Rapid Sensory Adaptation Redux: A Circuit Perspective

Abstract

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

Figure 1. Concepts of adaptation span from intrinsic currents in a single neuron to the perception of a sensory stimulus

In this perspective, we aim to expand our neurophysiological understanding of adaptation at the single neuron level to a circuit level representation of adaptation as a method of determining the underlying neural correlates of the perceptual effects of adaptation. Brain image edited from Livingstone, BIODIDAC.

Figure 2. Intrinsic adaptation allows rescaling of the input-output function

A) Sensory encoding models have classically described two stages of transformation from the sensory stimulus to the spike output. The first stage can be envisioned as a filter stage where the sensory stimulus is filtered by the feature selectivity of a given neuron. The second stage can be described as an input-output stage that transforms the filtered stimulus input into a firing rate output. B) A neuron has a finite amount of spikes that can be elicited in a given time window. If a neuron is tuned to encode a finite number of potential stimuli, the neuron could encode each input linearly such that there is a one-to-one mapping between the input and the output. C. Realistically, a neuron will not encounter every possible stimulus in a short time window so the range of potential stimuli is likely restricted. Without adaptation, the input-output function for the neuron would not rescale which would limit the dynamic range of the outputs (left, grey shading). With adaptation, the input-output function can be rescaled to shift the dynamic firing range of the neuron to the stimulus range that is likely (right, grey shading). Neuron image adapted from Ramon y Cajal (1889).

Figure 3. Synaptic plasticity dynamically alters feature selectivity in an input-specific manner

A) The feature selectivity of a neuron is determined by its presynaptic inputs. In a simplistic scenario, the feature selectivity of a neuron (S, purple) could be the sum of the feature selectivity of its presynaptic inputs (s₁, s_2; blue, red respectively). B) When the synaptic drive from one presynaptic population (s₁, dashed) is adapted, the synapse projecting downstream may become depressed (grey). The reduced drive from one synaptic input will cause the feature selectivity of the downstream cortical neuron to momentarily shift away from the adapted population (purple, dashed).

Figure 4. Stimulus specific adaptation can be partially explained by the adaptation in narrow frequency model

A) The frequency tuning of a neuron in primary auditory cortex (A1) could be inherited from the frequency tuning of its presynaptic inputs. In an SSA paradigm, one off-peak frequency for the A1 is used as the ‘standard’ stimulus that is presented frequently (blue) while a second off-peak frequency is used as the infrequently presented ‘deviant’ stimulus (orange). Repetitive ‘standard’ stimulation will depress the feedforward synapses (grey). B) The evoked responses to the ‘deviant’ and ‘standard’ stimuli are approximately equal without adaptation (solid vertical lines). The evoked response to the ‘standard’ stimulus is greatly reduced when adapted, while the ‘deviant’ stimulus is largely unaffected (dashed vertical lines). C) It has been proposed that differential adaptation of the presynaptic populations that are tuned to the ‘standard’ stimulus will reduce the inputs from those neurons (dashed frequency tuning curves) and shift the frequency tuning of the A1 neuron (B, purple dashed line).

Figure 5. Feedforward inhibition in the thalamocortical and corticothalamic pathways of the rodent vibrissa system

The feedforward thalamocortical inputs from the ventral posteromedial (VPm) thalamus project to both excitatory and inhibitory neurons in layer 4 of primary sensory cortex (S1L4; solid lines). The corticothalamic feedback inputs from S1 L6 provide direct excitation to VPm and feedforward inhibition to VPm mediated through the reticular nucleus of the thalamus (nRT; dashed lines). While the exact properties of these feedforward and feedback pathways differ in detail, the similar feedforward inhibition motif leads to common adaptive properties.

Figure 6. Shifts in contrast adaptation can sharpen temporal dynamics of receptive fields

A) The feature selectivity of a neuron will incorporate both excitatory and inhibitory inputs. In this example, the inhibitory input is simply a temporally shifted version of the excitatory input (right, bottom). The subtraction of these two temporal features results in a biphasic feature selectivity (right, top). B) In the high contrast adapted condition, the excitatory and inhibitory filters are more temporally precise (right, bottom) leading to a temporally sharpened component of the receptive field (right, top).

Figure 7. Divisive normalization modulates the input-output tuning function

Although not typically described in conjunction with sensory adaptation, divisive normalization could also be considered a pooled adaptation effect where the firing activity across neurons can reshape the input-output function of an individual cell in an activity dependent manner. There are two methods of input-output scaling that have been proposed and demonstrated in different sensory pathways. A) Input scaling refers to shifting the input-output curve laterally while B) output scaling refers to scaling the input-output curve vertically. Both scaling mechanisms represent an activity dependent network mechanism to shift the encoding of sensory neurons.