Program
14/4/2023
Neurosciences
9:30-10:00 Francesco Pavone, Victoria Barygina, Francesco Goretti, Alessandro Scaglione, Chiara Noferini
Neural correlates in social interactions in animals: towards a human study
10:00-10:30 Maria M Del Viva, Renato Budinich, Serena Castellotti, Vladimir S Georgiev & Giovanni Punzi
Role of the cost of plasticity in determining the features of fast vision in humans.
Several studies have demonstrated the usefulness of general principles of computational efficiency and maximum
information preservation in predicting even rather detailed properties of early vision [1,2,3,4.]
While all these studies have deeply examined the efficiency of the computation involved in the processing that actually occurs during the early visual analysis, not much attention has been devoted to the issue of the complexity of the computation required to determine the base ingredients of that processing themselves (neural Receptive Fields). Indeed, some of those algorithms require rather complex calculations in order to determine the shape of the RFs.
Considering the plasticity of the visual systems, one might expect that the algorithms employed by the visual system should not only be economical to execute, but also reasonably economical to set up, and to update when adapting to varying external conditions.
In this regards, it is an interesting question whether there are examples where the visual system has made a choice that is suboptimal from the point of view of the run-time performance, but lends to easier and more efficient updates and improvement. In this work we present results of a psychophysical experiment that appears to be such an example.
We start from a model of early vision [5], where the general principle of computational optimality takes the form of a maximization of transferred entropy within a limited bandwidth and from a fixed, finite number of discrete patterns, that are
assumed to be the only information recognized by the system. This approach captures very well the problem faced by a system with finite computational resources, and has proved to be very effective in practice, in describing the actual human performance in fast vision in a number of situations [5,6]. In addition, it lends very well to comparing the properties of mathematically optimal solutions to approximate, and therefore sub-optimal, solutions that easier to compute and update.
Specifically, the optimality condition that is imposed to the set of RF in this approach, can be formulated as a case of a class of well-known problems that go under the name of "knapsack problems" [7]. These problems admit exact numerical solutions, that in the general case are rather expensive to compute, and simpler approximate solutions that are slightly less optimal, as the one that has been heuristically adopted in Del Viva et al.5. We have found that application of these approaches to the extraction of optimal visual patterns lead to similar but nonetheless clearly distinguishable solutions, raising the interesting question of which of the two better describes the actual performance of fast vision in human subjects.
By performing psychophysical experiments we found clear evidence that the actual performance of human vision is in agreement with the simpler approximate solution rather than the mathematical optimum. While the latter is slightly better from the point of view of computational efficiency of the image analysis, the simpler solution is much easier to determine and update in case of the need to adapt to changes of the external conditions. This experimental result thus seems to be evidence for a well-defined role of the "cost of plasticity", in shaping the features of the visual system.
References
1. Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61:183–193.
2. Atick JJ (1992) Could information theory provide an ecological theory of sensory processing? Network 3: 213-51.
3. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607-609.
4.Zhaoping L (2006) Theoretical understanding of the early visual processes by data compression and data selection. Network, 17: 301–334.
5. Del Viva MM, Punzi G, Benedetti D (2013) Information and
Perception of Meaningful Patterns. PLoS ONE 8(7)
6. Del Viva MM, Punzi G, Shevell SK (2016) Chromatic Information and Feature Detection in Fast Visual Analysis. PLoS ONE 11(8)
7. Dantzig, T. Numbers: The Language of Science, 1930
10:30-11:00 Elena Serritella, Andrea Guazzini, Mirko Duradoni
Obsessive-Compulsive Disorder (OCD) types and Social Media: are social media important and impactful for OCD people?
Social media (SM) are the new standard for social interaction and people with OCD use such platforms like everyone else. However, the research on these individuals provides limited, sporadic, and difficult-to-generalize data outside of social-media evidence for one specific context concerning how SM is experienced by people with OCD. Our cross-sectional study involved 660 participants (71.4% females, 28.6% males) with 22% of the sample surpassing the 90 percentile threshold to be identified as high-level OCD-symptomatic individuals. Our work highlighted that roughly all OCD types are affected by social media in terms of mood and that these individuals appeared to give SM more importance than non-OCD individuals. The evidence presented, although very narrow, can be conceived as the first building blocks to encourage future research considering how individuals with OCD experience social media, since they appear to be affected more by them compared to non-OCD individuals.
11:00-11:30 Coffee break
Neurosciences
11:30-12:00 Martina Fiorenza, Andrea Guazzini, Mirko Duradoni
'Climate change' and 'Implicit attitudes': towards a new eye-tracking-based measure
A sustainable future requires radical changes in people's behavior and lifestyles: changes that directly affect values and consolidated practices.
The present study aimed to measure implicit attitudes toward climate change using an ecological, and noninvasive assessment through eye-tracking technology.
As a preliminary stage, a systematic review of the literature was conducted, selecting articles that used the IAT methodology to analyze implicit attitudes toward climate change. A sample of 22 subjects was administered an SC-IAT test while the eye-tracking was recorded.
The traditional IAT D-Score was used as a criterion to validate a brand new measure based on eye-tracking activity (i.e., sustained attention and cognitive loading). Correlation between this new predictor based on Eye tracking and the SC-IAT scoring supported the research hypothesis's validation.
Neural networks and machine learning
12:00-12:30 Lorenzo Chicchi
Recurrent Spectral Networks: using a discrete map to reach automated classification
A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories towards distinct asymptotic target destinations. These latter are incorporated into the model by taking advantage of the spectral decomposition of the operator that rules the linear evolution across the processing network. Non-linear terms act for a transient and allow to disentangle the data supplied as initial condition to the discrete dynamical system. The system effectively aligns along assigned directions, which reflect the specificity of the provided input and that are encoded in the loss function via suitable spectral projections. The network can be equipped with several memory kernels which can be sequentially activated for serial datasets handling. Our novel approach to classification, that we here term Recurrent Spectral Network (RSN), is successfully challenged against a simple test-bed model, created for illustrative purposes, as well as a standard dataset for image processing training.
12:30-13:00 Arturo Berrones-Santos [Online]
Delayed binary time-series model for sequential machine learning
Discrete sequential data arises in a number of domains like natural language processing, economic time series and pricing, production or demand forecasting of renewable energy, among many other applications of current interest. The identification of temporal structures in this kind of data and their incorporation to machine learning models has been traditionally difficult due to the exponentially large hyper-parameter space exploration for non-Markovian, non-stationary data. We introduce a discrete probabilistic time series model capable of reproducing rich long range temporal dependencies with a parsimoniously small number of delay parameters. By construction the model leads to a straightforward estimation of the delays and their relative influence, which in turn are used to instantiate necessary and difficult to estimate parameters for more advanced machine learning predictive models.
13:00-14:30 Lunch and discussion
Topology and nonlinear dynamics
14:30-15:00 Lorenzo Giambagli
Diffusion-driven instability of topological signals coupled by the Dirac operator
The study of reaction-diffusion systems on networks is of paramount relevance for the understanding of nonlinear processes in systems where the topology is intrinsically discrete, such as the brain. Until now, reaction-diffusion systems have been studied only when species are defined on the nodes of a network. However, in a number of real systems including, e.g., the brain and the climate, dynamical variables are not only defined on nodes but also on links, faces, and higher-dimensional cells of simplicial or cell complexes, leading to topological signals. In this work, we study reaction-diffusion processes of topological signals coupled through the Dirac operator. The Dirac operator allows topological signals of different dimension to interact or cross-diffuse as it projects the topological signals defined on simplices or cells of a given dimension to simplices or cells of one dimension up or one dimension down. By focusing on the framework involving nodes and links, we establish the conditions for the emergence of Turing patterns and we show that the latter are never localized only on nodes or only on links of the network. Moreover, when the topological signals display a Turing pattern their projection does as well. We validate the theory hereby developed on a benchmark network model and on square lattices with periodic boundary conditions.
Disease modeling and sociophysics
15:00-15:30 Tim Van Wesmael
Coupled disease-behavior spreading processes on networks .
Classical epidemiological models assume a homogeneous population that is well mixed.
In order to take spatial information and contact heterogeneity into account, epidemics on networks have been introduced.
This framework, in which a node represents a single person, allows to model individual responses to prevent the spreading of the disease as well.
The change in behavior may result from several factors such as local risk perception, governmental restrictions and peer-to-peer spreading of information, true or false.
This talk focusses on coupled disease-awareness spreading models on networks, with disease-avoiding awareness being generated by a combination of risk perception and peer pressure.
15:30-16:00 Filippo Albani, Franco Bagnoli
Non-linear Kuramoto model
The Kuramoto model is a kind of prototype of synchronization of human behavior. However, often humans synchronize in clusters, a behavior not explained by mean-field coupling. We present here some results of a non-linear extension of the Kuramoto model, which seems to capture this cluster dynamics.