Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules

Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence. We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances. The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations. The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted

Complementary approaches to synaptic plasticity : from objective functions to Biophysics

Different approaches are possible when it comes to modeling the brain. Given its biological nature, models can be constructed out of the chemical and biological building blocks known to be at play in the brain, formulating a given mechanism in terms of the basic interactions underlying it. On the other hand, the functions of the brain can be described in a more general or macroscopic way, in terms of desirable goals. This goals may include reducing metabolic costs, being stable or robust, or being efficient in computational terms. Synaptic plasticity, that is, the study of how the connections between neurons evolve in time, is no exception to this. In the following work we formulate (and study the properties of) synaptic plasticity models, employing two complementary approaches: a top-down approach, deriving a learning rule from a guiding principle for rate-encoding neurons, and a bottom-up approach, where a simple yet biophysical rule for time-dependent plasticity is constructed. We begin this thesis with a general overview, in Chapter 1, of the properties of neurons and their connections, clarifying notations and the jargon of the field. These will be our building blocks and will also determine the constrains we need to respect when formulating our models. We will discuss the present challenges of computational neuroscience, as well as the role of physicists in this line of research. In Chapters 2 and 3, we develop and study a local online Hebbian self-limiting synaptic plasticity rule, employing the mentioned top-down approach. Firstly, in Chapter 2 we formulate the stationarity principle of statistical learning, in terms of the Fisher information of the output probability distribution with respect to the synaptic weights. To ensure that the learning rules are formulated in terms of information locally available to a synapse, we employ the local synapse extension to the one dimensional Fisher information. Once the objective function has been defined, we derive an online synaptic plasticity rule via stochastic gradient descent. In order to test the computational capabilities of a neuron evolving according to this rule (combined with a preexisting intrinsic plasticity rule), we perform a series of numerical experiments, training the neuron with different input distributions. We observe that, for input distributions closely resembling a multivariate normal distribution, the neuron robustly selects the first principal component of the distribution, showing otherwise a strong preference for directions of large negative excess kurtosis. In Chapter 3 we study the robustness of the learning rule derived in Chapter 2 with respect to variations in the neural model’s transfer function. In particular, we find an equivalent cubic form of the rule which, given its functional simplicity, permits to analytically compute the attractors (stationary solutions) of the learning procedure, as a function of the statistical moments of the input distribution. In this way, we manage to explain the numerical findings of Chapter 2 analytically, and formulate a prediction: if the neuron is selective to non-Gaussian input directions, it should be suitable for applications to independent component analysis. We close this section by showing how indeed, a neuron operating under these rules can learn the independent components in the non-linear bars problem. A simple biophysical model for time-dependent plasticity (STDP) is developed in Chapter 4. The model is formulated in terms of two decaying traces present in the synapse, namely the fraction of activated NMDA receptors and the calcium concentration, which serve as clocks, measuring the time of pre- and postsynaptic spikes. While constructed in terms of the key biological elements thought to be involved in the process, we have kept the functional dependencies of the variables as simple as possible to allow for analytic tractability. Despite its simplicity, the model is able to reproduce several experimental results, including the typical pairwise STDP curve and triplet results, in both hippocampal culture and layer 2/3 cortical neurons. Thanks to the model’s functional simplicity, we are able to compute these results analytically, establishing a direct and transparent connection between the model’s internal parameters and the qualitative features of the results. Finally, in order to make a connection to synaptic plasticity for rate encoding neural models, we train the synapse with Poisson uncorrelated pre- and postsynaptic spike trains and compute the expected synaptic weight change as a function of the frequencies of these spike trains. Interestingly, a Hebbian (in the rate encoding sense of the word) BCM-like behavior is recovered in this setup for hippocampal neurons, while dominating depression seems unavoidable for parameter configurations reproducing experimentally observed triplet nonlinearities in layer 2/3 cortical neurons. Potentiation can however be recovered in these neurons when correlations between pre- and postsynaptic spikes are present. We end this chapter by discussing the relation to existing experimental results, leaving open questions and predictions for future experiments. A set of summary cards of the models employed, together with listings of the relevant variables and parameters, are presented at the end of the thesis, for easier access and permanent reference for the reader

Mezu kimikoak eta Ips sexdentatus kakalardoaren kudeaketa

50 urte baino gehiago igaro dira substantzia infokimikoak animalien arteko komunikazioan duten bitartekaritza frogatu zenetik. Mezu bat bidera dezaketen substantziak dira, eta berebiziko garrantzia hartu dute izurri batzuen kudeaketa jasangarrian; izan ere, ingurumenerako kaltegarri izan daitezkeen pestiziden erabilera ordezkatu dute hein handi batean, bai eta izurrite bilaka daitezkeen intsektuen eta haien ingurunearen arteko harremanak hobeto ezagutzen lagundu ere. Ips sexdentatus kakalardoak, bere beste ahaide eskolitidoen antzera, galerak eragin ditzake pinu-ustiaketetan, baldintzak egokiak izanez gero. Nahiz eta haren feromonaren osagaietako batzuk ezagunak diren aspalditik, mezu kimikoak ez dira erabili orain dela gutxira arte, ez kakalardo zulatzaile horren kudeaketan, ez haren ekologiaren ikerketan

Proof verification in algebraic topology

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Carles Casacuberta[en] Homotopy type theory is a relatively new field which results from the surprising blend of algebraic topology (homotopy) and type theory (type), that tries to serve as a theoretical base for theorem-proving software. This setting is particularly suitable for synthetic homotopy theory. In this work, we describe how the programming language Agda can be used for proof verification, by examining the construction of the fundamental group of the circle $\mathbb{S}^{1}$. Then, trying to obtain the fundamental group of the real projective plane $\mathbb{R} \mathrm{P}^{2}$, we end up exploring a new construction of $\mathbb{R} \mathrm{P}^{2}$ as a higher inductive type

Transcription Factors in the Fungus Aspergillus nidulans: Markers of Genetic Innovation, Network Rewiring and Conflict between Genomics and Transcriptomics

Gene regulatory networks (GRNs) are shaped by the democratic/hierarchical relationships among transcription factors (TFs) and associated proteins, together with the cis-regulatory sequences (CRSs) bound by these TFs at target promoters. GRNs control all cellular processes, including metabolism, stress response, growth and development. Due to the ability to modify morphogenetic and developmental patterns, there is the consensus view that the reorganization of GRNs is a driving force of species evolution and differentiation. GRNs are rewired through events including the duplication of TF-coding genes, their divergent sequence evolution and the gain/loss/modification of CRSs. Fungi (mainly Saccharomycotina) have served as a reference kingdom for the study of GRN evolution. Here, I studied the genes predicted to encode TFs in the fungus Aspergillus nidulans (Pezizomycotina). The analysis of the expansion of different families of TFs suggests that the duplication of TFs impacts the species level, and that the expansion in Zn2Cys6 TFs is mainly due to dispersed duplication events. Comparison of genomic annotation and transcriptomic data suggest that a significant percentage of genes should be re-annotated, while many others remain silent. Finally, a new regulator of growth and development is identified and characterized. Overall, this study establishes a novel theoretical framework in synthetic biology, as the overexpression of silent TF forms would provide additional tools to assess how GRNs are rewired.Work at O.E.’s lab was supported by the University of the Basque Country (GIU19/014 to O.E.) and the Basque Government (PIBA-PUE, PIBA_2020_1_0032, to O.E., and Elkartek, KK-2019/00076, to María Teresa Dueñas)

Real Time Tempo Canons With Antescofo

(Abstract to follow

Experiencias de programación en las escuelas

En los últimos años, diferentes organismos han promovido la enseñanza de la programación en la escuela. En esta publicación, analizaremos algunas experiencias de enseñanza de la programación en escuelas públicas primarias y secundarias de la provincia de Córdoba que se desplegaron como resultado de cursos cortos de formación docente. Desde un enfoque exploratorio identificamos, a partir de reflexiones docentes y observaciones de clase, cuatro emergentes en torno a experiencias de enseñanza de la programación con videojuegos: 1) el entusiasmo que genera en los estudiantes, 2) la posibilidad de integrar disciplinas a partir de proyectos de programación, 3) el desarrollo del trabajo colaborativo y 4) la inclusión en tareas de programación de estudiantes con diferentes capacidades cognoscitivas. Se ofrece una sistematización de las reflexiones de los docentes y algunos registros de observación que permitieron construir estos emergentes.Fil: Echeveste, María Emilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física. Sección Ciencias de la Computación; ArgentinaFil: Martinez, Maria Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Cordoba. Facultad de Filosofia y Humanidades. Cent.de Invest. Maria Saleme de Burnichon; Argentin

E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active

Geology of the Manantial Espejo epithermal district, Deseado Massif, Patagonia Argentina

The silver–gold epithermal mining district, Manantial Espejo, is located southwest of the Deseado Massif, Patagonia. The district is set into Jurassic volcaniclastic rocks of the Bahía Laura Group. A geological map of the district, at a scale of 1:50,000 drawn over a base map prepared from the fusion of satellite imagery and aerial photographs, is included. A suite of andesitic to rhyolitic eruptive units was identified, with prevailing high-grade rhyolitic ignimbrites. Travertine levels show the beginning of a hot-spring system in the region. Quartz veins, with typical crustiform–colloform banded structures, fill WNW, sub-vertical, normal faults, originating from extensional tectonics. The silicification of travertines, tuffs and breccia is the most common hydrothermal alteration.Facultad de Ciencias Naturales y Muse