Adam M. Packer

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition?

GABAergic interneurons play a major role in the function of the mammalian neocortex, but their circuit connectivity is still poorly understood. We used two-photon RuBi-Glutamate uncaging to optically map how the largest population of cortical interneurons, the parvalbumin-positive cells (PV+), are connected to pyramidal cells (PCs) in mouse neocortex. We found locally dense connectivity from PV+ interneurons onto PCs across cortical areas and layers. In many experiments, all nearby PV+ cells were connected to every local PC sampled. In agreement with this, we found no evidence for connection specificity, as PV+ interneurons contacted PC pairs similarly regardless of whether they were synaptically connected or not. We conclude that the microcircuit architecture for PV+ interneurons, and probably neocortical inhibition in general, is an unspecific, densely homogenous matrix covering all nearby pyramidal cells.

Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging

Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.

Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo

We describe an all-optical strategy for simultaneously manipulating and recording the activity of multiple neurons with cellular resolution in vivo. We performed simultaneous two-photon optogenetic activation and calcium imaging by coexpression of a red-shifted opsin and a genetically encoded calcium indicator. A spatial light modulator allows tens of user-selected neurons to be targeted for spatiotemporally precise concurrent optogenetic activation, while simultaneous fast calcium imaging provides high-resolution network-wide readout of the manipulation with negligible optical cross-talk. Proof-of-principle experiments in mouse barrel cortex demonstrate interrogation of the same neuronal population during different behavioral states and targeting of neuronal ensembles based on their functional signature. This approach extends the optogenetic toolkit beyond the specificity obtained with genetic or viral approaches, enabling high-throughput, flexible and long-term optical interrogation of functionally defined neural circuits with single-cell and single-spike resolution in the mouse brain in vivo.

Spike inference from calcium imaging using sequential Monte Carlo methods.

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest--spike trains and/or time varying intracellular calcium concentrations--are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

Two-photon optogenetics of dendritic spines and neural circuits

We demonstrate a two-photon optogenetic method that generates action potentials in neurons with single-cell precision, using the red-shifted opsin C1V1T. We applied the method to optically map synaptic circuits in mouse neocortical brain slices and to activate small dendritic regions and individual spines. Using a spatial light modulator, we split the laser beam onto several neurons and performed simultaneous optogenetic activation of selected neurons in three dimensions.

Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes

Deciphering the circuitry of the neocortex requires knowledge of its components, making a systematic classification of neocortical neurons necessary. GABAergic interneurons contribute most of the morphological, electrophysiological and molecular diversity of the cortex, yet interneuron subtypes are still not well defined. To quantitatively identify classes of interneurons, 59 GFP-positive interneurons from a somatostatin-positive mouse line were characterized by whole-cell recordings and anatomical reconstructions. For each neuron, we measured a series of physiological and morphological variables and analyzed these data using unsupervised classification methods. PCA and cluster analysis of morphological variables revealed three groups of cells: one comprised of Martinotti cells, and two other groups of interneurons with short asymmetric axons targeting layers 2/3 and bending medially. PCA and cluster analysis of electrophysiological variables also revealed the existence of these three groups of neurons, particularly with respect to action potential time course. These different morphological and electrophysiological characteristics could make each of these three interneuron subtypes particularly suited for a different function within the cortical circuit.

The Logic of Inhibitory Connectivity in the Neocortex

Although inhibition plays a major role in the function of the mammalian neocortex, the circuit connectivity of GABAergic interneurons has remained poorly understood. The authors review recent studies of the connections made to and from interneurons, highlighting the overarching principle of a high density of unspecific connections in inhibitory connectivity. Whereas specificity remains in the subcellular targeting of excitatory neurons by interneurons, the general strategy appears to be for interneurons to provide a global “blanket of inhibition” to nearby neurons. In the review, the authors highlight the fact that the function of interneurons, which remains elusive, will be informed by understanding the structure of their connectivity as well as the dynamics of inhibitory synaptic connections. In a last section, the authors describe briefly the link between dense inhibitory networks and different interneuron functions described in the neocortex.

Designing optimal stimuli to control neuronal spike timing.

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents.

PackIO and EphysViewer: software tools for acquisition and analysis of neuroscience data

We present an open-source synchronization software package, PackIO, that can record and generate voltage signals to enable complex experimental paradigms across multiple devices. This general purpose package is built on National Instruments data acquisition and generation hardware and has temporal precision up to the limit of the hardware. PackIO acts as a flexibly programmable master clock that can record experimental data (e.g. voltage traces), timing data (e.g. event times such as imaging frame times) while generating stimuli (e.g. voltage waveforms, voltage triggers to drive other devices, etc.). PackIO is particularly useful to record from and synchronize multiple devices, for example when simultaneously acquiring electrophysiology while generating and recording imaging timing data. Experimental control is easily enabled by an intuitive graphical user interface. We also release an open-source data visualisation and analysis tool, EphysViewer, written in MATLAB, as well as a module to import data into Python. These flexible and programmable tools allow experimenters to configure and set up customised input and output protocols in a synchronized fashion for controlling, recording, and analysing experiments.