Rate-specific synchrony: Using noisy oscillations to detect equally active neurons

Departments of Molecular Biology and Physics, The Lewis Sigler Institute for Integrative Genomics, and Princeton Neuroscience Institute, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08544, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 07/2008; 105(24):8422-7. DOI: 10.1073/pnas.0803183105
Source: PubMed

ABSTRACT Although gamma frequency oscillations are common in the brain, their functional contributions to neural computation are not understood. Here we report in vitro electrophysiological recordings to evaluate how noisy gamma frequency oscillatory input interacts with the overall activation level of a neuron to determine the precise timing of its action potentials. The experiments were designed to evaluate spike synchrony in a neural circuit architecture in which a population of neurons receives a common noisy gamma oscillatory synaptic drive while the firing rate of each individual neuron is determined by a slowly varying independent input. We demonstrate that similarity of firing rate is a major determinant of synchrony under common noisy oscillatory input: Near coincidence of spikes at similar rates gives way to substantial desynchronization at larger firing rate differences. Analysis of this rate-specific synchrony phenomenon reveals distinct spike timing "fingerprints" at different firing rates that emerge through a combination of phase shifting and abrupt changes in spike patterns. We further demonstrate that rate-specific synchrony permits robust detection of rate similarity in a population of neurons through synchronous activation of a postsynaptic neuron, supporting the biological plausibility of a Many Are Equal computation. Our results reveal that spatially coherent noisy oscillations, which are common throughout the brain, can generate previously unknown relationships among neural rate codes, noisy interspike intervals, and precise spike synchrony codes. All of these can coexist in a self-consistent manner because of rate-specific synchrony.

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Available from: John J Hopfield, Sep 12, 2014
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    • "We confirm this somewhat counterintuitive prediction with recordings from regularly firing mitral cells of the main olfactory bulb. In addition to heterogeneity in response properties, neurons can fire at different frequencies, and such frequency differences can significantly impact correlatednoise induced synchronization (Markowitz et al., 2008; Burton et al., 2012). Here, we find that for some frequency differences between oscillators, there is an optimal time scale of correlated noise that will maximally synchronize the oscillators. "
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    ABSTRACT: Synchronization plays an important role in neural signal processing and transmission. Many hypotheses have been proposed to explain the origin of neural synchronization. In recent years, correlated noise-induced synchronization has received support from many theoretical and experimental studies. However, many of these prior studies have assumed that neurons have identical biophysical properties and that their inputs are well modeled by white noise. In this context, we use colored noise to induce synchronization between oscillators with heterogeneity in both phase-response curves and frequencies. In the low noise limit, we derive novel analytical theory showing that the time constant of colored noise influences correlated noise-induced synchronization and that oscillator heterogeneity can limit synchronization. Surprisingly, however, heterogeneous oscillators may synchronize better than homogeneous oscillators given low input correlations. We also find resonance of oscillator synchronization to colored noise inputs when firing frequencies diverge. Collectively, these results prove robust for both relatively high noise regimes and when applied to biophysically realistic spiking neuron models, and further match experimental recordings from acute brain slices.
    Frontiers in Computational Neuroscience 08/2013; 7:113. DOI:10.3389/fncom.2013.00113 · 2.23 Impact Factor
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    • "Specifically, a single neuron equipped with STDP (Figure 8) can robustly detect a hidden pattern repeating at random intervals, which involves only a subset of its afferents, and is automatically encoded in their firing phases (Figure 9). The oscillatory drive improves the spike time precision by decreasing their sensitivity to initial conditions, and avoiding jitter accumulation, so that they depend mainly on the current input values (Brette and Guigon, 2003; Hasenstaub et al., 2005; Schaefer et al., 2006; Markowitz et al., 2008). The ability of STDP to detect repeating spike patterns had been noted before in continuous activity (Masquelier et al., 2008, 2009a), but it turns out that oscillations greatly facilitate learning, which is possible even when only a small fraction of the afferents (∼10%) exhibits PoFC. "
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    Frontiers in Psychology 06/2011; 2:151. DOI:10.3389/fpsyg.2011.00151 · 2.80 Impact Factor
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    • "However, the functional role of correlated activity is not fully understood. Theoretical tools can guide our efforts to understand the impact of spatiotemporal activity structure on neural coding (Averbeck and Lee 2006; de la Rocha et al. 2007; Gutnisky and Dragoi 2008; Markowitz et al. 2008; Moreno et al. 2002; Poort and Roelfsema 2009; Romo et al. 2003; Salinas and Sejnowski 2000, 2001; Shea-Brown et al. 2008). "
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