Neurotechnology company Eon Systems released this demonstration as a clear example of a brain model, based on a real biological connectome, controlling a physics-based body in a closed loop. More broadly, this work highlights a fast-growing area of neuroscience that is moving from static brain maps to digital systems where the brain, body, and environment interact. At the center of the work is the fruit fly, Drosophila melanogaster, a tiny insect that has become one of neuroscience’s most useful model organisms. Its brain is far smaller than a mammal’s, but still complex enough to support navigation, feeding, grooming, and other organized behaviors. That makes it a practical starting point for researchers trying to understand how a complete biological brain can be reconstructed and simulated in software. The groundwork for this new demonstration was set in 2024, when researchers published a computational model of the adult fruit fly brain with over 125,000 neurons and 50 million synaptic connections. The model was built using the FlyWire connectome and machine learning to predict neurotransmitter identity, according to the source materials. Philip Shiu, an Eon senior scientist, led this research. That earlier model was a major milestone, but it had a key limitation: it was essentially a brain without a body. While it could simulate neural activity and predict motor behavior, it did not work within a physical environment where signals could move from sensation to movement and back. The new demonstration addresses this by connecting the brain model to a simulated fly body using MuJoCo, a physics engine commonly used in robotics and simulation. The virtual fly shows behaviors like walking, grooming, and feeding. The main point is that these actions were not programmed as simple animations. Instead, the project description says they came from the brain model’s own neural circuits, as sensory input traveled through the connectome and motor output returned to the body. This is what makes the demonstration unique. Earlier research often focused on only one part of the problem. Some projects mapped nervous systems in detail but did not link them to an active body. Others built realistic simulated animals that could move well, but these were controlled by reinforcement learning or engineered control systems rather than by a brain model reconstructed from biological wiring. Eon says its latest work brings these elements together more fully. Scientists are able to simulate sensory inputs, such as the presence of sugar in front of the fly, and the model responds appropriately, for example by signaling the fly to stick out its tongue in the correct direction. When researchers say they simulate sensory input in this model, they do not mean that real sugar or smells are present. Instead they artificially activate the same sensory neurons that would normally fire when a stimulus is detected. For example, if sugar would normally trigger a specific taste receptor neuron, the simulation simply injects activity into that neuron as if sugar had been detected. The signal then propagates through the network according to the connectivity of the brain. If the wiring is correct, the activity eventually reaches the motor neurons responsible for extending the fly’s proboscis, the feeding tube that acts like a tongue. In this way the simulated brain produces the same response a real fly would produce when it detects sugar. However, the behaviour demonstrated in these models is more limited than popular summaries sometimes suggest. Fruit flies are capable of learning, remembering food locations, navigating environments, and performing complex behaviours, but the simulations so far have mostly demonstrated specific circuits such as feeding or sensory processing. The models do not yet show a full virtual fly flying through space, remembering past experiences, or performing complex navigation. What scientists have built is primarily a brain-wide network model that allows them to stimulate inputs and observe how signals propagate through the wiring. Another important limitation of these simulations concerns synaptic weights. A connectome map typically tells scientists which neurons connect to each other and how many synapses exist between them, but it does not directly reveal the exact strength of each synapse. Synaptic strength determines how strongly one neuron influences another, and these weights change continuously as learning occurs. In simulations researchers therefore approximate weights using available information. Often they assume that a connection with more synapses between two neurons is stronger than a connection with fewer synapses. They can also often determine whether a neuron is excitatory or inhibitory, which constrains whether the signal increases or suppresses activity in the receiving neuron. The neurons themselves are typically simulated using simplified models known as leaky integrate and fire neurons. These models accumulate input signals and produce spikes when a threshold is reached, approximating the behavior of real neurons without modeling the full complexity of biological ion channels. Even with approximate weights and simplified neurons, the overall structure of the network can still produce meaningful behavior. If sensory neurons connect through intermediate neurons to motor outputs in the correct pattern, stimulating the sensory neurons naturally produces activity in the motor neurons. Nevertheless, the connectome alone does not fully capture how a brain works. Synaptic strengths are not fixed and depend on learning and plasticity mechanisms such as long term potentiation and long term depression. The brain also relies on neurotransmitter chemistry involving molecules like glutamate, GABA, dopamine, serotonin, and acetylcholine, which influence how signals are transmitted and how neurons respond. Neuromodulators can change the activity of entire brain regions, altering states such as attention, motivation, or arousal. Inside each neuron thousands of genes regulate ion channels, receptors, and metabolic processes, meaning that neurons with identical wiring may still behave differently depending on their molecular state. Glial cells, which make up roughly half of the cells in the brain, also regulate synaptic activity and metabolic support. Furthermore, the brain constantly rewires itself through plasticity, meaning that any connectome map represents only a snapshot in time. A person’s brain wiring today will differ slightly from their wiring tomorrow as experiences modify synapses. Taken together, these efforts show both how far neuroscience has progressed and how much remains unknown. The fruit fly connectome demonstrates that it is possible to reconstruct the full wiring diagram of a brain and simulate how signals flow through its circuits. At the same time it highlights the enormous gap between knowing the structure of a brain and fully reproducing the dynamic processes that produce learning, memory, and complex behaviour. The connectome provides the skeleton of the system, but the living brain also depends on chemistry, plasticity, electrical dynamics, and developmental history. Understanding how all of these layers interact remains one of the deepest challenges in science. A Drosophila computational brain model reveals sensorimotor processing https://www.nature.com/articles/s41586-024-07763-9 submitted by /u/LongjumpingTear3675
Originally posted by u/LongjumpingTear3675 on r/ArtificialInteligence