Sparse Representation of Sounds in the Unanesthetized Auditory Cortex

By T. Hromádka, Michael Robert DeWeese, Anthony M. Zador. PLoS Biology, 2008

605 citations recorded by the source corpus.

How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

Subfields: Cognitive Neuroscience, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/3305b7f1b715d6037f34c9882cf0fb3d6ea87975

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Citations in the corpus

Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation. (2010, background): “Studies of both mammalian auditory cortex (Hromadka et al., 2008) and the avian song system (Theunissen et al., 2001) indicate that neural responses are highly selective for features present in natural sounds.”
Neuronal circuits underlying persistent representations despite time varying activity. (2012, result): “The distribution of spontaneous firing rates in the sparse FEVER network has a heavy tail (Figure 5B), similar to that observed in cortical neurons [46].”
Finding Neurons in a Haystack: Case Studies with Sparse Probing (2023, background): “…different coding strategies for different functions bears resemblance to the diverse coding strategies employed by biological neural networks in different brain regions with different functional roles (Olshausen & Field, 1996; Hromádka et al., 2008; Johnson & Leon, 2000; Leutgeb et al., 2005).”
Spontaneous events outline the realm of possible sensory responses in neocortical populations (2009, methodology): “For awake head-fixed experiments, we used an incremental training procedure (see, e.g., de Kock and Sakmann, 2008; Hromadka et al., 2008; Luczak et al., 2007; Robbe et al., 2006).”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “These face three major challenges (Rey et al., 2015): First, they must reliably capture the activity of simultaneously recorded neurons whose firing rates can vary over many orders of magnitude (Hromádka et al., 2008; Mizuseki and Buzsáki, 2013).”
Energy limitation as a selective pressure on the evolution of sensory systems (2008, background): “Strategies to improve the energy efficiency of neural coding are not restricted to single neurons but can also occur within populations of neurons (Levy and Baxter, 1996; Vinje and Gallant, 2000; Balasubramanian et al., 2001; Willmore and Tolhurst, 2001; De Polavieja, 2002; Perez-Orive et al., 2002;”
Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex (2009, positive): “Of particular interest, however, is a recent study by Hromadka et al. (2008), who used cell-attached recording techniques in auditory cortex of unanesthetized rats, but reported only modest differences in spontaneous and evoked responses across layers.”
Modulation of visual responses by behavioral state in mouse visual cortex. (2010, neutral): “It is not clear whether the lower firing rates are a result of different experimental preparations or technology or whether they are simply a characteristic of rodent cortex (Hromádka et al., 2008).”
High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays (2018, positive): “Unsurprisingly, units displayed a large range and diversity of firing rates throughout a day (Hromadka et al., 2008; Mizuseki and Buzsaki, 2013; O'Connor et al., 2010).”
SLEP: Sparse Learning with Efficient Projections (2011, background): “In neuroscience, the neural representation of sounds in the auditory cortex of unanesthetized animals is sparse, since the fraction of neurons that are active at a given instant is typically small [20].”
Sparse, Decorrelated Odor Coding in the Mushroom Body Enhances Learned Odor Discrimination (2014, neutral): “n, left to right, given as number of flies [number of experiments]: 23 [6], 26 [6], 16 [7], 44 [7], 18 [8], 55 [8], 32 [9], 51 [9].”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, neutral): “5 B ; cf. Hroma´dka et al., 2008).”