UC Berkeley

The supralinearity of neural responses shapes neurons’ activity. When depolarized close to threshold, they sensitively transduce small changes in their input; however, when hyperpolarized, they fail to spike despite large changes in their input. This means that a neuron which combines inputs from across sensory cortical space will nonlinearly encode stimuli, representing a conjunction of multiple stimuli differently from the sum of their parts. Further, via interaction with synaptic connections, this supralinearity has profound consequences for how signals propagate between nearby neurons. Signals might strongly propagate through highly active neurons in the network, while not propagating through inactive neurons. This shapes the functional interaction of neurons across sensory space: two neurons which cooperate under one stimulus condition, each mutually driving activity in the other, might compete in another condition. Further, the net effect of neural connections might be to supralinearly amplify some part of their inputs over another, shaping how their signals are added across space. In this work, we explore several questions related to the consequences of supralinearity for neurons’ encoding of stimuli of varying spatial extent and complexity.

First, we found a sparse but comprehensive population code for higher order tactile and visual features that depended on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different pyramidal cells (PCs) in primary somatosensory (S1) and primary visual (V1) cortex, summed most combinations of sensory inputs sublinearly, but integrated specific inputs supralinearly, leading to selective responses to higher order features. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

Next, in V1, PCs integrate widely across space when signals are weak, but narrowly when signals are strong, a phenomenon known as contrast-dependent surround suppression. Theoretical work has proposed that local interneurons could mediate a shift from cooperation to competition of PCs across cortical space, underlying this computation. Our calcium imaging and optogenetic data, along with computational modeling, showed that recurrent amplification drives a transition from a positive PC→VIP⊣SST⊣PC feedback loop at small size and low contrast to a negative PC→SST⊣PC feedback loop at large size and high contrast to contribute to this flexible computation. This may represent a widespread mechanism for gating competition across cortical space to optimally meet task demands.

Finally, this mechanism has been proposed to support integration of weak signals across space to support sensitive detection. For example, observers are better able to detect large low contrast stimuli than small low contrast stimuli, obeying a power law relationship between contrast sensitivity and stimulus size, and several psychophysical models have related the exponent in the power law relationship to the intrinsic nonlinearity of contrast coding in the visual system. We measured contrast sensitivity vs. stimulus size for the first time in mice and combined this with calcium imaging and optogenetics to constrain the role of V1 in this computation. V1 was necessary both for low contrast detection thresholds and, based on a psychophysical model, nonlinear encoding of contrast. This mechanism could provide a general framework for understanding sensory cortices’ role in flexibly assigning cognitive significance to arbitrary small environmental changes. Together, these studies of sensory encoding of space, ranging from the scale of synaptic connections to behavior, shed light on the role of nonlinearities in primary sensory cortex in sensory processing.