Asynchronous Inhibition in Neocortical Microcircuits

Neurons are constantly integrating information from external and internal sources, causing them to spike at particular times. The exact timing of spikes is determined by a neuron's intrinsic properties, as well as the interplay between local excitatory and inhibitory inputs. Although inhibitory interneurons have been extensively studied, their contribution to neuronal integration and spike timing remains poorly understood. To elucidate the functional role of GABAergic interneurons during cortical activity, we combined molecular identification of interneurons, two photon imaging and electrophysiological recordings in mouse thalamocortical slices. In this preparation, cortical UP states, a network state characterized by prolonged periods of depolarization and synchronized spiking, can be evoked by thalamic stimulation and can also occur spontaneously. To assay the role of inhibition, we first characterized the firing properties of Parvalbumin (PV) and Somatostatin (SOM) interneurons during UP states activity, and found a higher probability and rate of spiking in these two subtypes compared to excitatory cells. These subtypes did not display differential timing of activation during the evoked response. Furthermore, calcium imaging showed low correlations among PV and SOM interneurons, indicating that neurons sharing these neurochemical markers do not coordinate their firing. Intracellular recordings confirmed that nearby interneurons, known to be electrically coupled, do not display more synchronous spiking than excitatory cells, suggesting that this coupling may not function to synchronize the activity of interneurons on fast time scales¬¬¬. After characterizing inhibitory interneuron outputs, we next studied the timing and correlation of inhibitory inputs, which we isolated from excitatory inputs by voltage clamping at the reversal for excitation (0mV) or inhibition (-70mV). In both thalamically triggered and spontaneous activations, IPSCs between cell pairs were remarkably well correlated, with correlation coefficients reaching over .9 in some cases. This high degree of correlation has previously been assumed to be due to interneuron synchrony, but our population imaging and paired recordings did not support this view. In addition, we found that the connection rate between interneurons is very high (~80%), and quantal analysis revealed that each IPSC recorded in neighboring cells during an UP state could be due to a single presynaptic interneuron. Therefore, we explain the high IPSCs correlations in nearby pyramidal cells are emerging from the common input from individual interneurons, rather than from synchronization of interneuron activity across the population. In a final set of experiments, we found that a partial pharmacological block of inhibitory signaling increased EPSC correlations. Our data support a model in which inhibitory neurons do not fire in a correlated fashion but have strong, dense connections to pyramidal neurons that serve to prevent local excitatory synchrony during UP states. This would mean that inhibition may not, as previously thought, serve to synchronize the firing of excitatory cells, but have precisely the opposite effect, decorrelating their activity by breaking down their coordinated firing. This is consistent with the hypothesis that pyramidal cells are carrying out an essentially integrative function in the circuit and that interneurons expand the temporal dynamic range of this integration

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

Fast non-negative deconvolution for spike train inference from population calcium imaging

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for 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 is fast enough that even when imaging over 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 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.Comment: 22 pages, 10 figure

Cell-Type-Specific Sensorimotor Processing in Striatal Projection Neurons during Goal-Directed Behavior

Goal-directed sensorimotor transformation drives important aspects of mammalian behavior. The striatum is thought to play a key role in reward-based learning and action selection, receiving glutamatergic sensorimotor signals and dopaminergic reward signals. Here, we obtain whole-cell membrane potential recordings from the dorsolateral striatum of mice trained to lick a reward spout after a whisker deflection. Striatal projection neurons showed strong task-related modulation, with more depolarization and action potential firing on hit trials compared to misses. Direct pathway striatonigral neurons, but not indirect pathway striatopallidal neurons, exhibited a prominent early sensory response. Optogenetic stimulation of direct pathway striatonigral neurons, but not indirect pathway striatopallidal neurons, readily substituted for whisker stimulation evoking a licking response. Our data are consistent with direct pathway striatonigral neurons contributing a "go'' signal for goal-directed sensorimotor transformation leading to action initiation