Lab

Lab for Neural Circuits and Behavior (Benucci Lab)

Institution: RIKEN

About the lab

In the Laboratory for Neural Circuits and Behavior we study the neural substrate of visual processing and vision-based decision making. To this end, we aim to define a research framework capable of linking neural architectures to the underlying computations. Our solution is to integrate experimental methods for all-optical dissection of neuronal circuits with large-scale dynamical network models based on artificial neural networks (aNNs). The connectivity architecture of aNNs closely mirrors that of biological neural networks, thus representing an effective theoretical framework to unify computational, algorithmic, and implementation levels of analysis.

Featured research (4)

During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.
Visually guided behaviors depend on the activity of cortical networks receiving visual inputs and transforming these signals to guide appropriate actions. However, non-retinal inputs, carrying motor signals as well as cognitive and attentional modulatory signals, also activate these cortical regions. How these networks integrate coincident signals ensuring reliable visual behaviors is poorly understood. In this study, we observe neural responses in the dorsal-parietal cortex of mice during a visual discrimination task driven by visual stimuli and movements. We find that visual and motor signals interact according to two mechanisms: divisive normalization and separation of responses. Interactions are contextually modulated by the animal’s state of sustained attention, which amplifies visual and motor signals and increases their discriminability in a low-dimensional space of neural activations. These findings reveal computational principles operating in dorsal-parietal networks that enable separation of incoming signals for reliable visually guided behaviors during interactions with the environment.
Visually-guided behaviors depend on the activity of cortical networks receiving visual inputs and transforming these signals to guide appropriate actions. However, non-retinal inputs, carrying motor signals as well as cognitive and attentional modulatory signals, also activate these cortical regions. How these networks avoid interference between coincident signals ensuring reliable visual behaviors is poorly understood. Here, we observed neural responses in the dorsal-parietal cortex of mice during a visual discrimination task driven by visual stimuli and movements. We found that visual and motor signals interacted according to two canonical mechanisms: divisive normalization and response demixing. Interactions were contextually modulated by the animal’s state of attention, with attention amplifying visual and motor signals and decorrelating them in a low-dimensional space of neural activations. These findings reveal canonical computational principles operating in dorsal-parietal networks that enable separation of incoming signals for reliable visually-guided behaviors during interactions with the environment.
Perception is an active process involving continuous interactions with the environment. During such interactions neural signals called corollary discharges (CDs) propagate across multiple brain regions informing the animal whether itself or the world is moving. How the interactions between concurrent CDs affect the large-scale network dynamics, and in turn help shape sensory perception is currently unknown. We focused on the effect of saccadic and body-movement CDs on a network of visual cortical areas in adult mice. CDs alone had large amplitudes, 3-4 times larger than visual responses, and could be dynamically described as standing waves. They spread broadly, with peak activations in the medial and anterior parts of the dorsal visual stream. Inhibition mirrored the wave-like dynamics of excitation, suggesting these networks remained E/I balanced. CD waves superimposed sub-linearly and asymmetrically: the suppression was larger if a saccade followed a body movement than in the reverse order. These rules depended on the cognitive state of the animal: when the animal was most engaged in a visual discrimination task, cortical states had large variability accompanied by increased reliability in sensory processing and a smaller non-linearity. Modeling results suggest these states permit independent encoding of CDs and sensory signals and efficient read-out by downstream networks for improved visual perception. In summary, our results highlight a novel cognitive-dependent arithmetic for the interaction of non-visual signals that dominate the activity of occipital cortical networks during goal-oriented behaviors. These findings provide an experimental and theoretical foundation for the study of active visual perception in ethological conditions.

Lab head

Andrea Benucci
Department
  • Center for Brain Science
About Andrea Benucci
  • www.benuccilab.net