Electrophysiological Classes of Cat Primary Visual Cortical Neurons In Vivo as Revealed by Quantitative Analyses

Abstract

To facilitate the characterization of cortical neuronal function, the responses of cells in cat area 17 to intracellular injection of current pulses were quantitatively analyzed. A variety of response variables were used to separate the cells into subtypes using cluster analysis. Four main classes of neurons could be clearly distinguished: regular spiking (RS), fast spiking (FS), intrinsic bursting (IB), and chattering (CH). Each of these contained significant subclasses. RS neurons were characterized by trains of action potentials that exhibited spike frequency adaptation. Morphologically, these cells were spiny stellate cells in layer 4 and pyramidal cells in layers 2, 3, 5, and 6. FS neurons had short-duration action potentials (<0.5 ms at half height), little or no spike frequency adaptation, and a steep relationship between injected current intensity and spike discharge frequency. Morphologically, these cells were sparsely spiny or aspiny nonpyramidal cells. IB neurons typically generated a low frequency (<425 Hz) burst of spikes at the beginning of a depolarizing current pulse followed by a tonic train of action potentials for the remainder of the pulse. These cells were observed in all cortical layers, but were most abundant in layer 5. Finally, CH neurons generated repetitive, high-frequency (350-700 Hz) bursts of short-duration (<0.55 ms) action potentials. Morphologically, these cells were layer 2-4 (mainly layer 3) pyramidal or spiny stellate neurons. These results indicate that firing properties do not form a continuum and that cortical neurons are members of distinct electrophysiological classes and subclasses.

Cortical pyramidal cells as non-linear oscillators: Experiment and spike-generation theory

“…Furthermore, the action potential amplitude and width generated by the theta neuron are largely independent of the injected current amplitude. It is important to note that these intrinsic properties to a large extent have been observed in real cortical neurons in vitro and in vivo (McCormick et al 1985,Nowak et al 2003.…”

Electrophysiological and morphological heterogeneity of slow firing neurons in medial septal/diagonal band complex as revealed by cluster analysis

“…The neurons spatially closest to each other are grouped together as a cluster. The analysis was performed using Ward's method which is based on the sum of squares between the clusters (Euclidean distances, summed over all variables) (Anderson et al, 1998) allowing an objective segregation of cell groups as previously reported (Pennartz et al, 1998, Cauli et al, 2000, Nowak et al, 2003. Electrophysiological variables were normalized to a common range (0-1) before performing the multidimentional analysis.…”

“…This manuscript focuses on the diversity of slow firing neurons of the MS/DB region. Analysis of the firing properties of other medial septal neuronal groups will follow in subsequent work.One major obstacle in categorizing neurons is determining whether a subjectively defined category can be assigned to a distinct subgroup or if a continuous spectrum of physiological properties exists in a larger population (for review see Nowak et al, 2003). In order to address this problem, we used cluster analysis on patch-clamp data recorded in vitro from adult rat MS/ DB neurons to identify subgroups of slow firing neurons.…”

“…The firing behavior of the neuron was first inspected visually for heterogeneity and later analyzed via unsupervised clustering analysis. This technique can assist in uncovering data structure and providing a scheme for classifying neuronal subtypes (Pennartz et al, 1998, Nowak et al, 2003, Halabisky et al, 2004. This method consists of plotting individual objects (neurons) in multidimensional space according to similarities or dissimilarities of input Pennartz et al, 1998, Cauli et al, 2000, Nowak et al, 2003.…”

“…Normalization was performed by computing the range normalization scores (RN) for variable data as follows: RN = (x − x min )/(x max − x min ), where x is data values, and x min and x max are the minimal and maximum values respectively (Halabisky et al, 2006). For cluster analysis, variables were selected based on their lack of linear correlation with each other and the presence of multimodality (non-normal distribution) (Nowak et al, 2003). Additionally, several variables were excluded as follows: (1) action potential amplitude which could be influenced by the quality of the capacitance compensation which, in turn, depends on the depth of the patch pipette in the slice; (2) the input resistance and membrane time constant which could also be transiently affected by levels of spontaneous ongoing activity when present (Bernander et al, 1991, Schaap et al, 1999.…”