Identification of a stress-sensitive anorexigenic neurocircuit from medial prefrontal cortex to lateral hypothalamus in humans and mice

Animal models that examine neural circuits controlling food intake often lack translational relevance. To address this limitation, we identified neural network dynamics related to homeostatic state and BMI in humans. This approach predicted a novel pathway projecting from the medial prefrontal cortex (mPFC) to the lateral hypothalamus (LH) in humans. We then dissected the mechanistic underpinnings of this human-relevant mPFC-LH circuit in mice. Chemogenetic or optogenetic activation of the mPFC-LH pathway in mice suppressed food intake and motivated sucrose-seeking. Fibre photometry demonstrated this pathway was active in response to acute stress or prior to novel environment or object exposure, suggesting a role in the predictive assessment of potential threat. Food consumption suppressed mPFC-LH neuronal activity, independent of metabolic state or palatability. Finally, inhibition of this circuit increased feeding and motivated behaviour under mild stress and chronic ablation caused weight gain. These studies identify the mPFC-LH as a novel stress-sensitive anorexigenic neural pathway involved in the cortical control of food intake and motivated reward-seeking.

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Animal behavior emerges from a seamless interaction between musculoskeletal elements, neural network dynamics, and the environment. Accessing and understanding the interplay between these intertwined systems requires the development of integrative neuromechanical simulations. Until now, there has been no such simulation framework for the widely studied model organism, Drosophila melanogaster. Here we present NeuroMechFly, a data-driven computational model of an adult female fly that is designed to synthesize rapidly growing experimental datasets and to test theories of neuromechanical behavioral control. NeuroMechFly combines a set of modules including an exoskeleton with articulating body parts---limbs, halteres, wings, abdominal segments, head, proboscis, and antennae---muscle models, and neural networks within a physics-based simulation environment. Using this computational framework, we (i) predict the minimal limb degrees-of-freedom needed for real Drosophila behaviors, (ii) estimate expected contact reaction forces, torques, and tactile signals during replayed Drosophila walking and grooming, and (iii) discover neural network and muscle parameters that can drive tripod walking. Thus, NeuroMechFly is a powerful testbed for building an understanding of how behaviors emerge from interactions between complex neuromechanical systems and their physical surroundings.

Inferring the function performed by a recurrent neural network

A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.

Robust Cortical Criticality and Diverse Neural Network Dynamics Resulting from Functional Specification

Despite recognized layered structure and increasing evidence for criticality in the cortex, how the specification of input, output and computational layers affects the self-organized criticality has been surprisingly neglected. By constructing heterogeneous structures with a well-accepted model of leaky neurons, we found that the specification can lead to robust criticality almost insensitive to the strength of external stimuli. This naturally unifies the adaptation to strong inputs without extra synaptic plasticity mechanisms. Presence of output neurons constitutes an alternative explanation to subcriticality other than the high frequency inputs. Degree of recurrence is proposed as a network metric to account for the signal termination due to output neurons. Unlike fully recurrent networks where external stimuli always render subcriticality, the dynamics of networks with sufficient feed-forward connections can be driven to criticality and supercriticality. These findings indicate that functional and structural specification and their interplay with external stimuli are of crucial importance for the network dynamics. The robust criticality puts forward networks of the leaky neurons as a promising platform for realizing artificial neural networks that work in the vicinity of critical points.