Cognitive computational neuroscience nature.com/articles/s41593-0… 1/ Move beyond brain mapping. Mapping which brain regions activate during a task is not enough to explain cognition. We need computational models that reproduce behavior and explain how neural activity generates perception, learning, decision-making, and action. The goal is to understand the mechanism, not just the location. 2/ Build models that perform cognitive tasks. Allen Newell argued that understanding cognition requires constructing systems that actually perform the functions we want to explain. A theory of decision-making, for example, should predict choices under different conditions—not merely describe them verbally. Simulations help test whether proposed mechanisms work together to produce intelligent behavior. 3/ Integrate three disciplines. Cognitive science explains the computations underlying thought; computational neuroscience investigates how neural circuits implement those computations; AI develops algorithms that perform complex tasks. Each provides different constraints. Combining them could produce models that are simultaneously behaviorally effective, computationally explicit, and biologically grounded. 4/ Neural networks are a bridge between brains and AI. Deep neural networks learn hierarchical representations that can resemble those observed in biological sensory systems. Research has shown that networks trained for object recognition can predict aspects of visual cortical representations. However, predictive similarity does not prove that the brain uses the same algorithms. Models must also be tested against neural and behavioral evidence. 5/ Biological neurons offer more than artificial units. Most conventional artificial neural networks use simplified units that combine inputs and apply nonlinear transformations. Biological neurons exhibit action potentials, dendritic computations, recurrent signaling, oscillations, and complex temporal dynamics. Incorporating these features into task-performing models may reveal computational principles that simpler AI architectures overlook. 6/ Neural decoding is not a complete explanation. Decoding asks what information can be extracted from neural activity. Encoding models predict neural responses; representational similarity analysis compares patterns of activity; connectivity models examine interactions. These methods constrain theories, but they do not necessarily explain how the brain transforms information to solve a task. Mechanistic models must connect representations to computation and behavior. 7/ Bayesian inference offers a theory of perception and reasoning. The brain must interpret noisy sensory information using prior knowledge about the world. Bayesian models formalize this process by combining prior beliefs with incoming evidence. Generative models go further by representing how hidden causes produce observations. They offer a framework for studying uncertainty, causal reasoning, concept formation, and the ability to learn from limited experience. 8/ Learning and control require multiple strategies. Reinforcement learning provides a framework for understanding how actions and feedback shape future behavior. Model-free approaches learn values or policies from experience; model-based approaches use an internal model to evaluate possible outcomes; episodic approaches reuse specific past experiences. Understanding how biological brains combine these strategies could inform more adaptive AI systems. 9/ Combine bottom-up and top-down approaches. Bottom-up neuroscience begins with neurons, circuits, and measured activity. Top-down approaches begin with a cognitive function and ask what computations are required to perform it. Neither is sufficient alone. Strong theories should explain how biological mechanisms support meaningful behavior—and use behavioral requirements to identify which neural mechanisms matter. 10/ The future is an integrated NeuroAI. A major challenge is to connect three levels: computational goals, representations and algorithms, and neurobiological implementation. This requires collaboration across neuroscience, cognitive science, mathematics, and AI, along with shared datasets, models, tasks, and evaluation methods. The ultimate ambition is to build systems that perform intelligent tasks while explaining how brains learn, represent knowledge, and adapt. The big idea: The next leap in AI and neuroscience may come from treating the brain not simply as a source of data, but as a system whose computational principles can be tested through models that connect neural mechanisms to intelligent behavior.

Real-time reinforcement for human-machine interface control cell.com/neuron/fulltext/S08… Summary 1. Real-time reinforcement improves motor performance: Personalized, closed-loop reinforcement feedback enhances force control during human–machine interface tasks. 2. Rapid learning: Fewer than 20 reinforcement trials produce immediate improvements in motor control. 3. Long-term retention: Improvements can persist after training, although retention gains were not observed after short training in chronic stroke patients. 4. Greater benefits with limited sensory feedback: Reinforcement is most effective when visual or somatosensory feedback is reduced. 5. Benefits for stroke patients: Real-time reinforcement improves online force control in chronic stroke patients under conditions of limited visual feedback. 6. Compensates for reduced feedback: Reinforcement helps maintain motor control when sensory information is sparse. 7. Promotes successful action selection: Reinforcement encourages individuals to repeat and exploit motor actions that produce successful outcomes. 8. Translational potential: This strategy could improve rehabilitation and control of human–machine interfaces, particularly for people with motor disabilities.

Programmable design of functional proteins from natural language nature.com/articles/s41586-0…

Advancing NeuroAI through developmental alignment cell.com/neuron/home SUMMARY: 1. Model the development of intelligence, not just its adult state. Current AI models primarily aim to replicate adult-level abilities. NeuroAI should also investigate how intelligence develops from infancy to adulthood to understand the mechanisms that produce sophisticated cognition. 2. Build AI architectures inspired by brain development. The human brain develops hierarchically, with sensory regions maturing before higher-level association areas. AI could adopt this principle by training early layers first and progressively developing higher-level layers, potentially improving robustness and learning efficiency. 3. Use developmental learning as a curriculum. Children encounter information in a natural progression from simple to complex. Training AI with gradually increasing complexity, such as blurry-to-clear images or simple-to-complex visual experiences, may improve generalization, robustness, and data efficiency. 4. Learn from children's limited but meaningful experiences. Unlike AI models trained on enormous datasets, children learn remarkably well from relatively few experiences. Training models on realistic, child-centered sensory data and age-appropriate experiences may help reduce their dependence on massive datasets. 5. Incorporate curiosity-driven learning and exploration. Children actively seek information that is neither too easy nor too difficult to understand. AI models could use predictive learning, information gain, and the ability to select their own experiences to develop more efficient and adaptive learning strategies. 6. Treat developmental milestones as benchmarks for AI. Children's perceptual and cognitive abilities emerge in a characteristic sequence. Evaluating whether AI models acquire similar capacities over training—not merely whether they achieve adult-level performance—could reveal whether their learning mechanisms resemble those of humans. 7. Use NeuroAI to discover the mechanisms of intelligence. By manipulating neural architecture, training experiences, and learning objectives in AI models, researchers can test which factors are necessary or sufficient for particular cognitive abilities. This approach can also help explain brain development, identify mechanisms of dysfunction, and inform future clinical research. 8. Integrate architecture, experience, and learning objectives. These three factors interact throughout development and should not be optimized independently. A developmentally informed approach could produce AI systems that are more biologically plausible, robust, energy-efficient, and capable of generalizing beyond their training data. Central message: To understand and reproduce human intelligence, NeuroAI must study not only what the adult brain can do, but also how the developing brain acquires those abilities. Childhood development offers a powerful blueprint for building more capable and biologically grounded AI systems.