183 research outputs found
Signaling in Small Subcellular Volumes. II. Stochastic and Diffusion Effects on Synaptic Network Properties
No full textAbstractThe synaptic signaling network is capable of sophisticated cellular computations. These include the ability to respond selectively to different patterns of input, and to sustain changes in response over long periods. The small volume of the synapse complicates the analysis of signaling because the chemical environment is strongly affected by diffusion and stochasticity. This study is based on an updated version of a previously proposed synaptic signaling circuit (Bhalla and Iyengar, 1999) and analyzes three network computation properties in small volumes: bistability, thresholding, and pattern selectivity. Simulations show that although there are diffusive regimes in which bistability may persist, chemical noise at small volumes overwhelms bistability. In the deterministic situation, the network exhibits a sharp threshold for transition between lower and upper stable states. This transition is broadened and individual runs partition between lower and upper states, when stochasticity is considered. The third network property, pattern selectivity, is severely degraded at synaptic volumes. However, there are regimes in which a process similar to stochastic resonance operates and amplifies pattern selectivity. These results imply that simple scaling of signaling conditions to femtoliter volumes is unlikely, and microenvironments, such as reaction complex formation, may be essential for reliable small-volume signaling
Mechanisms for Temporal Tuning and Filtering by Postsynaptic Signaling Pathways
No full textAbstractNetworks of signaling pathways perform complex temporal decoding functions in diverse biological systems, including the synapse, development, and bacterial chemotaxis. This paper examines temporal filtering and tuning properties of synaptic signaling pathways as a possible substrate for emergent temporal decoding. A mass action kinetic model of 16 synaptic signaling pathways was used to dissect out the contribution of these pathways in linear cascades and when coupled to form a network. The model predicts two primary mechanisms of temporal tuning of pathways: a weighted summation of responses of pathways with different timings and the presence of biochemical feedback loop(s) with emergent dynamics. Regulatory inputs act differently on these two tuning mechanisms. In the first case, regulators act like a gain-control on pathways with different intrinsic tuning. In the case of feedback loops, the temporal properties of the loop itself are changed. These basic tuning mechanisms may underlie specialized temporal tuning functions in more complex signaling systems in biology
How to record a million synaptic weights in a hippocampal slice.
No full textA key step toward understanding the function of a brain circuit is to find its wiring diagram. New methods for optical stimulation and optical recording of neurons make it possible to map circuit connectivity on a very large scale. However, single synapses produce small responses that are difficult to measure on a large scale. Here I analyze how single synaptic responses may be detectable using relatively coarse readouts such as optical recording of somatic calcium. I model a network consisting of 10,000 input axons and 100 CA1 pyramidal neurons, each represented using 19 compartments with voltage-gated channels and calcium dynamics. As single synaptic inputs cannot produce a measurable somatic calcium response, I stimulate many inputs as a baseline to elicit somatic action potentials leading to a strong calcium signal. I compare statistics of responses with or without a single axonal input riding on this baseline. Through simulations I show that a single additional input shifts the distribution of the number of output action potentials. Stochastic resonance due to probabilistic synaptic release makes this shift easier to detect. With approximately 80 stimulus repetitions this approach can resolve up to 35% of individual activated synapses even in the presence of 20% recording noise. While the technique is applicable using conventional electrical stimulation and extracellular recording, optical methods promise much greater scaling, since the number of synapses scales as the product of the number of inputs and outputs. I extrapolate from current high-speed optical stimulation and recording methods, and show that this approach may scale up to the order of a million synapses in a single two-hour slice-recording experiment
HillTau models of key signaling motifs.
No full textA-C: Feedback inhibition. A: Mass-action reaction scheme for feedback inhibition, involving 7 molecules and 5 reactions. B: HillTau version. Each box represents a molecule. If there are input arrows to the box it means there is a reaction whose product is the named molecule. Input arrows can be either inputs (reagents), activators, inhibitors, or modifiers. This reaction consists of 3 molecules (input, fb, and output) and 2 reactions (fb and output). C: Simulations for mass-action (blue) andHillTau (orange) versions of feedback inhibition. The green trace is the input molecule. D-F: Oscillator from ultrasensitive MAPK cascade, taken from [35]. D: Output of simulation. Blue is ODE output and orange is HillTau. E: ODE model. This uses 15 molecules, and 11 reactions. MAPK-pp is the molecular species used as output of the oscillator. F: HillTau reaction scheme for oscillator, using 5 molecules and 3 reactions. The concentration of the ‘output’ molecule is plotted. G: HillTau model of bistable system, involving 4 molecules and 2 reactions. H: Phase plot showing stable states of system as the intersection points between the steady-state dose-response curves. This was generated by varying the feedback molecule fb, and measuring output (brown curve), and then varying the output molecule and measuring fb (pink curve). I: Time-series illustration of state switching in the bistable. As before, output is in brown and fb in pink. The Y axes of H and I are the same to show that the steady-state output levels (brown) match. The system starts in the low state. At 20 s a small excitatory input stim is given which fails to switch the state. At 40 s a strong input causes switching to the high state. At 60 s a weak inhibitory input fails to turn it off, but at 80 s a strong inhibitory input returns the state to baseline. Excitatory and inhibitory inputs were delivered by transiently setting the level of stim to high or low values.</p
Models of cell signaling pathways
No full textCellular signaling circuits handle an enormous range of computations. Beyond the housekeeping, replicating and other functions of individual cells, signaling circuits must implement the immensely complex logic of development and function of multicellular organisms. Computer models are useful tools to understand this complexity. Recent studies have extended such models to include electrical, mechanical and spatial details of signaling, and to address the stochastic effects that arise when small numbers of molecules interact. Increasing numbers of models have been developed in close conjunction with experiments, and this interplay gives a deeper and more reliable insight into signaling function
Dendrites, deep learning, and sequences in the hippocampus
No full textThe hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation
Multiscale Modeling and Synaptic Plasticity
No full textSynaptic plasticity is a major convergence point for theory and computation, and the process of plasticity engages physiology, cell, and molecular biology. In its many manifestations, plasticity is at the hub of basic neuroscience questions about memory and development, as well as more medically themed questions of neural damage and recovery. As an important cellular locus of memory, synaptic plasticity has received a huge amount of experimental and theoretical attention. If computational models have tended to pick specific aspects of plasticity, such as STDP, and reduce them to an equation, some experimental studies are equally guilty of oversimplification each time they identify a new molecule and declare it to be the last word in plasticity and learning. Multiscale modeling begins with the acknowledgment that synaptic function spans many levels of signaling, and these are so tightly coupled that we risk losing essential features of plasticity if we focus exclusively on any one level. Despite the technical challenges and gaps in data for model specification, an increasing number of multiscale modeling studies have taken on key questions in plasticity. These have provided new insights, but importantly, they have opened new avenues for questioning. This review discusses a wide range of multiscale models in plasticity, including their technical landscape and their implications
Multiscale interactions between chemical and electric signaling in LTP induction, LTP reversal and dendritic excitability
No full textSynaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains excitability. Both these processes involve interactions between molecular events, electrical events, and network activity. Here I explore these intersections with a multilevel model that embeds molecular events following synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal neuron. I model synaptic plasticity using a two-state (bistable) molecular switch that controls glutamate receptor insertion into the post-synaptic density. I also model dendritic activation of the MAPK signaling pathway, which in turn phosphorylates and inactivates A-type potassium channels. I find that LTP-inducing stimuli turn on individual spines and raise dendritic excitability. This increases the amount of calcium that enters due to synaptic input triggered by network activity. As a result, LTD is now induced in some synapses. Overall, this suggests a mechanism for cellular homeostasis where strengthening of some synapses eventually balances out through weakening of a possibly overlapping set of other synapses. Even in this very narrow slice of cellular events, interesting system properties arise at the interface between multiple scales of cellular function
Model fitting and model reduction.
No full textA: Block diagram of source model with 123 molecules and 120 reactions, from [44]. B: First pass reduced model in HillTau, with 1 reaction and 4 molecules. C: Final reduced HillTau model including activated S6K (aS6K) and activated CaMKIII (aCaMKIII) as intermediate readouts which also were fit to data. This model has 4 reactions and 10 molecules. D-K: Eight ‘experiments’ on reference and HillTau models, not part of stimulus set used to tune parameters for HillTau version. In all cases protein production rate is readout. Blue plots are reference, orange areHillTau. D: [email protected] nM + [email protected] μM, 900 seconds. E: [email protected], Ca2+@1μM. F: 3 pulses of [email protected] nM for 5s, coincident with Ca2+@10μM for 1s, pulses separated by 300 s. G: Same as F, but Ca2+ held at baseline of 0.08 μM. H: Dose-response of protein vs. Ca2+, holding BDNF fixed at 3.7 nM. I: Dose-response of protein vs BDNF, holding Ca2+ fixed at 0.08 μM. J: Protein production rate for fixed [email protected] nM, where Ca2+ was given in 1 second pulses at intervals of 300, 120, 60 and 10 seconds; each pulse sequence lasting for 1200 s.. K: Dose response of protein production rate vs. Ca2+, holding BDNF at basal levels of 0.05 nM. The average normalized RMS difference across the eight ‘experiments’ was under 10%, and in all cases the qualitative properties such as direction of change, matched well.</p
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