The Effect of Correlated Neuronal Firing and Neuronal Heterogeneity on Population Coding Accuracy in Guinea Pig Inferior Colliculus

Abstract

It has been suggested that the considerable noise in single-cell responses to a stimulus can be overcome by pooling information from a large population. Theoretical studies indicated that correlations in trial-to-trial fluctuations in the responses of different neurons may limit the improvement due to pooling. Subsequent theoretical studies have suggested that inherent neuronal diversity, i.e., the heterogeneity of tuning curves and other response properties of neurons preferentially tuned to the same stimulus, can provide a means to overcome this limit. Here we study the effect of spike-count correlations and the inherent neuronal heterogeneity on the ability to extract information from large neural populations. We use electrophysiological data from the guinea pig Inferior-Colliculus to capture inherent neuronal heterogeneity and single cell statistics, and introduce response correlations artificially. To this end, we generate pseudo-population responses, based on single-cell recording of neurons responding to auditory stimuli with varying binaural correlations. Typically, when pseudo-populations are generated from single cell data, the responses within the population are statistically independent. As a result, the information content of the population will increase indefinitely with its size. In contrast, here we apply a simple algorithm that enables us to generate pseudo-population responses with variable spike-count correlations. This enables us to study the effect of neuronal correlations on the accuracy of conventional rate codes. We show that in a homogenous population, in the presence of even low-level correlations, information content is bounded. In contrast, utilizing a simple linear readout, that takes into account the natural heterogeneity, even of neurons preferentially tuned to the same stimulus, within the neural population, one can overcome the correlated noise and obtain a readout whose accuracy grows linearly with the size of the population.

Long-range intralaminar noise correlations in the barrel cortex

“…Essentially, this analysis extracts from the matrices several "principal components," representing groups of cells that fluctuate together and thus form "collective modes" of network behavior (Zohar et al 2013;Shamir 2014). The eigenvalues represent the variance of the specific mode and are ordered such that successive components account for decreasing magnitudes of variance.…”

Logarithmic distributions prove that intrinsic learning is Hebbian

“…inferior temporal (IT) cortex from monkeys [39] 2. primary auditory cortex (A1) from monkeys [40] 3. primary auditory cortex (A1) of rats [2] 4. Purkinje cells in cerebellum [41,42,43,44] 5. midbrain principal cells from inferior colliculus (IC) from the guinea pig [45] In monkey IT, single unit activity was recorded over 200ms for passive viewing of 77 different natural stimuli for 100 neurons, each stimulus shown 10 times [39]. This yielded 770 spike rate response data points per neuron.…”

“…data for spike rates from the primary auditory cortex of rats for four different conditions, which were recorded as cell-attached in vivo recordings ([2], Figure 2C). For midbrain nuclei neurons (IC), we re-analyzed spike rates in response to tones (for 200ms after stimulus onset) under variations of binaural correlation [45]. The frequency ranking of neurons by mean spike rate, standard deviation, min-max values, CV and FF are shown in ( Figure 3A Data from GABAergic cerebellar Purkinje cells offer some difficulty for this analysis since they have regular single spikes at high frequencies, and in addition, calcium-based complex spikes [43].…”

Modelling firing regularity in the ventral cochlear nucleus: Mechanisms, and effects of stimulus level and synaptopathy

“…This ties in with a general picture of neural functioning that has been developing in recent years, in which heterogeneity of cell properties is important for carrying out robust computations (Brette, 2012;Day and Delgutte, 2013;Garden et al, 2008;Goodman et al, 2013;Marsat and Maler, 2010;Padmanabhan and Urban, 2010;Raman et al, 2010;Zohar et al, 2013). In order to make further progress in our understanding of the function of the VCN, we may need to develop a clearer idea of how this heterogeneity may support downstream auditory computations.…”