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Objective: To compare the outcomes of excision of pseudo aneurysm along with ligation and local debridement with the outcomes of excision of pseudo aneurysm along with ligation and revascularization in the management of infected femoral pseudo aneurysm among drug addicts. Study Design: Retrospective Analytic study. Setting: Surgical Unit-1, Benazir Bhutto Hospital, Rawalpindi. Period: July 2010 to July 2019. Material & Method: Among patients presented with pseudo aneurysm in Surgical Emergency and out patients department. Data was collected through medical record of the hospital. P-value less than 0.05 was set statistically significant. Results: Mean age and duration of addiction for study population were 36.50 years with SD of ±6 years and 2.80 years with SD of ±0.90 years respectively. All enrolled participants were males (100%). Heroine (76%) was the most common used by addicts while other drugs included benzodiazepine (12%), amphetamine (8%), and cocaine (4%). Left sided femoral artery (70%) was more involved in pseudo aneurysm as compare to right sided femoral artery (30%). Post operative adverse events percentage for ligation without revascularization was 22.72%, whereas, it was 100% for ligation with revascularization. Mortality was 0% for excision with ligation and local debridement while it was 50% for excision with ligation and revascularization. Conclusion: For the optimal management of infected femoral pseudo aneurysm, excision with ligation and local debridement is effective in comparison to excision with ligation and revascularization among addicts.

Objective: To determine the effect of IV Magnesium sulphate (MgSO4) in attenuating the hemodynamic effects of pneumoperitoneum. Methodology: Our single blind randomized control trial (based on computer generated randomize allocation) was carried out at Holy Family Hospital and included 60 patients, not allergic to MgSO4 and belonging to the American Society of Anesthesiology (class 1 and 2. Patients were divided into two groups with Intervention group (M group) receiving 30 mg/kg of MgSO4 and the control group (C group) receiving a placebo. Hemodynamic parameters were then measured at different points of time during surgery. Data was collected and analyzed by using statistical package for the social sciences (SPSS) version 25.0. Results: Out of 60 patients selected 86.7% were females while 13.3% were males. Mean age was 35.78(SD=8.26). As compared to group, patients belonging to group had significantly lower heart rate, systolic and diastolic blood pressure at 15 minutes, after release of pneumoperitoneum and at extubation with p values less than 0.05. Conclusion: Our study concludes that 30 mg/kg of MgSO4 given as a bolus just before creation of pneumoperitoneum protect against its possible adverse hemodynamic effects.

Background: Forensic odontology has a advance role in identifying a deceased person using dental records. Because of the growing trend of litigation around the world, precise recording of clinical dental operations has become increasingly necessary. Objective: To see the endodontic obturation radiographs have a hidden function in identification. Study Design: Cross-sectional study. Place and Duration of Study: Dental OPD, Central Park, Lahore and Private Clinics of Lahore from 1st March 2022 to 31st August 2022. Methods: From the digital X-ray system's data bank, 30 periapical radiographs of patients who had endodontic treatment of the mandibular left (first molars with three canals) were randomly selected. The ante-mortem data "Set 1" consisted of post-operative radiographs. From the thirty radiographs, ten were reprinted, labelled (A-J), and deemed post-mortem data "Set 2."This "Set 2" post-mortem group of 10 radiographs would be compared to the "Set 1" ante-mortem group of 30 radiographs. Ten dentally educated persons would examine these two sets of radiographs. Each examiner was given thirty radiographs from "Set 1" and ten radiographs from "Set 2," with the task of matching the individual post-mortem radiographs ("Set 2") with the ante-mortem radiographs ("Set 1"). Results: Thirty four examiners had a 100% success rate, 4 examiners had a 97.5% success rate (1 mismatch), and 2 examiners had a 95% success rate (2 mismatches). Conclusion: On the off chance that after death radiographs are precise duplicates of endodontic obturated radiographs, the obturation's novel shape can be taken advantage of for forensic ID. Keywords: Forensic odontology, Endodontic obturated radiographs

Biological learning operates at multiple interlocking timescales, from long evolutionary stretches down to the relatively short time span of an individual9s life. While each process has been simulated individually as a basic learning algorithm in the context of spiking neuronal networks (SNNs), the integration of the two has remained limited. In this study, we first train SNNs separately using individual model learning using spike-timing dependent reinforcement learning (STDP-RL) and evolutionary (EVOL) learning algorithms to solve the CartPole reinforcement learning (RL) control problem. We then develop an interleaved algorithm inspired by biological evolution that combines the EVOL and STDP-RL learning in sequence. We use the NEURON simulator with NetPyNE to create an SNN interfaced with the CartPole environment from OpenAI9s Gym. In CartPole, the goal is to balance a vertical pole by moving left/right on a 1-D plane. Our SNN contains multiple populations of neurons organized in three layers: sensory layer, association/hidden layer, and motor layer, where neurons are connected by excitatory (AMPA/NMDA) and inhibitory (GABA) synapses. Association and motor layers contain one excitatory (E) population and two inhibitory (I) populations with different synaptic time constants. Each neuron is an event-based integrate-and-fire model with plastic connections between excitatory neurons. In our SNN, the environment activates sensory neurons tuned to specific features of the game state. We split the motor population into subsets representing each movement choice. The subset with more spiking over an interval determines the action. During STDP-RL, we supply intermediary evaluations (reward/punishment) of each action by judging the effectiveness of a move (e.g., moving the CartPole to a balanced position). During EVOL, updates consist of adding together many random perturbations of the connection weights. Each set of random perturbations is weighted by the total episodic reward it achieves when applied independently. We evaluate the performance of each algorithm after training and through the creation of sensory/motor action maps that delineate the network9s transformation of sensory inputs into higher-order representations and eventual motor decisions. Both EVOL and STDP-RL training produce SNNs capable of moving the cart left and right and keeping the pole vertical. Compared to the STDP-RL and EVOL algorithms operating on their own, our interleaved training paradigm produced enhanced robustness in performance, with different strategies revealed through analysis of the sensory/motor mappings. Analysis of synaptic weight matrices also shows distributed vs clustered representations after the EVOL and STDP-RL algorithms, respectively. These weight differences also manifest as diffuse vs synchronized firing patterns. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our models offer novel predictions on the biological mechanisms supporting learning behaviors. Author SummaryA longstanding challenge in neuroscience is to understand how animals produce intelligent behaviors and how pathology results in behavioral/cognitive deficits. The advent of modern imaging techniques has enabled recording large populations of neurons in behaving animals. However, animal experiments still impose limitations in recording widely across multiple brain areas while manipulating the individual components of the circuit, thus limiting our understanding of how the behavior emerges from sensory and motor interactions. Multiscale data-driven models of neural circuits can help dissect mechanisms of sensory-motor behaviors. However, most biologically detailed models are used to reproduce and understand the origins of activity patterns observed in vivo. In contrast, Deep Learning models show extraordinary performance in complex sensory-motor tasks. Despite this, Deep Learning models are not routinely used to dissect mechanisms of sensory-motor behavior because of their lack of biological detail. Here, we developed several spiking neuronal network models of the visual-motor system and trained them using biologically inspired learning mechanisms to play a racket-ball game. We use the models to dissect circuit architectures and learning rules that enhance performance. We offer our models and analyses for the neuroscience community to better understand neuronal circuit mechanisms contributing to learning and behavior.

Electrophysiological oscillations in the brain have been shown to occur as multicycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We developed an open-source wavelet-based tool to detect and characterize such oscillation events (OEvents) and exemplify the use of this tool in both simulations and two invasively-recorded electrophysiology datasets: one from human, and one from nonhuman primate (NHP) auditory system. After removing incidentally occurring event-related potentials (ERPs), we used OEvents to quantify oscillation features. We identified ∼2 million oscillation events, classified within traditional frequency bands: δ, θ, α, β, low γ, γ, and high γ. Oscillation events of 1–44 cycles could be identified in at least one frequency band 90% of the time in human and NHP recordings. Individual oscillation events were characterized by nonconstant frequency and amplitude. This result necessarily contrasts with prior studies which assumed frequency constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intraevent rhythmicity, there was also evidence of interevent rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano factor (FF) measures differed significantly from a Poisson distribution assumption. Overall, our study provides an easy-to-use tool to study oscillation events at the single-trial level or in ongoing recordings, and demonstrates that rhythmic, multicycle oscillation events dominate auditory cortical dynamics.

The levels of voltage-gated and synaptic currents in the same neuron type can vary substantially across individuals. Yet, the phase relationships between neurons in oscillatory circuits are often maintained, even in the face of varying oscillation frequencies. We examined whether synaptic and intrinsic currents are matched to maintain constant activity phases across preparations, using the lateral pyloric (LP) neuron of the stomatogastric ganglion of the crab, Cancer borealis. LP produces stable oscillatory bursts upon release from inhibition, with an onset phase that is independent of oscillation frequency. We quantified the parameters that define the shape of the synaptic current inputs across preparations and found no linear correlations with voltage-gated currents. However, several synaptic parameters were correlated with oscillation period and burst onset phase, suggesting they may play a role in phase maintenance. We used the dynamic clamp to apply artificial synaptic inputs and found that those synaptic parameters correlated with phase and period were ineffective in influencing burst onset. Instead, parameters that showed the least variability across preparations had the greatest influence. Thus, parameters that influence circuit phasing are constrained across individuals, while those that have little effect simply co-vary with phase and frequency.

Electrophysiological oscillations in the brain have been shown to occur as multi-cycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We used two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing incidentally occuring event related potentials, we used a wavelet transform based method to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, low gamma, gamma, high gamma. Oscillation events of 1-44 cycles were present in at least one frequency band in 90% of the time in human and non-human primate recordings. Individual oscillation events were characterized by non-constant frequency and amplitude. This result naturally contrasts with prior studies which assumed such constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intra-event rhythmicity, there was also evidence of inter-event rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano Factor measures differed significantly from a Poisson distribution assumption. Overall, our study demonstrates that rhythmic, multi-cycle oscillation events dominate auditory cortical dynamics.

In oscillatory systems, neuronal activity phase is often independent of network frequency. Such phase maintenance requires adjustment of synaptic input with network frequency, a relationship that we explored using the crab,Cancer borealis, pyloric network. The burst phase of pyloric neurons is relatively constant despite a >2-fold variation in network frequency. We used noise input to characterize how input shape influences burst delay of a pyloric neuron, and then used dynamic clamp to examine how burst phase depends on the period, amplitude, duration, and shape of rhythmic synaptic input. Phase constancy across a range of periods required a proportional increase of synaptic duration with period. However, phase maintenance was also promoted by an increase of amplitude and peak phase of synaptic input with period. Mathematical analysis shows how short-term synaptic plasticity can coordinately change amplitude and peak phase to maximize the range of periods over which phase constancy is achieved.

Many signaling pathways crucial for homeostatic regulation, synaptic plasticity, apoptosis and immune response depend on Ca2+. Ca2+ dysregulation disrupts normal function of neurons and neuronal networks. This causes severe motor and cognitive disabilities. Understanding how Ca2+ dysregulation triggers disease onset and progression, and affects downstream processes, can help identify targets for treatments. Because of intermingling of molecular pathways, dissecting the role of individual mechanisms and establishing causality is very challenging. Computational models provide a way to decipher these processes. I review some computational models with Ca2+ dynamics to illustrate their predictive power, and note where extending those models to capture multiscale interaction of Ca2+ dependent molecular pathways can be useful for therapeutic and drug discovery purposes.

Nanotechnology refers to the engineering and exploitation of materials whose size ranges from 1-100 nm. There are several types of nanoparticles platforms which are differentiated based on their size, shape, composition and their functionalities. These platforms include liposomes, Albumin-bound, Polymeric, Quantum Dots and Iron Oxide. Biological method for the synthesis of nanoparticles is easy, cost-effective and environmental friendly. Biological systems have the capabilities of converting metal ions into metal nanoparticles in a very friendly manner. Nanoparticles can be used in Fluorescent biological labels, drug and gene delivery, tissue engineering etc. Furthermore, nanoparticles have vast applications in daily life.

Bursts of dendritic calcium spikes play an important role in excitability and synaptic plasticity in many types of neurons. In single Purkinje cells, spontaneous and synaptically evoked dendritic calcium bursts come in a variety of shapes with a variable number of spikes. The mechanisms causing this variability have never been investigated thoroughly. In this study, a detailed computational model using novel simulation routines is applied to identify the roles that stochastic ion channels, spatial arrangements of ion channels, and stochastic intracellular calcium have toward producing calcium burst variability. Consistent with experimental recordings from rats, strong variability in the burst shape is observed in simulations. This variability persists in large model sizes in contrast to models containing only voltage-gated channels, where variability reduces quickly with increase of system size. Phase plane analysis of Hodgkin-Huxley spikes and of calcium bursts identifies fluctuation in phase space around probabilistic phase boundaries as the mechanism determining the dependence of variability on model size. Stochastic calcium dynamics are the main cause of calcium burst fluctuations, specifically the calcium activation of mslo/BK-type and SK2 channels. Local variability of calcium concentration has a significant effect at larger model sizes. Simulations of both spontaneous and synaptically evoked calcium bursts in a reconstructed dendrite show, in addition, strong spatial and temporal variability of voltage and calcium, depending on morphological properties of the dendrite. Our findings suggest that stochastic intracellular calcium mechanisms play a crucial role in dendritic calcium spike generation and are therefore an essential consideration in studies of neuronal excitability and plasticity.

Almost all synapses show activity-dependent dynamic changes in efficacy. Numerous studies have explored the mechanisms underlying different forms of short-term synaptic plasticity (STP), but the functional role of STP for circuit output and animal behavior is less understood. This is particularly true for inhibitory synapses that can play widely varied roles in circuit activity. We review recent findings on the role of synaptic STP in sensory, pattern generating, thalamocortical, and hippocampal networks, with a focus on synaptic inhibition. These studies show a variety of functions including sensory adaptation and gating, dynamic gain control and rhythm generation. Because experimental manipulations of STP are difficult and nonspecific, a clear demonstration of STP function often requires a combination of experimental and computational techniques.

There is growing interest in understanding calcium dynamics in dendrites, both experimentally and computationally. Many processes influence these dynamics, but in dendrites there is a strong contribution of morphology because the peak calcium levels are strongly determined by the surface to volume ratio (SVR) of each branch, which is inversely related to branch diameter. In this study we explore the predicted variance of dendritic calcium concentrations due to local changes in dendrite diameter and how this is affected by the modeling approach used. We investigate this in a model of dendritic calcium spiking in different reconstructions of cerebellar Purkinje cells and in morphological analysis of neocortical and hippocampal pyramidal neurons. We report that many published models neglect diameter-dependent effects on calcium concentration and show how to implement this correctly in the NEURON simulator, both for phenomenological pool based models and for implementations using radial 1D diffusion. More detailed modeling requires simulation of 3D diffusion and we demonstrate that this does not dissipate the local concentration variance due to changes of dendritic diameter. In many cases 1D diffusion of models of calcium buffering give a good approximation provided an increased morphological resolution is implemented.

This chapter 9 analyzes an algorithm for repairing computer-based neuronal reconstructions. It deals with a new probability density cloud approach for characterizing and repairing neuron dendritic and axonal morphologies. The chapter uses a layer 2 pyramidal cell to validate the dendritic repair process. In the repair algorithm, it assumes that the dendrites of neurons have a statistical symmetry in their morphologies, although they do not necessarily seem geometrically symmetric. The chapter also shows that the algorithm depends on the quality as well as the quantity of the population of cells.... This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications.

Previous studies have suggested that the activity pattern of a cerebellar Purkinje cells (PC) is significantly controlled by voltage activated Ca2+ channels and Ca2+ activated K+ channels, present mainly on its elaborate dendritic tree [1]. Although the main somatic excitatory drive propagates very weakly into the dendritic tree [2], somehow a significant interaction between somatic and dendritic spiking occurs. Ca2+ entering through P-type channel is thought to be the main source of this excitability modulation, and this Ca2+ influx also activates large conductance (BK) and small conductance (SK) Ca2+ dependent K+ channels [1,3]. This interaction often results in the counter-intuitive computational somatic and dendritic spiking behavior of PCs [4], but nevertheless this important aspect has not been thoroughly investigated in previous computational modeling studies.

This paper presents a method for removing electroocular (EOG) artifacts in the electroencephalogram (EEG). The procedure is based on blind source separation (BSS) and, in contrast to methods already available in the literature, it is completely automated and does not require the availability of peri-ocular EOG electrodes. The proposed approach removed most EOG artifacts in 6 longterm EEG recordings containing epilectic seizures without out distorting the recorded ictal activity.

Intracellular Ca2+ concentrations play a crucial role in the physiological interaction between Ca2+ channels and Ca2+-activated K+ channels. The commonly used model, a Ca2+ pool with a short relaxation time, fails to simulate interactions occurring at multiple time scales. On the other hand, detailed computational models including various Ca2+ buffers and pumps can result in large computational cost due to radial diffusion in large compartments, which may be undesirable when simulating morphologically detailed Purkinje cell models. We present a method using a compensating mechanism to replace radial diffusion and compared the dynamics of different Ca2+ buffering models during generation of a dendritic Ca2+ spike in a single compartment model of a PC dendritic segment with Ca2+ channels of P- and T-type and Ca2+-activated K+ channels of BK- and SK-type. The Ca2+ dynamics models used are (1) a single Ca2+ pool; (2) two Ca2+ pools, respectively, for the fast and slow transients; (3) detailed Ca2+ dynamics with buffers, pump, and diffusion; and (4) detailed Ca2+ dynamics with buffers, pump, and diffusion compensation. Our results show that detailed Ca2+ dynamics models have significantly better control over Ca2+-activated K+ channels and lead to physiologically more realistic simulations of Ca2+ spikes and bursting. Furthermore, the compensating mechanism largely eliminates the effect of removing diffusion from the model on Ca2+ dynamics over multiple time scales. Electronic supplementary material The online version of this article (doi:10.1007/s12311-010-0224-3) contains supplementary material, which is available to authorized users.

Ca mechanisms, present mainly on the dendritic tree of cerebellar Purkinje cells (PC) [1], significantly influence its activity pattern [2,3], synaptic integration [4], etc. Particularly, the intracellular dynamics controlling Caconcentrations can play a crucial role in the physiological interaction between the Ca channels and Ca-activated K (KCa) channels [5]. The simplest, but commonly used model, the Ca pool with a short relaxation time, will fail to simulate interactions occurring at multiple time scales. On the other hand, detailed computational models including various Ca buffers and pumps [6] can result in large computational cost due to radial diffusion in large compartments, which may need to be avoided when simulating morphologically detailed PC models. We present a method using compensating mechanisms to replace radial diffusion and compared the dynamics of different Ca buffering models during generation of dendritic Ca spikes during somatic bursting or depolarization [1]. As for the membrane mechanisms, we used a recently constructed single compartment model of a PC dendritic segment with the Ca channels of Pand T-type and KCa channels of BKand SK-type, which can generate the Ca spikes comparable to the experimental recordings [7]. The Ca dynamics models are (i) a single Ca pool, (ii) two Ca pools respectively for the fast and slow transients, (iii) detailed Ca dynamics with calbindin, parvalbumin, pump and diffusion, and (iv) detailed Ca dynamics with calbindin, parvalbumin, pump and diffusion compensation [6]. The simulated membrane voltage was compared with electrophysiological data. Our results show that detailed Ca dynamics models with buffers, pumps, and diffusion have significantly better control over Ca activated K channels and lead to physiologically more realistic simulations of Ca spikes. Furthermore, the effect on Ca dynamics of removing diffusion from the model can largely be eliminated by the compensating mechanisms. Therefore, physiologically realistic Ca concentration dynamics can be simulated at reasonable computational cost.

Neuronal activity is largely influenced by voltage-dependent and calcium-dependent ion channels, and their interaction with calcium related mechanisms present in and around the complex cellular morphology. In the past, people have focused on stochasticity of voltage-gated ion channels to study its effect on neuronal excitability [1-4] but have ignored the intracellular aspects, in particular calcium dynamics. It is our aim to clarify the role of stochasticity of intracellular calcium dynamics in modulating neuronal output. Intracellular calcium dynamics in neuronal systems significantly control their firing pattern, such as a calcium spike. A calcium spike is generated by interaction of voltage gated Ca2+ channels and Ca2+-activated K+ channels, where the interaction is mediated through intracellular calcium mechanisms. A significant proportion of calcium entering through voltage-gated calcium channels binds to buffers, diffuses away and is extruded. Only a limited amount of calcium binds to Ca2+-activated K+ channels to conform it to conducting states. These complex interactions take place in and around complex cellular morphology, where stochastic interaction between diffusing molecules and surface bound molecules, stochastic transitions between ion channel conformations and variability in molecular arrangement may have a significant effect on neuronal excitability. We studied the stochastic behavior of dendritic calcium spikes in Purkinje neurons. In our study, we used a model for dendritic calcium spikes, which included P- and T- type Ca2+ channels, BK- and SK- type Ca2+-activated K+ channels, parvalbumin and calbindin as calcium buffers, pumps, diffusion of Ca2+ molecules, diffusion of free buffers and diffusion of Ca2+ bound buffers. Details about the ion channel kinetics, buffer kinetics and diffusion rate constants used in this study can be found in Anwar et al. 2010 [5] .We ran all simulations in STEPS [6], which supports stochastic and deterministic molecular simulations alongside accurate computation of the electrical behavior of the cellular region, all within complex 3D morphologies. Therefore we could run a series of stochastic, deterministic and hybrid simulations, investigate the different sources of noise individually and together, and compare to deterministic solutions. By comparing the sources of noise in this system in absolute and relative terms at different lengths of dendritic section we demonstrate the significance of the different contributing factors to stochasticity in the system, including calcium dynamics and ion channel gating, on a variety of spatial scales.