Tavoni Lab | Google Scholar Profile
Research
The Tavoni Laboratory investigates the fundamental principles underlying brain function, from sensing the environment to forming memories and making decisions. We study how information is represented and processed within brain networks to optimize behavior in diverse environments. Our theories provide normative predictions about the organization and function of neural circuits, and we integrate these theories with biophysically inspired models and neural data. We strive to develop unified frameworks that connect computational, algorithmic and implementational perspectives. Ultimately, uncovering the principles of neural coding is essential for understanding disruptions in brain diseases.
Areas of focus in the lab include:
Unveiling neural coding principles via unified multi-level theories
Animals constantly receive sensory stimuli, which are processed by neural networks to generate behavior. While sensory and decision-making circuits are typically studied separately, they work synergistically in the brain: different sensory modalities influence each other, and behavioral goals shape representations at all levels, from the sensory periphery to decision-making centers. We are interested in understanding the principles of neural coding in interacting networks.
Efficient and predictive coding in multimodal sensory networks
Recently, we developed a normative coding theory for feedback-modulated canonical networks — a ubiquitous excitatory-inhibitory motif in the brain. Feedback in these networks transmits information from one sensory modality to another. The theory offers a unified perspective on efficient and predictive coding, computational and algorithmic principles, and unimodal and multimodal sensory processing [Tavoni, BioRxiv 2025].
First, we showed that in feedback-modulated canonical networks, efficient coding automatically generates predictive codes, where stimuli from one modality can serve as predictors for stimuli in another modality. Cross-modal predictive computations are an emergent property of efficient codes. Second, we demonstrated how efficient and predictive computations are concurrently supported by a shared neural substrate and how different network components can implement these computations at the algorithmic level. Third, we provided a normative explanation for a class of observed multimodal receptive fields while integrating previous knowledge of unimodal processing as a special case within a broader, unitary framework. The theory also makes new predictions that can be tested experimentally, including an “inverse effectiveness” phenomenon, where feedback preferentially enhances the separation of sensory representations that are less discriminable.
We are now focused on generalizing this framework to other types of interacting networks to study various contextual effects on neural coding.
Understanding how optimal codes are implemented in the brain via neuroplasticity
The brain continually adapts its structure to optimally represent and respond to environmental inputs. An active area of research in the lab focuses on uncovering how various mechanisms, including synaptic plasticity, neurogenesis (as seen in the olfactory system) and other forms of structural plasticity, along with neuromodulatory processes, contribute to shaping optimal neural codes. These efforts aim to connect computational and implementational perspectives on neural coding.
Network analyses of neural circuits for economic decisions
The goal of this project, conducted in collaboration with the Padoa-Schioppa Lab, is to illuminate the principles that govern how the values of goods are encoded in the brain and how they are compared within neural networks to drive economic decisions. We use techniques based on maximum entropy inference and non-stationary models to reconstruct the functional circuits in the orbitofrontal cortex (OFC), which play a crucial role in economic decision-making. In parallel, we develop and test theories on how these circuits support the optimal coding of values and choices. Since economic choice behavior is specifically disrupted in a range of clinical disorders, understanding the neural mechanisms underlying this behavior is important from both scientific and medical perspectives.
Bayesian and complexity theories of high-level cognition
We continuously gather noisy data through our senses to make inferences about past, present and future states of the world. Accessible information, time and resources are limited and constrain the accuracy and complexity of viable inference strategies. The lab develops normative theories to understand how efficient inference processes adapt their complexity to environmental uncertainty and task demands [Tavoni, Doi, Pizzica, Balasubramanian, Gold, Nat Hum Behav 2022]. We recently identified a hierarchical (nested) organization in a wide range of models, from Bayesian probabilistic strategies to simpler, heuristic update processes that are often described as implementing a “model-free” form of learning. By studying this hierarchy, we identified two fundamental principles: (a) a power law of diminishing returns, whereby increasing computational complexity and cognitive effort gives progressively smaller gains in accuracy, and (b) a non-monotonic relationship between cognitive demands and statistical uncertainty in the world, such that complex inference strategies are necessary only in a relatively narrow range of intermediate-noise environments.
Memory optimization in brain-inspired heterogeneous networks
To acquire knowledge, we need effective ways to store and access information in the brain. In this project, we investigate how neural networks can optimize their memory capacity. Earlier research has primarily focused on estimating the capacity of homogeneous Hopfield networks, but biological neural networks are largely heterogeneous. In a recent study [Zhang, Tavoni, BioRxiv 2024], we calculated — using methods from the statistical mechanics of spin glasses — the capacity of a broader class of brain-inspired networks with heterogeneous connectivity and arbitrary coding levels (i.e., varying activation rates of neurons in memory patterns). This result allows us to make normative predictions about the conditions that optimize memory function in these networks. We predicted that the number of inward connections and the coding levels of neurons must be correlated to maximize capacity. We also found that an optimal memory encoding strategy in bipartite networks, schematically representing the CA3-DG circuit in the hippocampus, is to use a sparse code in the DG to “index” (i.e., bind) neurons in CA3, which stores the informational context of memories. Ultimately, understanding the fundamental principles of memory function is crucial for identifying specific therapeutic targets for Alzheimer’s disease and other conditions that cause memory decline.
If you are interested in joining the lab as a postdoc or graduate student, please email Gaia Tavoni at gaia.tavoni@wustl.edu.
Selected publications and preprints
- Tavoni G. Convergence of efficient and predictive coding in multimodal sensory processing. BioRxiv. 2025. doi: 10.1101/2025.02.24.639817.
- Zhang K, Tavoni G. Maximizing memory capacity in heterogeneous networks. BioRxiv. 2024; doi: 10.1101/2024.09.25.615056.
- Tavoni G, Doi T, Pizzica C, Balasubramanian V, Gold JI. Human inference reflects a normative balance of complexity and accuracy. Nature Human Behaviour. May 30, 2022; doi: 10.1038/s41562-022-01357-z.
- Tavoni G, Kersen DEC, Balasubramanian V. Cortical feedback and gating in odor discrimination and generalization. PLoS Computational Biology. Oct 11, 2021; 17(10): e1009479. doi: 10.1371/journal.pcbi.1009479.
- Tavoni G, Balasubramanian V, Gold JI. What is optimal in optimal inference? Current Opinion in Behavioral Sciences. 2019; 29:117-126.
- Tavoni G, Ferrari U, Battaglia FP, Cocco S, Monasson R. Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity. Network Neuroscience. 2017; 1(3):275-301.
- Cocco S, Monasson R, Posani L, Tavoni G. Functional networks from inverse modeling of neural population activity. Current Opinion in Systems Biology. 2017; 3:103-110.
- Tavoni G, Cocco S, Monasson R. Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings. Journal of Computational Neuroscience. 2016; 41(3): 269-293.
Education
2010, BS in Physical Engineering, Polytechnic of Turin
2012, International MS in Physics of Complex Systems, International university consortium (Polytechnic of Turin, International School for Advanced Studies and International Centre for Theoretical Physics of Trieste, Universities Pierre & Marie Curie, Paris Diderot, Paris-Sud and École Normale Supérieure at Cachan)
2015, PhD in Physics, École Normale Supérieure (Paris), Laboratories of Statistical and Theoretical Physics
2020, Post-doc in Theoretical Neuroscience, University of Pennsylvania, Computational Neuroscience Initiative
Selected honors
2024, Sloan Fellowship Award
2024, PRX Life Reviewer Excellence Award
2017, Swartz Foundation Fellowship Award for Theory in Neuroscience
2015, Computational Neuroscience Initiative Postdoctoral Fellowship
2010, admitted to the Alta Scuola Politecnica (declined)