1,721,007 research outputs found
Stochastic and executable models of synaptic processes
No full textNeurons communicate with each other at synapses. These are plastic
communication units that modulate signal transmission. Synaptic
plasticity, i.e. the possibility of being modified by activity,
contributes to neuro-computational capabilities, underlying phenomena
like memory and learning. The synaptic signal transduction involves
complex intra- and inter-cellular biochemical reactions, which, under
several respects, appear to be most suitably described by stochastic
models.
On these bases, we have developed a stochastic and computationally
executable model of the calyx of Held synapse. The aim of this work is
to provide formal descriptive techniques for the analysis of such a
complex and systemic behaviour. We exploit an interpretation of the
structure of life systems as interacting computational entities from
which the overall behaviour of the system emerges. We have exploited
"process calculi", developed within concurrency theory in computer
science, as representation language. These calculi describe the
interactive behaviour of a system in terms of the behaviour of its
active processes. A distinguished feature is that these
representations have a direct executable interpretation, which allows
the behaviour of a system, like the calyx, to be qualitatively
simulated. Often, these models and their analysis enjoy nice
compositional properties.
Process behaviour is defined starting from basic interaction steps,
which model simple molecular interactions, and compositional
operators, through which we build up the complex behaviour of a
structured system. The semantics of process calculi is generally
expressed by a transition system, with states representing the current
configuration of a system and transition representing its capability
to act and move to one or more future states. Stochastic semantics has
been developed in order to study system performances when relevant
events occur according to a given probability distribution. Many of
these semantics are based on the Gillespie’s Stochastic Simulation
Algorithm, originally proposed within chemical reaction modeling. This
has further fostered the use of process calculi for biology and lead
to the definition of new calculi for this purpose.
The approach we followed benefits from conjugating the abstract and
compositional algebraic models, the possibility of precisely describe
their semantics and formally reasoning about them, and the
quantitative analysis provided by stochastic semantics, accounting for
the non-continuous, nor discrete, nature of many phenomena. Building
on available data in literature, fitting some unknown parameters and
developing working hypotheses, we have defined a model which, to our
knowledge, is the first computational model of synaptic activity based
on process calculi. The in-silico simulations performed are coherent
with experimental data in literature and faithfully comprise
short-term synaptic plasticity phenomena, like facilitation,
depression and potentiation. Overall, the simulation results represent
a quite articulate description of the presynaptic and postsynaptic
activity.
Additionally, this multidisciplinary work validates the application of
the technique in life sciences, suggesting possibly improvements, such
as a refined representation of space that overcomes the standard
assumption of spatial uniformity (well-stirred space)
Circular plate capacitor with different discs
No full textIn this paper, we write a system of integral equations
for a capacitor composed of two discs of different
radii, generalizing Love’s equation for equal discs.
We compute the complete asymptotic form of the
capacitance matrix for both large and small distances
obtaining a generalization of Kirchhoff’s formula for
the latter case
Neural circuits underlying feeding in Aplysia
No full textFeeding behavior of Aplysia is a useful model system with which to study the neural control of relatively complex and adaptive behaviors. Key aspects of feeding behavior involve rhythmic movements of structures in the foregut, such as the radula (the toothed grasping surface). The buccal ganglia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. The behavior and the underlying pattern of neural activity (i.e., buccal motor patterns; BMPs) can be divided into two basic phases: the protraction and retraction of the radula. Different behaviors (e.g., ingestion vs rejection) are determined by the timing of other movements, such as closure of the radula. Thus, the CPG generates at least two types of BMPs: one that mediates ingestion (iBMP) and one that mediates rejection (rBMP).
Although many cells and synaptic connections within the CPG have been identified, an analysis of its overall function is still incomplete. The constructionist methods of mathematical modeling can help develop insights to the functions of neural circuits. Previously, we developed a conductance-based model of the individual neurons and their synaptic connections. The simulations were performed with the neurosimulator SNNAP. The model successfully simulated the observed properties of neurons and synaptic connections within the CPG. The model circuit contained neurons B31/32, B34, B35, B63, B4/5, B8, B51, B64, B52 and an unidentified cell (termed Z). The Z cell was necessary to mediate the transition between protraction and retraction and represents an important prediction of the model.
The present study extended our previous model by incorporating a more detailed description of B31/B32 (see Hurwitz et al. 2008; Saada et al. 2009). B31/32 respond after a delay to stimuli and play a key role in the decision to initiate a BMP. The currents underlying the decision making process have been characterized and consist of one inward and three outward currents. The inward current is modulated by muscarinic synaptic transmission. We modeled this current and its modulation and we re-evaluated the mechanisms underlying the genesis of BMPs. The network model produced robust rhythmic activity similar to rBMPs. In addition, the model faithfully reproduced the delay of activity following stimulation. The present study illustrates that a ten-cell network can reproduce some patterns of activity that underlie feeding in Aplysia. However, the model does not represent all of the identified elements of the CPG. For example, the model is currently be extended to include cells B20 and B65, which can initiate BMPs, in part via their actions on B31/B32. Thus, the model provides a quantitative and modifiable framework with which to investigate how additional elements may contribute to the overall function of the CPG. In addition to providing a tool with which to investigate the CPG, the model can be expanded to include higher-order cells (e.g., the command-like neurons) and modulatory processes. The continual expansion and development of the model will provide a useful tool for analyses of the neuronal mechanisms underlying rhythmic behaviors and their plasticity
Protracted withdrawal from alcohol impacts excitability and dynamical properties of neurons of the bed nucleus of stria terminalis
No full textThe juxta-capsular subdivision of bed nucleus of stria terminalis (jcBNST) is part of the extended amygdala and plays an important role in the regulation of stress and reward related behaviors. This compact nucleus contains at least 3 types of biophysically diverse populations of GABAergic neurons that play an important role in the regulation of downstream neural circuits of the central amygdala. While drug-related neuroadaptive changes in the synaptic properties of BNST and other extended amygdala neurons have been well documented, their integrative properties and circuit interactions are also to be studied in order to gain a better understanding of the neurophysiological aspects of addictive behavior. Here we performed a series of dynamic clamp experiments with synthetic presynaptic voltage waveforms that were transformed to simulated excitatory and inhibitory synaptic conductances and injected into jcBNST neurons. This methodological approach allowed us to characterize how the temporal structure of the firing responses and the precision of spike timing depended on the type of neuron and how these properties were modified after prolonged withdrawal in alcohol dependent animals. Additionally, we performed a comprehensive analysis of neuronal responses under standard current clamp stimulation and used up to 12 characteristics of the neurons voltage output to compare their physiological properties, including resting membrane potential, membrane resistance, rehobase, the occurrence and intensity of post inhibitory response spikes, among others. In order to compare this complex set of physiological and dynamical parameters of jcBNST neurons from rats with a history of prolonged withdrawal and controls, we used the fuzzy K-means clustering algorithm, an unsupervised learning technique derived from a standard K-means algorithm. The goal of cluster analysis is the classification of objects based on similar properties among them and the subdivision into groups or clusters. Among the advantages of the fuzzy clustering algorithm one is that it allows objects to belong to several clusters simultaneously, with different degrees of membership. Our results showed that jcBNST neurons from normal animals displayed a higher degree of physiological diversity than those from dependent animals, meaning that the former subdivided into a greater number of clusters than the latter. Our experiments also showed that long-term neuroadaptive changes induced by drugs of abuse can alter populations of extended amygdala neurons in a way that might remain undetected if evaluating only their gross physiological properties
Computational study of neuronal mechanisms underlying value-based decision making
No full textValue-based decision making is a cognitive process in which an animal selects a specific behavior from a
set of alternatives. The selection is based on the anticipated reward associated with each behavioral
alternative (i.e., subjective values). Subjective values are established, in part, by reinforcement learning
(RL). Substantial progress is being made in identifying neural systems, microcircuits and cellular
mechanisms of decision making. However, dynamical and structural complexity make it difficult to
achieve a comprehensive understanding of mechanisms that underlie value-based decision making.
Computational models can help address this issue. Computational models provide a quantitative
framework for simultaneously studying multiple levels of organization, testing the validity of assumptions,
and assessing the roles of component processes. Moreover, modeling studies can help identify general
principles that apply to a variety of animal species and to diverse behavioral circumstances, and that can be
adapted to artificial systems.
We are using modeling studies to investigate the ways in which an identified neural circuit in Aplysia
selects between two alternative feeding behaviors: ingestion vs. rejection [4]. The neurosimulator SNNAP
[2] was used to develop a neurobiologically plausible model network with cells B4, B8, B31, B34, B35,
B51, B52, and B64 [3]. These cells are elements in a central pattern generator (CPG) that mediates feeding.
The model simulated features of fictive feeding. Currently, the model is being extended by: 1) including an
autapse in B31; 2) adding CPG cells B20, B30, B65, B65 and CBIs 2-4; and 3) incorporating identified
correlates of operant conditioning. Simulations indicated: 1) the autapse and positive feedback among
B31, B34, B35, B63 and B65 mediated the decision to initiate fictive feeding; 2) incorporating the known
neuronal correlates of operant conditioning [1,5] (i) reduced the threshold for eliciting fictive feeding, (ii)
biased the neural activity toward fictive ingestion; the bias toward ingestion resulted from changes in B51,
whereas the reduced threshold resulted from changes in cells B63 and B65 and the electrical coupling
among cells B30, B63, and B65. Finally, the results suggested that as yet unidentified modifications are
necessary to produce more complete ingestion-like neural activity.
Our computational studies suggested that value-based decision making involve multiple sites of plasticity.
These sites mediate the initial commitment to respond and assignment of subjective values. The interplay
among these sites biased the fictive feeding behavior toward a single, highly valued response, which
previously had been associated with positive reinforcement. Finally, these studies are providing an
opportunity to simultaneously investigate decision making at theoretical, algorithmic, and implementation
levels and are providing insights into cognitive processes
Sensitization and dishabituation of swim induction in the leech Hirudo medicinalis: Role of serotonin and cyclic AMP
No full textIn this paper the role of serotonin (5HT) and cyclic AMP (cAMP) in sensitization and dishabituation of swim induction (SI) has been investigated in the leech Hirudo medicinalis. Electrical stimulation of the body wall evokes swimming activity with a constant latency. In animals with a disconnection between head ganglion and segmental ganglia, repetitive stimulation induces habituation of swimming whereas brushing on the dorsal skin provokes sensitization of a naïve response or dishabituation of a previously habituated response. Our findings indicate that 5HT is the neurotransmitter underlying both sensitization and dishabituation of SI. Injection of the 5HT receptor blocking agent methysergide impaires the onset of sensitization and dishabituation induced by brushing. Moreover, injection of 5HT mimics these forms of nonassociative learning, whereas injection of dopamine does not. Finally, the effects of 5HT are mediated by cAMP: (1) after injections of specific adenylate cyclase inhibitors such as MDL 12.330A or SQ22536, brushing becomes ineffective in facilitating the SI in either non-habituated or habituated animals. (2) 8Br-cAMP application mimics both sensitization and dishabituation of SI. © 2003 Elsevier B.V. All rights reserved
Inhibition of Na+/K+ ATPase potentiates synaptic transmission in tactile sensory neurons of the leech
No full textIncreasing evidence indicates that modulation of Na+⁄K+ ATPase activity is involved in forms of neuronal and synaptic plasticity. In
tactile (T) neurons of the leech Hirudo medicinalis, Na+⁄K+ ATPase is the main determinant of the afterhyperpolarization (AHP),
which characterizes the firing of these mechanosensory neurons. Previously, it has been reported that cAMP (3',5'-cyclic adenosine
monophosphate), which mediates the effects of serotonin (5HT) in some forms of learning in the leech, negatively modulates Na+⁄K+ ATPase activity, thereby reducing the AHP amplitude in T neurons. Here, we show that a transient inhibition of Na+⁄K+ ATPase can affect the synaptic connection between two ipsilateral T neurons. Bath application of 10 nm dihydroouabain (DHO), an ouabain analogue, causes an increase in the amplitude of the synaptic potential (SP) recorded in the postsynaptic element when a test stimulus is applied in the presynaptic neuron. Iontophoretic injection of cAMP into the presynaptic T neuron also produces an increase of SP. Simulations carried out by using a computational model of the T neuron suggest that a reduction of the pump rate and a consequent depression of the AHP might facilitate the conduction of action potentials to the synaptic terminals. Moreover, nearly
intact leeches injected with 10 nm DHO respond with a swimming episode more quickly to an electrical stimulation, which selectively activates T neurons exhibiting sensitization of swimming induction. Collectively, our results show that inhibition of Na+⁄K+ ATPase is critical for short-term plasticity
Single neuron activity-dependent signal processing
Full text linkActivity in a neural network can affect both the synaptic strengths and the intrinsic electrical properties of neurons within the network. Changes of the intrinsic properties can enhance, reduce or stabilize the neural excitability. One of the activity-dependent regulatory mechanisms is the afterhyperpolarization, generally due to the activation of K+ conductances and to a Na+/K+ pump. In many neurons, the afterhyperpolarization is modified after a period of spike activity. In the mechanosensory T neurons of the leech, a prolonged electrical activity produces an increase of the afterhyperpolarization. This is believed to induce conduction block of spikes in several regions of the neuron, which in turn may decrease presynaptic invasion of spikes and thereby decrease transmitter release. To explore this possibility, we developed a multicompartment model of a T neuron [1]. The model incorporated empirical data describing the geometry of the cell and activity-dependent changes of the afterhyperpolarization. Simulations indicated that at some branching points activity-dependent increases of the afterhyperpolarization reduced the number of spikes transmitted from the receptive fields to the soma and beyond. Simulations also showed that the afterhyperpolarization could modulate transmission from the soma to the synaptic terminals, suggesting that it can regulate spike conduction within the presynaptic arborizations of the neuron, contributing to the synaptic depression correlated with increases in the afterhyperpolarization. In order to investigate how the afterhyperpolarization modulatory capabilities on transmission were dependent on the axonal geometry as well as on membrane properties, we developed [2] another multicompartment model of the mechanosensory cell, representing the reduced version of the model developed in [1]. The simulations suggested that channel kinetics influence the afterhyperpolarization-dependent modulation of spike conduction through points of impedance mismatch. The processing or conductive features of neurons seems to be determined in the first instance by the channel kinetics of the membrane and secondarily by the axonal geometry and activity-dependent processes and noise. We have also showed [3] that the role of the afterhyperpolarization induced by Na+/K+ pump-activity, which consists in a slow reduction in excitability, is also involved in neuronal coding. We showed that the regulation of excitability by Na+/K+ pump-activity is necessary for the neuron to make different responses depending on the statistical context of the stimuli. We investigate the role of membrane kinetics and input conductance mismatch in the adaptation of spike bursting to stimulus statistics
Measuring the refractive index of water with a pulsed laser diode
No full textIn a previous paper published in this journal (Ronzani et al 2008 Eur. J. Phys. 29 957), an estimate of the light speed in air, obtained by measuring the time of flight of a pulsed laser beam, was reported. Using the same method and apparatus, we have improved the measure of the light speed in air, by increasing the data sample, and measured the light speed in water, obtaining an estimate of the water refractive index equal to n=1.323 (0.016), at the wavelength of 665 nm
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