Jose A. Garcia-Lazaro

Independent population coding of speech with sub-millisecond precision.

To understand the strategies used by the brain to analyze complex environments, we must first characterize how the features of sensory stimuli are encoded in the spiking of neuronal populations. Characterizing a population code requires identifying the temporal precision of spiking and the extent to which spiking is correlated, both between cells and over time. In this study, we characterize the population code for speech in the gerbil inferior colliculus (IC), the hub of the auditory system where inputs from parallel brainstem pathways are integrated for transmission to the cortex. We find that IC spike trains can carry information about speech with sub-millisecond precision, and, consequently, that the temporal correlations imposed by refractoriness can play a significant role in shaping spike patterns. We also find that, in contrast to most other brain areas, the noise correlations between IC cells are extremely weak, indicating that spiking in the population is conditionally independent. These results demonstrate that the problem of understanding the population coding of speech can be reduced to the problem of understanding the stimulus-driven spiking of individual cells, suggesting that a comprehensive model of the subcortical processing of speech may be attainable in the near future.

Response linearity in primary auditory cortex of the ferret

The responses of neurons within the primary auditory cortex (A1) of the ferret elicited by broadband dynamic spectral ripple stimuli were examined over a range of ripple spectral densities and ripple velocities. The large majority of neurons showed modulated responses to these stimuli and responded most strongly at low ripple densities and velocities. The period histograms of their responses were subjected to Fourier analysis, and the ratio of the magnitudes of the f1 and f0 (DC) components of these responses were calculated to give a quantitative index of response linearity. For 82 out of 396 neurons tested (20.7%) this ratio remained above 1.0 over the entire range of ripple densities and velocities. These neurons were classified as ‘consistently linear’. A further 134/396 (33.8%) of neurons maintained an f1/f0 ratio above 1.0 for either a range of ripple densities at a fixed ripple velocity, or over a range of ripple velocities at a specific ripple density, and were classified as ‘locally linear’. Interestingly, for the superficial layers of the primary auditory cortex, consistently linear and locally linear neurons outnumbered nonlinear neurons by a 2:1 ratio. The converse was true for the deep layers. Unlike in primary visual cortex, where f1/f0 ratios have been reported to exhibit a bimodal distribution with a minimum at f1/f0≈ 1, f1/f0 ratios for A1 are unimodally distributed with a peak at f1/f0≈ 1.

Emergence of Tuning to Natural Stimulus Statistics along the Central Auditory Pathway

We have previously shown that neurons in primary auditory cortex (A1) of anaesthetized (ketamine/medetomidine) ferrets respond more strongly and reliably to dynamic stimuli whose statistics follow “natural” 1/f dynamics than to stimuli exhibiting pitch and amplitude modulations that are faster (1/f 0.5) or slower (1/f 2) than 1/f. To investigate where along the central auditory pathway this 1/f-modulation tuning arises, we have now characterized responses of neurons in the central nucleus of the inferior colliculus (ICC) and the ventral division of the mediate geniculate nucleus of the thalamus (MGV) to 1/f γ distributed stimuli with γ varying between 0.5 and 2.8. We found that, while the great majority of neurons recorded from the ICC showed a strong preference for the most rapidly varying (1/f 0.5 distributed) stimuli, responses from MGV neurons did not exhibit marked or systematic preferences for any particular γ exponent. Only in A1 did a majority of neurons respond with higher firing rates to stimuli in which γ takes values near 1. These results indicate that 1/f tuning emerges at forebrain levels of the ascending auditory pathway.

Analysis of the neural hypercolumn in parallel PCSIM simulations

Abstract Large and sudden changes in pitch or loudness occur statistically less frequently than gradual fluctuations, which means that natural sounds typically exhibit 1/f spectra. Experiments conducted on human subjects showed that listeners indeed prefer 1/f distributed melodies to melodies with faster or slower dynamics. It was recently demonstrated by using animal models, that neurons in primary auditory cortex of anesthetized ferrets exhibit a pronounced preference to stimuli that exhibit 1/f statistics. In the visual modality, it was shown that neurons in primary visual cortex of macaque monkeys exhibit tuning to sinusoidal gratings featuring 1/f dynamics. One might therefore suspect that neurons in mammalian cortex exhibit Self-Organizing Criticality. Indeed, we have found SOC-like phenomena in neurophysiological data collected in rat primary somatosensory cortex. In this paper we concentrated on investigation of the dynamics of cortical hypercolumn consisting of about 128 thousand simulated neurons. The set of 128 Liquid State Machines, each consisting 1024 neurons, was simulated on a simple cluster built of two double quad-core machines (16 cores). PCSIM was designed as a tool for simulating artificial biological-like neural networks composed of different models of neurons and different types of synapses. The simulator was written in C++ with a primary interface dedicated for the Python programming language. As its authors ensure it is intended to simulate networks containing up to millions of neurons and on the order of billions of synapses. This is achieved by distributing the network over different nodes of a computing cluster by using Message Passing Interface. The results obtained for Leaky Integrate-and-Fire model of neurons used for the construction of the hypercolumn and varying density of inter-column connections will be discussed. Benchmarking results for using the PCSIM on the cluster and predictions for grid computing will be presented to some extent. Research presented herein makes a good starting point for the simulations of very large parts of mammalian brain cortex and in some way leading to better understanding of the functionality of human brain.

Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks.

Periodotopy in the gerbil inferior colliculus: Local clustering rather than a gradient map

Periodicities in sound waveforms are widespread, and shape important perceptual attributes of sound including rhythm and pitch. Previous studies have indicated that, in the inferior colliculus (IC), a key processing stage in the auditory midbrain, neurons tuned to different periodicities might be arranged along a periodotopic axis which runs approximately orthogonal to the tonotopic axis. Here we map out the topography of frequency and periodicity tuning in the IC of gerbils in unprecedented detail, using pure tones and different periodic sounds, including click trains, sinusoidally amplitude modulated (SAM) noise and iterated rippled noise. We found that while the tonotopic map exhibited a clear and highly reproducible gradient across all animals, periodotopic maps varied greatly across different types of periodic sound and from animal to animal. Furthermore, periodotopic gradients typically explained only about 10% of the variance in modulation tuning between recording sites. However, there was a strong local clustering of periodicity tuning at a spatial scale of ca. 0.5 mm, which also differed from animal to animal.

An Overrepresentation of High Frequencies in the Mouse Inferior Colliculus Supports the Processing of Ultrasonic Vocalizations.

Mice are of paramount importance in biomedical research and their vocalizations are a subject of interest for researchers across a wide range of health-related disciplines due to their increasingly important value as a phenotyping tool in models of neural, speech and language disorders. However, the mechanisms underlying the auditory processing of vocalizations in mice are not well understood. The mouse audiogram shows a peak in sensitivity at frequencies between 15-25 kHz, but weaker sensitivity for the higher ultrasonic frequencies at which they typically vocalize. To investigate the auditory processing of vocalizations in mice, we measured evoked potential, single-unit, and multi-unit responses to tones and vocalizations at three different stages along the auditory pathway: the auditory nerve and the cochlear nucleus in the periphery, and the inferior colliculus in the midbrain. Auditory brainstem response measurements suggested stronger responses in the midbrain relative to the periphery for frequencies higher than 32 kHz. This result was confirmed by single- and multi-unit recordings showing that high ultrasonic frequency tones and vocalizations elicited responses from only a small fraction of cells in the periphery, while a much larger fraction of cells responded in the inferior colliculus. These results suggest that the processing of communication calls in mice is supported by a specialization of the auditory system for high frequencies that emerges at central stations of the auditory pathway.

Analysis and modelling of variability and covariability of population spike trains across multiple time scales

As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that allows for analysis and modelling of variability and covariability of population spike trains across multiple time scales. The model framework can be used to specify the values of many different variability measures including spike timing precision across trials, coefficient of variation of the interspike interval distribution, and Fano factor of spike counts for individual neurons, as well as signal and noise correlations and correlations of spike counts across multiple neurons. Using both simulated data and data from different stages of the mammalian auditory pathway, we demonstrate the range of possible independent manipulations of different variability measures, and explore how this range depends on the sensory stimulus. The model provides a powerful framework for the study of experimental and surrogate data and for analyzing dependencies between different statistical properties of neuronal populations.

Tell me something interesting: context dependent adaptation in somatosensory cortex.

It is widely accepted that through a process of adaptation cells adjust their sensitivity in accordance with prevailing stimulus conditions. However, in two recent studies exploring adaptation in the rodent inferior colliculus and somatosensory cortex, neurons did not adapt towards global mean, but rather became most sensitive to inputs that were located towards the edge of the stimulus distribution with greater intensity than the mean. We re-examined electrophysiological data from the somatosensory study with the purpose of exploring the underlying encoding strategies. We found that neural gain tended to decrease as stimulus variance increased. Following adaptation to changes in global mean, neuronal output was scaled such that the relationship between firing rate and local, rather than global, differences in stimulus intensity was maintained. The majority of cells responded to large, positive deviations in stimulus amplitude; with a small number responding to both positive and negative changes in stimulus intensity. Adaptation to global mean was replicated in a model neuron by incorporating both spike-rate adaptation and tonic-inhibition, which increased in proportion to stimulus mean. Adaptation to stimulus variance was replicated by approximating the output of a population of neurons adapted to global mean and using it to drive a layer of recurrently connected depressing synapses. Within the barrel cortex, adaptation ensures that neurons are able to encode both overall levels of variance and large deviations in the input. This is achieved through a combination of gain modulation and a shift in sensitivity to intensity levels that are greater than the mean.