TY - JOUR
AB - Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. However, the dendritic mechanisms underlying rapid interneuron recruitment have remained unclear. To quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. Both specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. Furthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. Thus, the somatodendritic gradient of R(m) was the reverse of that in pyramidal neurons. Further computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. These properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.
AU - Norenberg, Anja
AU - Hua Hu
AU - Vida, Imre
AU - Bartos, Marlene
AU - Peter Jonas
ID - 3831
IS - 2
JF - PNAS
TI - Distinct nonuniform cable properties optimize rapid and efficient activation of fast-spiking GABAergic interneurons
VL - 107
ER -
TY - JOUR
AB - A recent paper by von Engelhardt et al. identifies a novel auxiliary subunit of native AMPARs, termedCKAMP44. Unlike other auxiliary subunits, CKAMP44 accelerates desensitization and prolongs recovery from desensitization. CKAMP44 is highly expressed in hippocampal dentate gyrus granule cells and decreases the paired-pulse ratio at perforant path input synapses. Thus, both principal and auxiliary AMPAR subunits control the time course of signaling at glutamatergic synapses.
AU - Guzmán, José
AU - Jonas, Peter M
ID - 3832
IS - 1
JF - Neuron
TI - Beyond TARPs: The growing list of auxiliary AMPAR subunits
VL - 66
ER -
TY - JOUR
AB - Background
The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.
Results
In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.
Conclusions
The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.
AU - Wolf, Verena
AU - Goel, Rushil
AU - Mateescu, Maria
AU - Henzinger, Thomas A
ID - 3834
IS - 42
JF - BMC Systems Biology
TI - Solving the chemical master equation using sliding windows
VL - 4
ER -
TY - CONF
AB - We present a numerical approximation technique for the analysis of continuous-time Markov chains that describe net- works of biochemical reactions and play an important role in the stochastic modeling of biological systems. Our approach is based on the construction of a stochastic hybrid model in which certain discrete random variables of the original Markov chain are approximated by continuous deterministic variables. We compute the solution of the stochastic hybrid model using a numerical algorithm that discretizes time and in each step performs a mutual update of the transient prob- ability distribution of the discrete stochastic variables and the values of the continuous deterministic variables. We im- plemented the algorithm and we demonstrate its usefulness and efficiency on several case studies from systems biology.
AU - Henzinger, Thomas A
AU - Mateescu, Maria
AU - Mikeev, Linar
AU - Wolf, Verena
ID - 3838
TI - Hybrid numerical solution of the chemical master equation
ER -
TY - CONF
AB - We present a loop property generation method for loops iterating over multi-dimensional arrays. When used on matrices, our method is able to infer their shapes (also called types), such as upper-triangular, diagonal, etc. To gen- erate loop properties, we first transform a nested loop iterating over a multi- dimensional array into an equivalent collection of unnested loops. Then, we in- fer quantified loop invariants for each unnested loop using a generalization of a recurrence-based invariant generation technique. These loop invariants give us conditions on matrices from which we can derive matrix types automatically us- ing theorem provers. Invariant generation is implemented in the software package Aligator and types are derived by theorem provers and SMT solvers, including Vampire and Z3. When run on the Java matrix package JAMA, our tool was able to infer automatically all matrix types describing the matrix shapes guaranteed by JAMA’s API.
AU - Henzinger, Thomas A
AU - Hottelier, Thibaud
AU - Kovács, Laura
AU - Voronkov, Andrei
ID - 3839
TI - Invariant and type inference for matrices
VL - 5944
ER -