SummaryThe thalamus receives sensory input from different circuits in the periphery. How these sensory channels are integrated at the level of single thalamic cells is not well understood. We performed targeted single-cell-initiated transsynaptic tracing to label the retinal ganglion cells that provide input to individual principal cells in the mouse lateral geniculate nucleus (LGN). We identified three modes of sensory integration by single LGN cells. In the first, 1–5 ganglion cells of mostly the same type converged from one eye, indicating a relay mode. In the second, 6–36 ganglion cells of different types converged from one eye, revealing a combination mode. In the third, up to 91 ganglion cells converged from both eyes, revealing a binocular combination mode in which functionally specialized ipsilateral inputs joined broadly distributed contralateral inputs. Thus, the LGN employs at least three modes of visual input integration, each exhibiting different degrees of specialization.

Inhibition plays a fundamental role in controlling neuronal activity in the brain. While perisomatic inhibition has been studied in detail, the majority of inhibitory synapses are found on dendritic shafts and are less well characterized. Here, we combine paired patch-clamp recordings and two-photon Ca(2+) imaging to quantify inhibition exerted by individual GABAergic contacts on hippocampal pyramidal cell dendrites. We observed that Ca(2+) transients from back-propagating action potentials were significantly reduced during simultaneous activation of individual nearby inhibitory contacts. The inhibition of Ca(2+) transients depended on the precise spike-timing (time constant < 5 ms) and declined steeply in the proximal and distal direction (length constants 23-28 μm). Notably, Ca(2+) amplitudes in spines were inhibited to the same degree as in the shaft. Given the known anatomical distribution of inhibitory synapses, our data suggest that the collective inhibitory input to a pyramidal cell is sufficient to control Ca(2+) levels across the entire dendritic arbor with micrometer and millisecond precision.

The use of transgenic mice in which subtypes of neurons are labeled with a fluorescent protein has greatly facilitated modern neuroscience research. GAD65-GFP mice, which have GABAergic interneurons labeled with GFP, are widely used in many research laboratories, although the properties of the labeled cells have not been studied in detail. Here we investigate these cells in the hippocampal area CA1 and show that they constitute ∼20% of interneurons in this area. The majority of them expresses either reelin (70±2%) or vasoactive intestinal peptide (VIP; 15±2%), while expression of parvalbumin and somatostatin is virtually absent. This strongly suggests they originate from the caudal, and not the medial, ganglionic eminence. GFP-labeled interneurons can be subdivided according to the (partially overlapping) expression of neuropeptide Y (42±3%), cholecystokinin (25±3%), calbindin (20±2%) or calretinin (20±2%). Most of these subtypes (with the exception of calretinin-expressing interneurons) target the dendrites of CA1 pyramidal cells. GFP-labeled interneurons mostly show delayed onset of firing around threshold, and regular firing with moderate frequency adaptation at more depolarized potentials.

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Hidden Markov models (HMMs) provide an excellent analysis of recordings with very poor signal/noise ratio made from systems such as ion channels which switch among a few states. This method has also recently been used for modeling the kinetic rate constants of molecular motors, where the observable variable-the position-steadily accumulates as a result of the motor's reaction cycle. We present a new HMM implementation for obtaining the chemical-kinetic model of a molecular motor's reaction cycle called the variable-stepsize HMM in which the quantized position variable is represented by a large number of states of the Markov model. Unlike previous methods, the model allows for arbitrary distributions of step sizes, and allows these distributions to be estimated. The result is a robust algorithm that requires little or no user input for characterizing the stepping kinetics of molecular motors as recorded by optical techniques.