Nonlinear computations shaping temporal processing of precortical vision

Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

Diverse neuro-imaging techniques measure different aspects of neural responses with distinct spatial and temporal resolutions. Relating measured neural responses across different methods has been challenging. Here, we take a step towards overcoming this challenge, by comparing the nonlinearity of neural dynamics measured across methods. We used widefield voltage-sensitive dye imaging (VSDI) to measure neural population responses in macaque V1 to visual stimuli with a wide range of temporal waveforms. We found that stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from the strong sub-additive dynamics previously measured using fMRI and electrocorticography (ECoG) in human visual cortex with a similar set of stimuli. To test whether this discrepancy is specific to VSDI-a signal dominated by subthreshold neural activity, we repeated our measurements using widefield imaging of a genetically encoded calcium indicator (GcaMP6f)-a signal dominated by spiking activity, and found that GCaMP signals in macaque V1 are also near-additive. Therefore, the discrepancies in the extent of sub-additivity between the macaque and the human measurements are unlikely due to differences between sub- and supra-threshold neural responses. Finally, we use a simple yet flexible delayed normalization model to capture these different dynamics across measurements (with different model parameters). The model can potentially generalize to a broader set of stimuli, which aligns with previous suggestion that dynamic gain-control is a canonical computation contributing to neural processing in the brain.

Stimulus contrast modulates burst activity in the lateral geniculate nucleus

Burst activity is a ubiquitous feature of thalamic neurons and is well documented for visual neurons in the lateral geniculate nucleus (LGN). Although bursts are often associated with states of drowsiness, they are also known to convey visual information to cortex and are particularly effective in evoking cortical responses. The occurrence of thalamic bursts depends on (1) the inactivation gate of T-type Ca²⁺ channels (T-channels), which become de-inactivated following periods of increased membrane hyperpolarization, and (2) the opening of the T-channel activation gate, which has voltage-threshold and rate-of-change (δv/δt) requirements. Given the time/voltage relationship for the generation of Ca²⁺ potentials that underlie burst events, it is reasonable to predict that geniculate bursts are influenced by the luminance contrast of drifting grating stimuli, with the null phase of higher contrast stimuli evoking greater hyperpolarization followed by a larger dv/dt than the null phase of lower contrast stimuli. To determine the relationship between stimulus contrast and burst activity, we recorded the spiking activity of cat LGN neurons while presenting drifting sine-wave gratings that varied in luminance contrast. Results show that burst rate, reliability, and timing precision are significantly greater with higher contrast stimuli compared with lower contrast stimuli. Additional analysis from simultaneous recordings of synaptically connected retinal ganglion cells and LGN neurons further reveals the time/voltage dynamics underlying burst activity. Together, these results support the hypothesis that stimulus contrast and the biophysical properties underlying the state of T-type Ca²⁺ channels interact to influence burst activity, presumably to facilitate thalamocortical communication and stimulus detection.

Data-Driven Approaches to Understanding Visual Neuron Activity

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

Functional characterization of retinal ganglion cells using tailored nonlinear modeling

The mammalian retina encodes the visual world in action potentials generated by 20-50 functionally and anatomically-distinct types of retinal ganglion cell (RGC). Individual RGC types receive synaptic input from distinct presynaptic circuits; therefore, their responsiveness to specific features in the visual scene arises from the information encoded in synaptic input and shaped by postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing the computations reflected in RGC spike output. Therefore, we developed a statistical model, the separable Nonlinear Input Model, to characterize the excitatory and suppressive components of RGC receptive fields. We recorded RGC responses to a correlated noise ("cloud") stimulus in an in vitro preparation of mouse retina and found that our model accurately predicted RGC responses at high spatiotemporal resolution. It identified multiple receptive fields reflecting the main excitatory and suppressive components of the response of each neuron. Significantly, our model accurately identified ON-OFF cells and distinguished their distinct ON and OFF receptive fields, and it demonstrated a diversity of suppressive receptive fields in the RGC population. In total, our method offers a rich description of RGC computation and sets a foundation for relating it to retinal circuitry.

Contrast gain control and retinogeniculate communication

Visual information processed in the retina is transmitted to primary visual cortex via relay cells in the lateral geniculate nucleus (LGN) of the dorsal thalamus. Although retinal ganglion cells are the primary source of driving input to LGN neurons, not all retinal spikes are transmitted to the cortex. Here, we investigate the relationship between stimulus contrast and retinogeniculate communication and test the hypothesis that both the time course and strength of retinogeniculate interactions are dynamic and dependent on stimulus contrast. By simultaneously recording the spiking activity of synaptically connected retinal ganglion cells and LGN neurons in the cat, we show that the temporal window for retinogeniculate integration and the effectiveness of individual retinal spikes are inversely proportional to stimulus contrast. This finding provides a mechanistic understanding for the phenomenon of augmented contrast gain control in the LGN-a nonlinear receptive field property of LGN neurons whereby response gain during low-contrast stimulation is enhanced relative to response gain during high-contrast stimulation. In addition, these results support the view that network interactions beyond the retina play an essential role in transforming visual signals en route from retina to cortex.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.