Cyclic AMP (cAMP) and its main effector Protein Kinase A (PKA) are critical for several aspects of neuronal function including synaptic plasticity. Specificity of synaptic plasticity requires that cAMP activates PKA in a highly localized manner despite the speed with which cAMP diffuses. Two mechanisms have been proposed to produce localized elevations in cAMP, known as microdomains: impeded diffusion, and high phosphodiesterase (PDE) activity. This paper investigates the mechanism of localized cAMP signaling using a computational model of the biochemical network in the HEK293 cell, which is a subset of pathways involved in PKA-dependent synaptic plasticity. This biochemical network includes cAMP production, PKA activation, and cAMP degradation by PDE activity. The model is implemented in NeuroRD: novel, computationally efficient, stochastic reaction-diffusion software, and is constrained by intracellular cAMP dynamics that were determined experimentally by real-time imaging using an Epac-based FRET sensor (H30). The model reproduces the high concentration cAMP microdomain in the submembrane region, distinct from the lower concentration of cAMP in the cytosol. Simulations further demonstrate that generation of the cAMP microdomain requires a pool of PDE4D anchored in the cytosol and also requires PKA-mediated phosphorylation of PDE4D which increases its activity. The microdomain does not require impeded diffusion of cAMP, confirming that barriers are not required for microdomains. The simulations reported here further demonstrate the utility of the new stochastic reaction-diffusion algorithm for exploring signaling pathways in spatially complex structures such as neurons.

Calcium plays a role in long term plasticity by triggering post-synaptic signaling pathways for both the strengthening (LTP) and weakening (LTD) of synapses. Since these are opposing processes, several hypotheses have been developed to explain how calcium can trigger LTP in some situations and LTD in others. These hypotheses fall broadly into three categories, based on the amplitude of calcium concentration, the duration of the calcium elevation, and the location of the calcium influx. Here we review the experimental evidence for and against each of these hypotheses and the recent computational models utilizing each. We argue that with new experimental techniques for the precise visualization of calcium and new computational techniques for the modeling of calcium diffusion, it is time to take a new look at the location hypothesis.

Gustafson, Nicholas, Elakkat Gireesh-Dharmaraj, Uwe Czubayko, Kim T. Blackwell, and Dietmar Plenz. A comparative voltage and current-clamp analysis of feedback and feedforward synaptic transmission in the striatal microcircuit in vitro. J Neurophysiol 95: 737-752, 2006. First published October 19, 2005 doi:10.1152/jn.00802.2005. Striatal spiny projection (SP) neurons control movement initiation by integrating cortical inputs and inhibiting basal ganglia outputs. Central to this control lies a "microcircuit" that consists of a feedback pathway formed by axon collaterals between GABAergic SP neurons and a feedforward pathway from fast spiking (FS) GABAergic interneurons to SP neurons. Here, somatically evoked postsynaptic potentials (PSPs) and currents (PSCs) were compared for both pathways with dual whole cell patch recording in voltage-and current-clamp mode using cortex-striatum-substantia nigra organotypic cultures. On average, feedforward inputs were 1 ms earlier, more reliable, and about twice as large in amplitude compared with most feedback inputs. On the other hand, both pathways exhibited widely varying, partially overlapping amplitude distributions. This variability was already established for single FS neurons targeting many SP neurons. In response to precisely timed action potential bursts, feedforward and feedback inputs consistently showed short-term depression Յ50 -70% in voltage-clamp, although feedback inputs also displayed strong augmentation in current-clamp in line with previous reports. The augmentation of feedback inputs was absent in gramicidin D perforated-patch recording, which also showed the natural reversal potential for both inputs to be near firing threshold. Preceding depolarizing feedback inputs during the down state did not consistently change subsequent postsynaptic action potentials. We conclude that feedback and feedforward inputs have their dominant effect during the up-state. The reversal potential close to the up-state potential, which supports shunting operation with millisecond precision and the strong synaptic depression, should enable both pathways to carry time-critical information.

Up states are prolonged membrane potential depolarizations critical for synaptic integration and action potential generation in cortical and striatal neurons. They commonly result from numerous concurrent synaptic inputs, whereas neurons reside in a down state when synaptic inputs are few. By quantifying the composition, frequency, and amplitude of synaptic inputs for both states, we provide important constraints for state transitions in striatal network dynamics. Up and down states occur naturally in cortex-striatum-substantia nigra cocultures, which were used as an in vitro model in the present study. Spontaneous synaptic inputs during down states were extracted automatically in spiny projection neurons and fast spiking interneurons of the striatum using a newly developed computer algorithm. Consistent with a heterogeneous population of synaptic inputs, PSPs and PSCs showed no correlation in amplitude and rise time and occurred at relatively low frequencies of 10-40 Hz during the down state. The number of synaptic inputs during up states, estimated from the up-state charge and the unitary charge of down-state PSCs, was 217 +/- 44. Given the average up-state duration of 284 +/- 34 msec, synaptic input frequency was approximately 800 Hz during up-states for both neuronal types. Many down-state events reversed at the chloride reversal potential and were blocked by GABA(A) antagonists. The high correlation between up- and down-state reversal potential suggests that despite these drastic changes in synaptic input frequency, the ratio of inhibitory to excitatory currents is similar during both states.

Background Microelectrode recordings along pre-planned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson’s disease. Usually, the demarcation of the STN borders is detected manually by a neurophysiologist. The exact detection of the borders is difficult and especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to sub-optimal location of the DBS lead and inadequate clinical outcomes. Methods We present machine learning classification procedures that utilize microelectrode recordings power spectra and allow for real time, high accuracy discrimination between STN and substantia nigra pars reticulata. Results A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10-fold cross validation). We used the same dataset as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recordings features and trajectory history to enable a real-time classification of the ventral STN border (STN exit). Seventy-three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%. Conclusion The results indicate that robust, accurate and automatic real-time electrophysiological detection of the ventral STN border is feasible.

In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb’s postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected to depend on the spatiotemporal dynamics of the underlying signaling pathways. However in most cases, due to technical limitations experiments grant only indirect access to these pathways. Computational models carefully constrained by experiments, allow for a better qualitative understanding of the molecular basis of STDP and its regulation by neuromodulators. Recently, computational models of calcium dynamics and signaling pathway molecules have started to explore STDP emergence in ex and in vivo-like conditions. These models are expected to reproduce better at least part of the complex modulation of STDP as an emergent property of the underlying molecular pathways. Elucidation of the mechanisms underlying STDP modulation and its consequences on network dynamics is of critical importance and will allow better understanding of the major mechanisms of memory storage and recall both in health and disease.

Growing evidence supports a critical role for the dorsal striatum in cognitive as well as motor control. Both lesions and in vivo recordings demonstrate a transition in the engaged dorsal striatal subregion, from dorsomedial to dorsolateral, as skill performance shifts from an attentive phase to a more automatic or habitual phase. What are the neural mechanisms supporting the cognitive and behavioral transitions in skill learning? To pursue this question, we used T-maze training during which rats transition from early, attentive (dorsomedial) to late habitual (dorsolateral) performance. Following early or late training, we performed the first direct comparison of bidirectional synaptic plasticity in striatal brain slices, and the first evaluation of striatal synaptic plasticity by hemisphere relative to a learned turn. Consequently, we find that long-term potentiation and long-term depression are independently modulated with learning rather than reciprocally linked as previously suggested. Our results establish that modulation of evoked synaptic plasticity with learning depends on striatal subregion, training stage, and hemisphere relative to the learned turn direction. Exclusive to the contralateral hemisphere, intrinsic excitability is enhanced in dorsomedial relative to dorsolateral medium spiny neurons early in training and population responses are dampened late in training. Neuronal reconstructions indicate dendritic remodeling after training, which may represent a novel form of pruning. In conclusion, we describe region-and hemisphere-specific changes in striatal synaptic, intrinsic, and morphological plasticity which correspond to T-maze learning stages, and which may play a role in the cognitive transition between attentive and habitual strategies.

The striatum is a major site of learning and memory formation for sensorimotor and cognitive association. One of the mechanisms used by the brain for memory storage is synaptic plasticity – the long lasting, activity-dependent change in synaptic strength. All forms of synaptic plasticity require an elevation in intracellular calcium, and a common hypothesis is that the amplitude and duration of calcium transients can determine the direction of synaptic plasticity. The utility of this hypothesis in the striatum is unclear in part because dopamine is required for striatal plasticity and in part because of the diversity in stimulation protocols. To test whether calcium can predict plasticity direction, we developed a calcium-based plasticity rule using a spiny projection neuron model with sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion. We utilize three spike-timing-dependent plasticity (STDP) induction protocols, in which post-synaptic potentials are paired with precisely timed action potentials and the timing of such pairing determines whether potentiation or depression will occur. Results show that despite the variation in calcium dynamics, a single, calcium-based plasticity rule, which explicitly considers duration of calcium elevations, can explain the direction of synaptic weight change for all three STDP protocols. Additional simulations show that the plasticity rule correctly predicts the NMDA receptor dependence of long-term potentiation and the L-type channel dependence of long term depression. By utilizing realistic calcium dynamics, the model reveals mechanisms controlling synaptic plasticity direction, and shows that the dynamics of calcium, not just calcium amplitude, are crucial for synaptic plasticity.