Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content

Abstract

Although examples of variation and diversity exist throughout the nervous system, their importance remains a source of debate. Even neurons of the same molecular type have notable intrinsic differences. Largely unknown, however, is the degree to which these differences impair or assist neural coding. We examined the outputs from a single type of neuron, the mitral cells of the mouse olfactory bulb, to identical stimuli and found that each cell's spiking response was dictated by its unique biophysical fingerprint. Using this intrinsic heterogeneity, diverse populations were able to code for twofold more information than their homogeneous counterparts. In addition, biophysical variability alone reduced pair-wise output spike correlations to low levels. Our results indicate that intrinsic neuronal diversity is important for neural coding and is not simply the result of biological imprecision.

Fig 1

Intrinsic diversity of mitral cell populations. a) Schematic of mammalian main olfactory bulb circuitry. Olfactory receptor neurons (ORNs) expressing one olfactory receptor all send their axons to the same glomerulus. All mitral/tufted (M/T) cell apical dendrites connected to a glomerulus receive inputs that are highly correlated. b-c) Biocytin fills of two representative mitral cells with spike responses to a fixed DC current. In both cells, apical dendrites and their tufts (green arrow) and lateral dendrites (blue arrow) are intact in the slice. d) Mitral cell spike outputs are also diverse based on the shape of the after hyperpolarizations that follow their action potentials (color corresponds to traces in b-c). e) Mitral cells differ widely in both firing rates and in the coefficients of variation (CVs) of their interspike intervals. f) Recordings of mitral cells show wide variation in excitability as described by the frequency of action potentials generated by constant current stimuli of different amplitudes. g) Confocal micrographs of the olfactory bulb stained for Kv1.2 (green, left panel) and Hoechst (blue, middle panel) and an overlay of Kv1.2 positive cell bodies and mitral cell nuclei (right panel). Red arrows highlight cell bodies of Kv positive neurons and their nuclei while white arrows highlight nuclei of mitral cells that do not express Kv1.2. Kv positive and negative mitral cells are interspersed in the same focal plane.

Fig 2

Uniqueness of mitral cell output to identical input. a) Spike rasters of 10 trials for three mitral cells to an identical fluctuating input (top black trace). b) Projection of all spike patterns (points) from multiple cells (colors) onto a space defined by the first 3 principal components calculated from all spike trains. c) Classification accuracy of spike trains based on recording identity as a function of the number of eigenvectors (λ) used for classification. d) The number of nearest neighbours (1, 3, 5, 10) does not affect the classification accuracy. e) The percentage of trials used in the testing and the training sets affects the classification accuracy only when 80% of spike trains are used in training. (error = s.d.)

Fig. 3

Intrinsic diversity affects pairwise spike train correlations. a) Histogram of all pairwise correlations for within-cell (black) and between-cell spike trains (red). Inset. Mean pairwise correlations are significantly different within-cells and between cells. b) Histogram of all pairwise correlations from cells receiving an identical input. c) Pairwise correlations of all spike trains from all mitral cells as a function of differences in firing rates. (Error = s.d.)

Fig 4

Mitral cell spike triggered average (STA) diversity. a) Spike-triggered averages (STA) for the three cells in Fig 3a. b) STAs for a population of mitral cells that all received noisy input illustrates the diversity (N = 35 STAs). Color corresponds to identity in e. c) Principal components (PC) 1-3 of the STAs in b. d) Variance explained by the 1st 5 principal components for this population of mitral cells. e) Projection of each STA onto the space of principal components in c shows mitral cell STAs are not uniformly distributed, but span an arc in the space.

Fig 5

Heterogeneous populations of mitral cells carry more information than their homogeneous counterparts. a) Representative trials of spike trains (6/cell) from 4 mitral cells all given an identical fluctuating input. b) A homogeneous population response was constructed by randomly drawing spike trains from a single recorded cell (blue). A heterogeneous population was constructed by randomly drawing spike trains from different neurons. The responses of each population were binarized into words of 0s and 1s and the pattern of words, for instance t_homogeneous and t_{heterogeneous}, were analyzed to calculate information. c) Heterogeneous populations of mitral cells carry twice as much information as homogeneous populations of cells. (Error = s.d.)

Fig. 6

Biophysical diversity correlates to information transfer. a) Spike train examples of a single trial for 10 mitral cells with different STAs. b) STAs of the 10 cells in (a) colour coded by cell identity. c) STA distance matrix calculated by measuring the Euclidian distance of the STAs to one another in the space defined by the principal components. d) Bits of information as a function of sum of STA distance/number of cells.

Fig. 7

Heterogeneous populations improve the coding of physiologically relevant stimuli. a) Synaptic input currents modulated by an 8 Hz periodic oscillation. b) Responses of 6 trials each from 3 mitral cells to the identical periodic input. c) Probability of spike firing at various stimulus epochs. d) Enlargement of one theta cycle and spike times for the three cells in b over multiple trials and the e) probability of firing during the cycle. f) STAs for the 3 mitral cells calculated by injecting a rapidly fluctuating noisy input.