Synaptic plasticity as a cortical coding scheme

Highlights

• Adaptation occurs throughout the nervous system at different time scales.

• The relation between synaptic depression and forward masking is still unclear.

• Including synaptic depression into STRF models substantially improves predictions.

• STRF models with nonlinearities fail to predict responses to vocalizations.

• AI is highly plastic, and neuromodulation rapidly transforms vocalization responses.

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.

Introduction

Natural sounds such as speech and music are composed of acoustic signals that vary over time. Early lesion studies indicated that the auditory cortex is critical for recognition of temporal sequences of auditory stimuli [1, 2], supported by newer stimulation studies in behaving rodents [3, 4, 5••]. However, it is unclear how AI represents and encodes sequences of temporally complex sounds. Relative to the primary visual cortex (V1), for example, less is known about the construction of AI receptive fields and the function of AI in acoustic scene analysis [6, 7, 8]. Knowledge of AI receptive field organization and dynamics is essential for understanding the neural basis of auditory perception and vocal communication, and for improvement of training programs and prosthetic devices designed to rehabilitate damaged brains, for example, for recovery of language comprehension with cochlear implants after hearing loss [9, 10].

The functional organization of AI has remained somewhat obscure. In part, this lack of information is due to the shortcomings of current analysis methods, such as reverse correlation and spike-triggered averaging (STA), to produce spectrotemporal receptive fields (STRFs) that accurately predict the responses of AI neurons over a wide range of stimuli (Figure 1a,b). STA is a standard approach used to determine which features of a continuous, usually white-noise stimulus reliably produced spiking in a given cell [11]. While STRFs extracted from STA techniques using relatively stereotyped stimuli do well at predicting the responses to other similar stimuli such as pure tones or dynamic ripples [12, 13, 14, 15•], these STRFs do not provide good predictions of responses to natural sounds. STRFs of AI neurons seem to be able to account for ∼10–20% of the structure of spike trains evoked by vocalization patterns, even in awake macaques [15^•], with a high variance depending on the form of stimuli used. This indicates that the predicted and actual responses to natural stimuli are only weakly correlated (linear correlation coefficient r: ∼0.1). In V1, linear predictions are modestly better but still generally account for less than half the variance [16]. Importantly, STRFs are often best at capturing AI responses that slowly vary in time [12].

The rest of response variance is presumably due to factors that contribute to receptive field nonlinearity. To predict the neuronal response to sensory stimulation, a given stimulus is convolved with the STRF. Convolution is a linear operation similar to taking the moving average of the sensory stimulus, weighted by the time and frequency components of the STRF (Figure 1c). If this convolution-based prediction closely matches the observed response, then the receptive field can be called ‘linear’, and different aspects of the stimulus (e.g., frequency and time) are generally independent from each other. Conversely, deviations from linear predictions reflect the presence of nonlinearities, such as interactions between different stimulus components or history-dependent processes.

There are many potential biological sources of nonlinearity for cortical responses. For example, the membrane potential threshold for spike generation is a nonlinear component of any neuronal response. In V1, spike threshold may play a role in determining whether cells exhibit simple or complex receptive field structure [17]. In the auditory midbrain of birds and mammals, threshold can also play a part in shaping feature selectivity [18, 19]. Unfortunately, inclusion of spike threshold or other time-invariant (static) nonlinearities does not significantly improve STRF predictions of AI responses to natural stimuli [12, 20••]. This suggests that more complex, time-varying nonlinear factors could contribute to tuning properties of cortical neurons [17, 21••]. Here we discuss two such nonlinear phenomena — adaptation and plasticity — and their influence over the temporal dynamics of AI receptive fields. We consider three issues in detail: the involvement of short-term synaptic depression in spike train adaptation, the relevance of long-term synaptic modifications for receptive field plasticity, and the relationship between short-term and long-term forms of plasticity.