection neurons form a sparsely-connected inhibitory network, and
this arrangement may be essential for the appropriate temporal organization of
behavior. Here we show that a simplified, sparse inhibitory network of
Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal
population activity, as observed in brain slices [Carrillo-Reid et al., J.
Neurophysiology 99 (2008) 1435{1450]. In particular we develop a new metric to
determine the conditions under which sparse inhibitory networks form
anti-correlated cell assemblies with time-varying activity of individual cells.
We found that under these conditions the network displays an input-specific
sequence of cell assembly switching, that effectively discriminates similar
inputs. Our results support the proposal [Ponzi and Wickens, PLoS Comp Biol 9
(2013) e1002954] that GABAergic connections between striatal projection neurons
allow stimulus-selective, temporally-extended sequential activation of cell
assemblies. Furthermore, we help to show how altered intrastriatal GABAergic
signaling may produce aberrant network-level information processing in
disorders such as Parkinson's and Huntington's diseases.Comment: 22 pages, 9 figure
Attentional modulation of firing rate and synchrony in a model cortical network
When attention is directed into the receptive field of a V4 neuron, its
contrast response curve is shifted to lower contrast values (Reynolds et al,
2000, Neuron 26:703). Attention also increases the coherence between neurons
responding to the same stimulus (Fries et al, 2001, Science 291:1560). We
studied how the firing rate and synchrony of a densely interconnected cortical
network varied with contrast and how they were modulated by attention. We found
that an increased driving current to the excitatory neurons increased the
overall firing rate of the network, whereas variation of the driving current to
inhibitory neurons modulated the synchrony of the network. We explain the
synchrony modulation in terms of a locking phenomenon during which the ratio of
excitatory to inhibitory firing rates is approximately constant for a range of
driving current values. We explored the hypothesis that contrast is represented
primarily as a drive to the excitatory neurons, whereas attention corresponds
to a reduction in driving current to the inhibitory neurons. Using this
hypothesis, the model reproduces the following experimental observations: (1)
the firing rate of the excitatory neurons increases with contrast; (2) for high
contrast stimuli, the firing rate saturates and the network synchronizes; (3)
attention shifts the contrast response curve to lower contrast values; (4)
attention leads to stronger synchronization that starts at a lower value of the
contrast compared with the attend-away condition. In addition, it predicts that
attention increases the delay between the inhibitory and excitatory synchronous
volleys produced by the network, allowing the stimulus to recruit more
downstream neurons.Comment: 36 pages, submitted to Journal of Computational Neuroscienc
Sisyphus Effect in Pulse Coupled Excitatory Neural Networks with Spike-Timing Dependent Plasticity
The collective dynamics of excitatory pulse coupled neural networks with
spike timing dependent plasticity (STDP) is studied. Depending on the model
parameters stationary states characterized by High or Low Synchronization can
be observed. In particular, at the transition between these two regimes,
persistent irregular low frequency oscillations between strongly and weakly
synchronized states are observable, which can be identified as infraslow
oscillations with frequencies 0.02 - 0.03 Hz. Their emergence can be explained
in terms of the Sisyphus Effect, a mechanism caused by a continuous feedback
between the evolution of the coherent population activity and of the average
synaptic weight. Due to this effect, the synaptic weights have oscillating
equilibrium values, which prevents the neuronal population from relaxing into a
stationary macroscopic state.Comment: 18 pages, 24 figures, submitted to Physical Review
Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex
Neuromodulators, like dopamine, have considerable influence on the\ud
processing capabilities of neural networks. \ud
This has for instance been shown in the working memory functions\ud
of prefrontal cortex, which may be regulated by altering the\ud
dopamine level. Experimental work provides evidence on the biochemical\ud
and electrophysiological actions of dopamine receptors, but there are few \ud
theories concerning their significance for computational properties \ud
(ServanPrintzCohen90,Hasselmo94).\ud
We point to experimental data on neuromodulatory regulation of \ud
temporal properties of excitatory neurons and depolarization of inhibitory \ud
neurons, and suggest computational models employing these effects.\ud
Changes in membrane potential may be modelled by the firing threshold,\ud
and temporal properties by a parameterization of neuronal responsiveness \ud
according to the preceding spike interval.\ud
We apply these concepts to two examples using spiking neural networks.\ud
In the first case, there is a change in the input synchronization of\ud
neuronal groups, which leads to\ud
changes in the formation of synchronized neuronal ensembles.\ud
In the second case, the threshold\ud
of interneurons influences lateral inhibition, and the switch from a \ud
winner-take-all network to a parallel feedforward mode of processing.\ud
Both concepts are interesting for the modeling of cognitive functions and may\ud
have explanatory power for behavioral changes associated with dopamine \ud
regulation
Membrane resonance enables stable and robust gamma oscillations
Neuronal mechanisms underlying beta/gamma oscillations (20-80 Hz) are not completely understood. Here, we show that in vivo beta/gamma oscillations in the cat visual cortex sometimes exhibit remarkably stable frequency even when inputs fluctuate dramatically. Enhanced frequency stability is associated with stronger oscillations measured in individual units and larger power in the local field potential. Simulations of neuronal circuitry demonstrate that membrane properties of inhibitory interneurons strongly determine the characteristics of emergent oscillations. Exploration of networks containing either integrator or resonator inhibitory interneurons revealed that: (i) Resonance, as opposed to integration, promotes robust oscillations with large power and stable frequency via a mechanism called RING (Resonance INduced Gamma); resonance favors synchronization by reducing phase delays between interneurons and imposes bounds on oscillation cycle duration; (ii) Stability of frequency and robustness of the oscillation also depend on the relative timing of excitatory and inhibitory volleys within the oscillation cycle; (iii) RING can reproduce characteristics of both Pyramidal INterneuron Gamma (PING) and INterneuron Gamma (ING), transcending such classifications; (iv) In RING, robust gamma oscillations are promoted by slow but are impaired by fast inputs. Results suggest that interneuronal membrane resonance can be an important ingredient for generation of robust gamma oscillations having stable frequency
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