Novel Causal Relations between Neuronal Networks due to Synchronization.

In the process of information transmission, information is thought to be transmitted from the networks that are activated by the input to the networks that are silent or nonactivated. Here, via numerical simulation of a 3-network motif, we show that the silent neuronal network when interconnected with other 2 networks can exert much stronger causal influences on the other networks. Such an unexpected causal relationship results from high degree of synchronization in this network. The predominant party is consistently the network whose noise is smaller when the noise level in each network is considered. Our results can shed lights on how the internal network dynamics can affect the information flow of interconnected neuronal networks.

Information encoding in an oscillatory network.

Information encoding in a globally coupled network is studied. When the network is in an oscillatory state, the network activities are dominated by the intrinsic oscillatory current and the stimulus is poorly encoded. However, when the amplitude of the input signal is large, the input can still be well read from the population rate and the temporal correlation between spike trains. The underlying reason is that there exists a competition between the intrinsic correlation caused by the oscillatory current and the external correlation caused by the input signal. With small input signal, the rate code performs better than the temporal correlation code. Our results provide insights into the effects of network dynamics on neuronal computations.

Spatial reference frames of visual, vestibular, and multimodal heading signals in the dorsal subdivision of the medial superior temporal area.

Heading perception is a complex task that generally requires the integration of visual and vestibular cues. This sensory integration is complicated by the fact that these two modalities encode motion in distinct spatial reference frames (visual, eye-centered; vestibular, head-centered). Visual and vestibular heading signals converge in the primate dorsal subdivision of the medial superior temporal area (MSTd), a region thought to contribute to heading perception, but the reference frames of these signals remain unknown. We measured the heading tuning of MSTd neurons by presenting optic flow (visual condition), inertial motion (vestibular condition), or a congruent combination of both cues (combined condition). Static eye position was varied from trial to trial to determine the reference frame of tuning (eye-centered, head-centered, or intermediate). We found that tuning for optic flow was predominantly eye-centered, whereas tuning for inertial motion was intermediate but closer to head-centered. Reference frames in the two unimodal conditions were rarely matched in single neurons and uncorrelated across the population. Notably, reference frames in the combined condition varied as a function of the relative strength and spatial congruency of visual and vestibular tuning. This represents the first investigation of spatial reference frames in a naturalistic, multimodal condition in which cues may be integrated to improve perceptual performance. Our results compare favorably with the predictions of a recent neural network model that uses a recurrent architecture to perform optimal cue integration, suggesting that the brain could use a similar computational strategy to integrate sensory signals expressed in distinct frames of reference.

Transmission of neural activity in a feedforward network.

In this work, the enhancement of coherence resonance of firings in a 10-layer feedforward neuronal network with sparse couplings is found when there is noise input to each layer. Periodic signals with frequency 30-80 Hz are found to be well transmitted though the network, and such a frequency sensitivity can be modulated by the noise intensity and is different in different layers. When a random pulse-like signal is input to the neurons of the first layer, the signal can be well read out from the population rates in an optimal range of noise intensity. This ability decreases as the layer index increases.

Impact of spatially correlated noise on neuronal firing.

We explore the impact of spatially correlated noise on neuronal firing when uncoupled Hodgkin-Huxley model neurons are subjected to a common subthreshold signal. Noise can play a positive role in optimizing neuronal behavior. Although the output signal-to-noise ratio decreases with enhanced noise correlation, both the degree of synchronization among neurons and the spike timing precision are improved. This suggests that there can exist precisely synchronized firings in the presence of correlated noise and that the nervous system can exploit temporal patterns of neural activity to convey more information than just using rate codes. The mechanisms underlying these noise-induced effects are also discussed in detail.

Rate-synchrony relationship between input and output of spike trains in neuronal networks.

Neuronal networks interact via spike trains. How the spike trains are transformed by neuronal networks is critical for understanding the underlying mechanism of information processing in the nervous system. Both the rate and synchrony of the spikes can affect the transmission, while the relationship between them has not been fully understood. Here we investigate the mapping between input and output spike trains of a neuronal network in terms of firing rate and synchrony. With large enough input rate, the working mode of the neurons is gradually changed from temporal integrators into coincidence detectors when the synchrony degree of input spike trains increases. Since the membrane potentials of the neurons can be depolarized to near the firing threshold by uncorrelated input spikes, small input synchrony can cause great output synchrony. On the other hand, the synchrony in the output may be reduced when the input rate is too small. The case of the feedforward network can be regarded as iterative process of such an input-output relationship. The activity in deep layers of the feedforward network is in an all-or-none manner depending on the input rate and synchrony.

Propagation of firing rate in a feed-forward neuronal network.

Propagation of the firing rate and synchronous firings in a 10-layer feed-forward neuronal network are studied. When neurons in layer 1 are subject to white noise, synchrony can be built up in deep layers and the firing rate can be propagated. A network with 6 layers is found to be enough for such behavior. A periodic signal with frequencies of 30-80 Hz can be selectively transmitted through the network. These abilities in information processing due to synchrony can be modulated by noise and the operating mode of neurons, and our results are relevant to experimental findings.