Synaptic plasticity as a cortical coding scheme

Abstract

Processing of auditory information requires constant adjustment due to alterations of the environment and changing conditions in the nervous system with age, health, and experience. Consequently, patterns of activity in cortical networks have complex dynamics over a wide range of timescales, from milliseconds to days and longer. In the primary auditory cortex (AI), multiple forms of adaptation and plasticity shape synaptic input and action potential output. However, the variance of neuronal responses has made it difficult to characterize AI receptive fields and to determine the function of AI in processing auditory information such as vocalizations. Here we describe recent studies on the temporal modulation of cortical responses and consider the relation of synaptic plasticity to neural coding.

Figure 1

STRFs of AI neurons. a, Two examples of STRFs from single units recorded in anesthetized adult cat AI. Recordings were obtained with a multielectrode from separate cortical layers in the same penetration. STRFs were derived via STA of spike trains evoked by dynamic ripple stimuli, with each evoked spike occurring at time 0. Red and blue colors indicate increases and decreases in firing rate, respectively, from the spontaneous rate. Data adapted from [13]. b, STRFs derived from responses to natural sounds, obtained with whole-cell recordings in anesthetized rat auditory cortex. Adapted from [12]. c, STRF prediction from left auditory cortex of awake macaque. Top, prediction spectrogram from convolution of STRF and stimulus spectrogram. Bottom, predicted (black) and actual response (gray), with amplitude of the predicted response scaled to the actual response. Adapted from [15•].

Figure 2

Rapid adaptation of AI spiking and synaptic responses. a, Example of neuronal adaptation from cell-attached recording in anesthetized adult rat auditory cortex. Shown are spike rasters and peristimulus time histograms of spiking responses evoked by pairs of pure tones separated by various intervals. The first tone is presented at time 0. The spiking response was reduced at short inter-stimulus intervals, but the response recovered with longer intervals. Adapted from [27]. b, Example RRTF from a single-unit recording in anesthetized cat AI. This unit preferred repetition rates of ~10 Hz, with reduced response at both faster and slower rates of presentation. Adapted from [102]. c, Forward suppression of tone-evoked synaptic currents (top) and conductances (bottom) observed in a whole-cell voltage-clamp recording from rat auditory cortex in vivo. Adapted from [27]. d, Short-term depression of synaptic responses measured with paired whole-cell recordings in young mouse auditory cortical brain slices. Adapted from [103].

Figure 3

Stimulus-timing-dependent plasticity in V1. Top, subthreshold membrane potential response to flashed bar (grey box) before pairing with a postsynaptic spike after the initial peak (positive timing), and suprathreshold response to same flashed bar stimulus after pairing. Pairing was performed during down states (‘Dn-state stim’). Middle, number of spikes evoked per visual stimulus before and after pairing. Same cell as traces shown in top panel. Bottom, different neuron receiving negative spike timing to induce LTD (postsynaptic spike before the initial peak). Adapted from [67••].

Figure 4

Responses to vocalizations in rodent AI. a, Single-unit recording from awake rat AI, playing two vocalizations with similar spectrotemporal characteristics. Top, vocalization waveforms, spectrogram, and power spectrum. Bottom, responses of the same unit to each vocalization. Note the reliable and precise response to vocalization 2 and negligible response to vocalization 3. Adapted from [20••]. b, Cell-attached recordings from anesthetized mother and virgin mouse AI. Top, infant isolation call spectrogram. Middle, three example trials. Bottom, raster of 12 trials to the same call. Spikes in yellow are approximately synchronous within ~10 msec. Adapted from [5••].

Figure 5

Neuromodulation reduces AI inhibition. a, Nucleus basalis stimulation in adult rat AI rapidly reduces inhibition; excitation more slowly increases afterward. Adapted from [68]. b, Foot shock disinhibits mouse AI. Top, foot shock responses are reduced by nicotinic receptor antagonists. Bottom, foot shock transiently reduces spontaneous inhibitory events. Adapted from [88]. c, Locus coeruleus stimulation reduces ongoing inhibition in rat AI. Adapted from [90•]. d, Voltage-clamp recording from anesthetized virgin mouse AI. Pup call stimuli initially evoke imbalanced EPSCs and IPSCs. Pairing oxytocin with pup calls first reduced IPSCs, then strengthened EPSCs, and finally call-evoked inhibition balanced excitation after 30+ minutes. Adapted from [5••].