Topic: area MT

Causal inference (CI) has been proposed as a universal computational motif in the brain [Shams & Beierholm 2020]. However, its neural implementation is unclear. Likewise, Divisive Normalization (DN) has been proposed as a canonical circuit motif [Heeger 2011], but there are competing theories about the computations that DN implements (gain control, attention). In this work, we unified both by showing how an extended DN model can account for neural predictions made by a CI model [Shivkumar et al. 2022] in the context of hierarchical motion perception. Specifically, we generated CI predicti

Causal inference (CI) has recently been proposed as a universal computational motif in the brain [Shams \& Beierholm 2020]. However, how CI is implemented by neural circuits, and its signatures in terms of single neuron responses, are still unclear. We have investigated this question in the context of complex motion processing. Motion perception deviates from retinal motion [Johansson 1973] a computation that can be understood in terms of hierarchical CI over which moving elements to integrate into coherent 'groups' vs segment into different ones [Gershman et al. 2016, Shivkumar et al. 202

Neuronal sensitivity and selectivity to our environment arise from the transformation of external inputs by the cortical circuitry. We dissect the circuitry for this emergence using a combination of large-scale simultaneous measurements from single cells in awake animals and computational models. In area MT, an area of the neocortex that processes visual motion, even weak motion signals evoke selective responses in the presence of noise. We also observe tuned patterns of activity in the absence of visual motion that suggest feature-selective amplification within the circuit. In addition, respo

Flexible decision making is an indispensable ability for humans. Although neurons in decision-related cortex accumulate relevant information preferentially depending on context, how sensory information represented in sensory cortex is conveyed to decision-related cortex remains unclear. To elucidate this question, we investigated the functional connectivity between sensory cortex (area MT) and decision-related cortex (area LIP) by recording simultaneously from both areas using two electrodes while monkeys performed a switching task.Two Japanese macaques were trained to flexibly switch between

Perceptual sensitivity varies from moment-to-moment with fluctuations in the gain of sensory-evoked responses. We previously found that intrinsic traveling waves (iTWs) of activity in marmoset Area MT regulate the gain of evoked responses for targets presented near perceptual threshold and correlate with the monkey’s detection performance (Davis, Muller, et al. Nature 2020; Davis, Benigno, et al. Nat. Communi. 2021). Here we ask whether target detection also depends on the spiking activity of neurons beyond the retinotopic locus of the target (off-target locations). We hypothesized that activi