Research On Signal Transmission And Processing In Complex Neural Systems

The brain is the center of the nervous system in all vertebrate and most invertebrate animals. Transmitting, integrating and processing of neuronal information in the brain are mainly realized through some functional neuronal ensembles. To reveal human higher cognitive processes and thinking, the first thing that we should do is to understand the highly complex and effective neural information transmission and processing mechanisms. As a new research area, computational neuroscience provides us some powerful tools to deal with the questions about this issue. Through modeling and simulations, this PhD thesis studies the effects of unreliable synaptic transmission, network topological structure and noise on information transmission and processing in several typical complex neural systems. The main contents and results are organized as follows:1. We systematically study both the synfire propagation and firing rate propagation in feed-forward neuronal network coupled in an all-to-all fashion. Here we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feed-forward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feed-forward neuronal network models. Finally, we study the signal propagation in feed-forward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.2. We present an in-depth investigation on the stochastic dynamics of a single Hodgkin-Huxley (HH) neuron driven by stochastic excitatory and inhibitory input spikes via unreliable synapses. Based on the mean-filed analysis for the total synaptic current, a novel intrinsic neuronal noise regulation mechanism stemming from the unreliable synapses is proposed. Our simulation results demonstrate that the HH neuron can indeed exploit the unreliable synapses to enrich its dynamic performance. Under certain conditions, we find that the stochastic resonance (SR) phenomenon is able to be induced by the unreliable synaptic transmission. Further numerical simulations confirm our prediction that the performance of SR is mainly determined by three factors: the effective mean spike arrival rate per synapse, the strength of excitatory spike input, as well as the relative strength of excitatory and inhibitory spike inputs. We also study the frequency sensitivity of weak periodic signal detection in the considered neural system. With a proper choice of synaptic parameters, it is observed that the HH neuron can better detect the weak periodic signal within a wide signal frequency range. Our results presented here, to a certain degree, provide insights into the functional roles of unreliable synapses in neural information processing.3. We investigate the effects of noise on information processing and stochastic dynamics in the FFL neuronal network motifs by computational modeling. The simulation results demonstrate that different types of FFL motifs can exploit noise to enrich its dynamic performance. With a proper choice of noise intensities, both the SR and CR can be exhibited in many types of the FFL motifs. This result indicates that suitable noise can enhance the neural information transmission and processing in FFL motifs. On the other hand, our results also indicate that the coupling strength serves as a control parameter, which has great impacts on the stochastic dynamics of the FFL motifs.4. The self-sustained irregular firing activity in 2-D small-world (SW) neural networks consisting of both excitatory and inhibitory neurons is systematically studied. For a proper proportion of unidirectional shortcuts, the stable self-sustained activity with irregular firing states indeed occurs in the considered network. By varying the shortcut density while keeping other system parameters fixed, different levels of irregular firing states are obtained in 2-D SW neural networks. It is also observed that this activity is sensitive to small perturbations, which might provide a possible mechanism for producing chaos. On the other hand, we find that several other system parameters, such as the network size and refractory period, have significant impact on this activity. Further simulation results show that the 2-D SW neural network can sustain such long-lasting firing behavior by using a smaller number of connections than the random neural network.