Recent advances in the experimental and theoretical study of dynamics of neuronal electrical firing activities are reviewed. Firstly, some experimental phenomena of neuronal irregular firing patterns, especially chaotic and stochastic firing patterns, are presented, and practical nonlinear time analysis methods are introduced to distinguish deterministic and stochastic mechanism in time series. Secondly, the dynamics of electrical firing activities in a single neuron is concerned, namely, fast-slow dynamics analysis for classification and mechanism of various bursting patterns, one-or two-parameter bifurcation analysis for transitions of firing patterns, and stochastic dynamics of firing activities (stochastic and coherence resonances, integer multiple and other firing patterns induced by noise, etc.).types of synchronization of coupled neurons with electrical and chemical synapses are discussed. As noise and time delay are inevitable in nervous systems, it is found that noise and time delay may induce or enhance synchronization and change firing patterns of coupled neurons. Noise-induced resonance and spatiotemporal patterns in coupled neuronal networks are also demonstrated. Finally, some prospects are presented for future research. In consequence, the idea and methods of nonlinear dynamics are of great significance in exploration of dynamic processes and physiological functions of nervous systems.

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Gaussian white noise (GWN), as an intrinsic noise source, can give rise to various firing activities at the rest state before a supercritical or subcritical Hopf bifurcation (supH or subH) in the Chay system without or with external current input, when V K , V C , λ n and I are considered as changeable control parameters. These firing activities are closely related to the global bifurcation mechanism of the whole system and the fast/slow dynamical subsystems, and can be tackled by means of bifurcation analysis.GWN can induce some typical bursting phenomena in the stochastic Chay system. Firstly, integer multiple "fold/homoclinic" or "circle/homoclinic" bursting due to GWN, with only one spike per burst, can arise from rest states before both subH and supH (with respect to the parameter V K ), and their respective trajectories have the same shape and property. However, less spikes appear and their peaks are lower before supH, comparing with those before subH. Secondly, a "fold/fold" point-point hysteresis loop bursting due to GWN is generated before supH (with respect to the parameter V C ) on the upper branch of a "Z"-shaped bifurcation curve between two fold bifurcations of the fast system. Thirdly, at a rest state before subH (with respect to the additional current I) situated on the lower branch of a "S"-shaped bifurcation curve between two fold bifurcations of the fast system, a GWN-induced firing pattern appears and is classified as "Hopf/homoclinic" bursting via "fold/homoclinic" point-point hysteresis loop.GWN-induced firing activities other than bursting can also be observed in the stochastic Chay system. For example, sometimes GWN-induced continuous spiking without any particular shape may arise at a rest state before supH (with respect to the parameter V K ) for certain values of parameters. Moreover, under the situation that a stable node and a stable focus coexist before subH (with respect to the parameter I) and the attractive region of the stable node is larger than that of the stable focus, GWN only provoke random subthreshold impulsing oscillation near the stable node.

Long-term potentiation (LTP) is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF) signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.

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Based on actual neuronal firing activities, bursting in the Chay neuronal model is considered, in which V K , reversal potentials for K + , V C , reversal potentials for Ca 2+ , time kinetic constant O n and an additional depolarized current I are considered as dynamical parameters. According to the number of the Hopf bifurcation points on the upper branch of the bifurcation curve of fast subsystem, which is associated with the stable limit cycle corresponding to spiking states, different types of bursting and their respective dynamical behavior are surveyed by means of fast-slow dynamical bifurcation analysis.neuronal model, bursting, fast-slow dynamics, bifurcation The biological nervous system is a very complex information network composed of innumerable coupling neurons, and it can encode, transfer and integrate information by firing activities. The firing activities of nervous system are mainly embodied in generation and processing of action potential pulses by neurons, and thus neural information coding is reflected by time rhythms and oscillating patterns of impulse firing sequences. Neuronal firing [1 6] can exhibit abundant nonlinear dynamical behavior since it is involved in a complex physical and chemical process and is affected by a lot of interior and exterior factors (neuronal intracelluar and extracellular ion concentration, activities of ion channels, intrinsic and extrinsic noise and depolarizing current, and so on).As a primary neuronal firing pattern, bursting exhibits a transition between a rest state and a spiking state, owing to a slow variation process modulating fast firing activities. Moreover, its dynamical behavior and classification have been investigated in many neural firing experiments [3 6] and theoretical studies [7 26] .Rinzel [7] firstly made some theoretical analyses on bursting and recognized the transition between the rest state and the spiking state induced by the modulation of slow variables on fast variables. The history of formal classification of bursting started from Rinzel [8] , where