Communication from Learned to Innate Olfactory Processing Centers Is Required for Memory Retrieval in Drosophila

Sensorimotor pathway controlling stopping behavior during chemotaxis in the Drosophila melanogaster larva

Sensory navigation results from coordinated transitions between distinct behavioral programs. During chemotaxis in the Drosophila melanogaster larva, the detection of positive odor gradients extends runs while negative gradients promote stops and turns. This algorithm represents a foundation for the control of sensory navigation across phyla. In the present work, we identified an olfactory descending neuron, PDM-DN, which plays a pivotal role in the organization of stops and turns in response to the detection of graded changes in odor concentrations. Artificial activation of this descending neuron induces deterministic stops followed by the initiation of turning maneuvers through head casts. Using electron microscopy, we reconstructed the main pathway that connects the PDM-DN neuron to the peripheral olfactory system and to the pre-motor circuit responsible for the actuation of forward peristalsis. Our results set the stage for a detailed mechanistic analysis of the sensorimotor conversion of graded olfactory inputs into action selection to perform goal-oriented navigation.

State-dependent plasticity of innate behavior in fruit flies

Behaviors are often categorized into innate or learned. Innate behaviors are thought to be genetically encoded and hardwired into the brain, while learned behavior is a product of the interaction between experience and the plasticity of synapses and neurons. Recent work in different models show that innate behavior, too, is plastic and depends on the current behavioral context and the internal state of an animal. Furthermore, these studies suggest that the neural circuits underpinning innate and learned behavior interact and even overlap. For instance, hunger modulates several innate behaviors relying in part on neural circuits required for learning and memory such as the mushroom body in the fruit fly. These new findings suggest that state-dependent innate behavior and learning rely on functionally and anatomically overlapping and shared neural circuits indicating a common evolutionary history.

Deep(er) Learning

Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptable to their environmental niche. A key process undergirding this ability to adapt is the process of learning. Although a complete characterization of the neural basis of learning remains ongoing, scientists for nearly a century have used the brain as inspiration to design artificial neural networks capable of learning, a case in point being deep learning. In this viewpoint, we advocate that deep learning can be further enhanced by incorporating and tightly integrating five fundamental principles of neural circuit design and function: optimizing the system to environmental need and making it robust to environmental noise, customizing learning to context, modularizing the system, learning without supervision, and learning using reinforcement strategies. We illustrate how animals integrate these learning principles using the fruit fly olfactory learning circuit, one of nature's best-characterized and highly optimized schemes for learning. Incorporating these principles may not just improve deep learning but also expose common computational constraints. With judicious use, deep learning can become yet another effective tool to understand how and why brains are designed the way they are.