The course consists of two modules A) Ecology and evolution The goals of this module are both to teach students fundamental concepts in ecology and evolution and provide them with basic notions and tools in dynamical systems and stochastic processes. The module will be divided into two main parts. The first part will be devoted to introducing the students with classic models in population and community dynamics. The second part of the module will focus on evolutionary theory and population genetics. We will discuss classic and modern experimental evidence of Darwinian (micro)evolution and the observational pieces of evidence of macroevolution. The students will study the effect of selection, mutations, and drift on the fixation of neutral, beneficial, and deleterious mutations in the context of simple stochastic models of population genetics. B) Introduction to systems and computational neuroscience The module covers a range of fundamental ideas in systems and computational neuroscience, with a particular focus on sensory perception.

A) Ecology and evolution At the end of the first part of the module, the students are expected to be able to quantitatively model community dynamics in the presence of different interaction types, identifying and justifying the important assumptions. They should also be able to perform stability analysis, identify the presence of bifurcations and have basic notions of limit cycles and chaos. At the end of the second part of the module, the student will know how to quantify the relative importance of the fundamental mechanisms of evolution (reproduction, mutation, selection, genetic drift and recombination) using the tools of stochastic processes. B) Introduction to systems and computational neuroscience Students will be familiar with the core ideas in the neuroscience of vision and touch, and in the Bayesian theory of perception. They will be able to navigate and interpret the modern research literature on vision, touch, and Bayesian models of perception.

A) Ecology and evolution Calculus, Basics of Linear Algebra, Basics of Probability B) Introduction to systems and computational neuroscience No special requirements. The course is self-contained, and all relevant prerequisites in mathematics, probability, and biology are introduced during the lectures.

A) Ecology and evolution [J.Grilli] 1. Single population dynamics (exponential growth, logistic growth, Allee effect) 2. Two species dynamics. Fixed points and stability. Lotka-Volterra 3. Multispecies communities 4. Experimental and observational evidence of evolution 5. Genetic Drift 6. Mutation and Selection 7. Coevolution B) Introduction to systems and computational neuroscience [D. Zoccolan, M. Diamond, E. Piasini] Part 1. Physiology and functions of the mammalian visual system Introduction to anatomy and physiology of the visual system A systems/computational approach to the study of the visual system; Anatomy of the visual system Classic findings about physiology of lower-level visual areas Data analysis approaches in Systems Neuroscience Classic findings about physiology of higher-level visual areas Descriptive models of visual neurons How to build models of visual neuronal responses (i.e., stimulus/response maps) Mechanistic models of the visual system Inferring the mechanisms underlying the response properties of visual neurons Functional models of the visual system Understanding neuronal population codes Part 2. Sensory Systems: Tactile Perception 1. Introduction to the study of the cerebral cortex 2. Sensory maps in the cerebral cortex 3. Transduction 4. Somatosensory system and pain 5. Methods for computational neuroscience of perception 6. Encoding and decoding 7. Perceptual memory 8. Neuroscience of perceptual knowledge Part 3: Bayesian modeling of perception Perception as Bayesian inference Bayesian inference under sensory noise Cue combination and evidence accumulation Discrimination, detection and classification

A) Ecology and evolution Theory classes B) Introduction to systems and computational neuroscience Mainly frontal lectures, except 3 hours of computational demonstrations in the Bayesian modeling module

A) Ecology and evolution Murray, Mathematical Biology vol I Strogatz, Non-linear dynamics and Chaos B) Introduction to systems and computational neuroscience No additional reading material is necessary beyond what is provided during lessons. However some of the teachers’ material comes from these sources and students are invited to seek additional background there: · Dayan, P. & Abbott, L. F. Theoretical Neuroscience. (MIT Press, 2001). · Martin, A. R., Brown, D. A., Diamond, M. E., Cattaneo, A. & De-Miguel, F. F. From Neuron to Brain, Sixth Edition. (Oxford University Press, 2021). · Rieke, F., Warland, D. & Bialek, W. Spikes: exploring the neural code. (The MIT Press, 1999). · Wichmann, F. A. & Hill, N. J. The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & Psychophysics 63, 1293-1313, doi:10.3758/BF03194544 (2001). · Ma, Koerding and Goldreich (2022). Bayesian Models of Perception and Action (https://www.cns.nyu.edu/malab/bayesianbook.html) · Richard McElreath (2nd ed 2020). Statistical Rethinking. (https://xcelab.net/rm/ )

Dispense;

Modalità di esame: Prova scritta (in aula); Prova orale obbligatoria;

Exam: Written test; Compulsory oral exam;

... A) Ecology and evolution The exam is only oral. The students will be assigned homework that will be discussed during the oral exam. B) Introduction to systems and computational neuroscience Written exam.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.