Emergence of associative learning in a neuromorphic inference network

OBJECTIVE: In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH: We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS: Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE: These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence

Modellizzazione computazionale dell'attività cerebrale: dai singoli neuroni ai circuiti su larga scala

Il presente lavoro di tesi intende descrivere il processo scientifico e metodologico che ha portato allo sviluppo di modelli matematici di neurone e di circuiti cerebrali sfruttando l’approccio metodologico delle neuroscienze computazionali. L’attività di ricerca, in particolare, ha riguardato la messa a punto di varie strategie di modellizzazione abbracciando molteplici scale spazio-temporali su diversi livelli di complessità. I risultati di questa attività scientifica sottolineano l'importanza degli strumenti computazionali da un lato nel processo di comprensione del funzionamento dei circuiti neurali e dall’altro per lo sviluppo di applicazioni ingegneristiche innovative. In questo lavoro intendo infatti mostrare come, utilizzando dati sperimentali, le neuroscienze computazionali favoriscano il progresso scientifico e tecnologico. Il processo di modellizzazione è stato affrontato partendo dal livello di scala del singolo neurone in cui un modello biologicamente realistico basato sulla modellizzazione di Hodgkin-Huxley è stato utilizzato per studiare dettagliatamente le dinamiche dell’eccitabilità e della plasticità neuronale. Sono stati poi modellizzati microcircuiti spiking su larga scala basati su singoli neuroni del tipo “integrate and fire” in quanto possono essere utilizzati per la generazione di gemelli digitali (digital twins) di regioni cerebrali estese. Se opportunamente calibrati su dati sperimentali, questi strumenti computazionali consentono di esplorare condizioni fisiologiche e patologiche non testabili sperimentalmente, offrendo uno strumento digitale innovativo contro le patologie a carico del sistema nervoso. Infine, in un processo di astrazione basato sulle neuroscienze teoriche, singoli neuroni inferenziali sono stati utilizzati per riprodurre la funzionalità di reti estese con un numero limitato di elementi computazionali al fine di sviluppare microprocessori neuromorfi ad elevata efficienza energetica. Sottolineando la profonda interconnessione tra modelli teorici, computazionali ed indagini sperimentali, questa ricerca evidenzia l’importanza di una efficace collaborazione tra la raccolta di dati sperimentali ed il processo di modellizzazione che da sempre è alla base della ricerca scientifica ed ha sempre accompagnato e assistito la neurofisiologia. Inoltre, suggerisce l'importanza dello sviluppo di tecnologie innovative che traggano ispirazione dall'efficienza e dalla robustezza dei meccanismi computazionali del cervello.This work of thesis presents the scientific and methodological process that underpin the development of mathematical models of neurons and brain circuits. Employing the methodological approach of computational neuroscience, the research activity, in particular, was focused on the refinement of various modeling strategies spanning multiple spatio-temporal scales and managing different levels of complexity. The results emphasize the importance of computational tools not only in expanding the knowledge about the functioning of neural circuits but also for the development of novel engineering applications. This work demonstrates how computational neuroscience, driven by experimental data, promotes both scientific and technological advancements. The modeling of brain activity was firstly addressed at the scale of single neurons, where a biologically realistic model based on Hodgkin-Huxley modeling strategy was used to study the dynamics of excitability and neural plasticity in detail. Subsequently, large-scale spiking microcircuits were modeled exploiting "integrate and fire" neuron models. These models aimed at creating digital twins of extended brain regions. When appropriately calibrated with experimental data, these computational tools allow exploration of physiological and pathological conditions that are not experimentally testable, providing an innovative digital tool against nervous system diseases. Finally, in an abstraction process based on theoretical neuroscience, we employed individual inferential neurons to replicate the functionality of extended networks with a limited number of computational elements with the aim of developing energy-efficient neuromorphic microprocessors. This research accentuates the profound interconnection between theoretical models, computational methodologies, and empirical investigations. It emphasizes the importance of an effective interchange between experimental data collection and the modelling process, a synergy that has always been fundamental to scientific research and neurophysiology. Moreover, it suggests the importance of applying this knowledge to the development of innovative technologies inspired by the efficiency and robustness of the brain's computational mechanisms

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing

A multidisciplinary approach to estimating wolf population size for long‐term conservation

From Crossref journal articles via Jisc Publications RouterHistory: received 2022-10-06, accepted 2023-05-23, epub 2023-07-28, issued 2023-07-28, published 2023-07-28Article version: VoRPublication status: PublishedAbstractThe wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices

A multidisciplinary approach to estimating wolf population size for long‐term conservation

From Wiley via Jisc Publications RouterHistory: received 2022-10-06, rev-recd 2023-02-06, accepted 2023-05-23, epub 2023-07-28Article version: VoRPublication status: PublishedFunder: EC LIFE Programme; Grant(s): LIFE18NAT/IT/000972Funder: Research Council of Norway; Grant(s): NFR 286886The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices