Intracellular responses to frequency modulated tones in the dorsal cortex of the mouse inferior colliculus

Abstract

Frequency modulations occur in many natural sounds, including vocalizations. The neuronal response to frequency modulated (FM) stimuli has been studied extensively in different brain areas, with an emphasis on the auditory cortex and the central nucleus of the inferior colliculus. Here, we measured the responses to FM sweeps in whole-cell recordings from neurons in the dorsal cortex of the mouse inferior colliculus. Both up- and downward logarithmic FM sweeps were presented at two different speeds to both the ipsi- and the contralateral ear. Based on the number of action potentials that were fired, between 10 and 24% of cells were selective for rate or direction of the FM sweeps. A somewhat lower percentage of cells, 6–21%, showed selectivity based on EPSP size. To study the mechanisms underlying the generation of FM selectivity, we compared FM responses with responses to simple tones in the same cells. We found that if pairs of neurons responded in a similar way to simple tones, they generally also responded in a similar way to FM sweeps. Further evidence that FM selectivity can be generated within the dorsal cortex was obtained by reconstructing FM sweeps from the response to simple tones using three different models. In about half of the direction selective neurons the selectivity was generated by spectrally asymmetric synaptic inhibition. In addition, evidence for direction selectivity based on the timing of excitatory responses was also obtained in some cells. No clear evidence for the local generation of rate selectivity was obtained. We conclude that FM direction selectivity can be generated within the dorsal cortex of the mouse inferior colliculus by multiple mechanisms.

Rest mutant zebrafish swim erratically and display atypical spatial preferences

“…The rest heterozygotes larvae also present with hypo-locomotion suggesting that there is an early heterozygote effect on behavior. A similar hypo-locomotion phenotype has also been described in the zebrafish mecp2 mutant [36]. Mecp2 is a methyl binding protein that associates with Rest to silence target genes [7].…”

Closed-Loop and Activity-Guided Optogenetic Control

“…One-photon structured illumination methods usable for multicell perturbational approaches include laser scanning (Wilson et al, 2012), micro-LED array (Grossman et al, 2010), digital micromirror device (DMD; Dhawale et al, 2010), light field (Levoy et al, 2009; Figure 5A), and holographic (Lutz et al, 2008; Szabo et al, 2014) illumination. In the case of two-photon imaging approaches used for activity observation, TPLSM (Denk et al, 1994), two-photon extended depth of field (EDoF; Quirin et al, 2013), and two-photon 3D random access scanning (Fernández-Alfonso et al, 2014; Cotton et al, 2013; Katona et al, 2012; Grewe et al, 2010; Duemani Reddy et al, 2008; Otsu et al, 2008; Iyer et al, 2006) all provide two and three-dimensional approaches to functional imaging. Two-photon-based methods for optical perturbation have used either laser scanning (Prakash et al, 2012; Rickgauer and Tank, 2009), temporal focusing (Rickgauer et al, 2014; Andrasfalvy et al, 2010), digital holography (Packer et al, 2012, 2015; Nikolenko et al, 2007), or a combination of digital holography and temporal focusing (Oron et al, 2012; Papagiakoumou, 2013; Bègue et al, 2013) for patterned optogenetic stimulation.…”

“…In the state-space formulation for neural data (Paninski et al, 2010), actual spiking activity of the neurons is hidden through a measurement process that involves both measurement noise and other measurement limitations, such as convolution of the spikes with slower calcium dynamics in the case of calcium imaging data. In this situation, if one is explicitly interested in the spiking data, it is necessary to infer the times or probabilities of individual action potentials using deconvolution (Andilla and Hamprecht, 2014; Vogelstein et al, 2009, 2010; Yaksi and Friedrich, 2006), template matching (Lütcke et al, 2013; On˜ ativia et al, 2013; Grewe et al, 2010; Greenberg et al, 2008; Kerr et al, 2007), or supervised learning algorithms trained on labeled data (Theis et al, 2014; Sasaki et al, 2008). …”

“…Several studies have shown that at high frame rates (>30 Hz) network information can be estimated from such inferred population spike dynamics (Fletcher and Rangan, 2014; Lütcke et al, 2013; Stetter et al, 2013; Mishchenko et al, 2011; Vogelstein et al, 2010). Indeed, simulations by Lutke et al suggest that connectivity between neurons can be partially inferred from limited observational calcium imaging data, limited sampling from the population, and in the context of fluctuating, unobserved, common input (Lütcke et al, 2013).…”

“…Indeed, simulations by Lutke et al suggest that connectivity between neurons can be partially inferred from limited observational calcium imaging data, limited sampling from the population, and in the context of fluctuating, unobserved, common input (Lütcke et al, 2013). However, these important theoretical studies were focused primarily on the recovery of estimated synaptic connectivity offline under somewhat idealized conditions and did not optimize for speed, or test how well these estimates performed in recapitulating circuit dynamics—critical considerations for model validation and control.…”