Parkinson's disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement.

Parkinson's disease (PD) is a neurodegenerative disease associated with progressive and inexorable loss of dopaminergic cells in Substantia Nigra pars compacta (SNc). Although many mechanisms have been suggested, a decisive root cause of this cell loss is unknown. A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress' variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model.

Three-dimensional (3D) spatial cells in the mammalian hippocampal formation are believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here we present a hierarchical network model for the formation of 3D spatial cells using anti-Hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border, and grid cells, as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher-dimensional cognitive maps.

Adaptive learning dynamics of the RaThe question of scale naturally arises in clusterdial Basis Function Network (RBFN) are com-ing. At a fine scale each data point can be viewed pared with a scale-based clustering technique as a cluster and at a coarse scale the entire d a h [Won931 and a relationship between the two set can be seen as a single cluster. Scale has not' is pointed out. Using this link, it is shown been given sufficient attention in cont,est of clust,erhow scale-based clustering can be done using ing even though the notion of scale is long familiar the RBFN, with the Radial Basis Function in various fields, and is fundamental to two new top-(RBF) width as the scale parameter. The ics in mathematics, viz. fractals and wavelet theory technique suggests the "right" scale at which [RV91]. the given data set must be clustered and obScale-based clustering has been studied hy several viates the need for knowing the number of authors in recent years ([RGFSO]; [Won93]). Rose clusters beforehand. We show how this method et al. have approa.ched the clustering problem froni solves the problem of determining the number of RBF units and the widths required to get a good network solution.

Studies on the effects of external pacing of heart suggest that the organ, like the nervous system, possesses the properties of 'memory' and adaptation. Changes induced in cardiac activation patterns persist long after the agent that induced those changes itself is removed. After the effects of stimulation have disappeared, response to the stimulus applied for a second time is much greater than the earlier response. Motivated by such results, this paper further explores the possibility of a 'cardiac memory'. In particular, we point out that communication via gap junctions in cardiac tissue is similar to synaptic conductance in nervous tissue and demonstrate, with the aid of a mathematical model, that cardiac tissue can exhibit memory-like behavior if gap-junctional conductances are allowed to adapt according to a Hebbian-like mechanism.