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Waveform spikes were then sorted offline using Neuralynx spike sorting software (SpikeSort) as follows: first, each spike waveform consisted of a 1-ms window surrounding its peak amplitude. Second, for each spike, we defined two features, amplitude of the peak and amplitude of the valley, for each of the four electrodes within a tetrode (a total of 8 features). Clusters EPZ5676 chemical structure were then defined according to these feature distributions, manually selecting the dimensions that best separated different clusters. We used several criteria to include a neuron in our data set. First, we inspected the ISI distribution. A histogram of the ISI distribution for the spikes within each cluster is expected to show a refractory period, that is, a dearth of spikes that occur within milliseconds of each other (Hill et al., 2011). Therefore, only clusters in which none of the ISIs were less than 1 ms and less than 5% of the ISIs were smaller than 5 ms were considered for further examination as candidates for single-units, thus ensuring minimal contamination. Second, the clustered waveforms were also Oxacillin inspected by eye to exclude those with aphysiological shapes. The waveform shape and amplitude were examined across the duration of the recording to ensure stability and reject the possibility of contamination by multiple neurons or potential loss of a neuron at an intermediate time within the recording. Finally, we performed cross-correlation between each spike waveform and the averaged waveform, and specified that the averaged correlation coefficient must exceed 0.95. To ensure stable recordings, we confirmed that the correlation coefficients between spikes in the first and last 5 min of recordings were not significantly different than those between the same number of randomly selected spikes across the recording. Recording sites were also verified histologically with electrolytic lesions at the termination of the experiment, when possible, using 15�C20 ?s of 100?��A direct current, or by visualizing the optical fiber track (Figure 1A). We adapted recent methods TGF-beta inhibitor for optogenetic identification (Lima et al., 2009, Cohen et al., 2012, Kravitz et al., 2013) of well-isolated single-units, to classify these units into three categories, as follows. First, to classify AgRP neurons, we delivered blue-light photostimulation pulses at 20 Hz, a stimulation frequency shown to elicit feeding and as well as sustained spiking in ChR2-expressing AgRP neurons in vitro (Aponte et al., 2011). Specifically, we delivered 1-s-long trains of 20-ms light pulses at 20 Hz (wavelength: 473 nm; intensity: 5�C20 mW/mm2), with 3 s between pulse trains (typically 50�C100 trains were used at a given laser intensity). The laser beam was passed through a Pockels cell to ensure accurate control of laser pulse shape (