no code implementations • 10 Nov 2023 • Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz, Johan Frederik Storm, Pier Stanislao Paolucci
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e. g., from sensory systems).
no code implementations • 16 Jun 2023 • Bruno Golosio, Jose Villamar, Gianmarco Tiddia, Elena Pastorelli, Jonas Stapmanns, Viviana Fanti, Pier Stanislao Paolucci, Abigail Morrison, Johanna Senk
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory.
2 code implementations • 15 Nov 2022 • Robin Gutzen, Giulia De Bonis, Chiara De Luca, Elena Pastorelli, Cristiano Capone, Anna Letizia Allegra Mascaro, Francesco Resta, Arnau Manasanch, Francesco Saverio Pavone, Maria V. Sanchez-Vives, Maurizio Mattia, Sonja Grün, Pier Stanislao Paolucci, Michael Denker
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques.
no code implementations • 13 Nov 2022 • Chiara De Luca, Leonardo Tonielli, Elena Pastorelli, Cristiano Capone, Francesco Simula, Cosimo Lupo, Irene Bernava, Giulia De Bonis, Gianmarco Tiddia, Bruno Golosio, Pier Stanislao Paolucci
We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits.
1 code implementation • 4 Nov 2022 • Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci
Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons.
1 code implementation • 20 May 2022 • Cristiano Capone, Pier Stanislao Paolucci
We propose a two-module (agent and model) spiking neural network in which "dreaming" (living new experiences in a model-based simulated environment) significantly boosts learning.
1 code implementation • 27 Jan 2022 • Cristiano Capone, Cosimo Lupo, Paolo Muratore, Pier Stanislao Paolucci
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency.
no code implementations • 2 Sep 2021 • Cristiano Capone, Paolo Muratore, Pier Stanislao Paolucci
Finally, we show that our theoretical formulation suggests protocols to deduce the structure of learning feedback in biological networks.
2 code implementations • 15 Apr 2021 • Cristiano Capone, Chiara De Luca, Giulia De Bonis, Robin Gutzen, Irene Bernava, Elena Pastorelli, Francesco Simula, Cosimo Lupo, Leonardo Tonielli, Anna Letizia Allegra Mascaro, Francesco Resta, Francesco Pavone, Micheal Denker, Pier Stanislao Paolucci
The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques.
no code implementations • 28 Jul 2020 • Bruno Golosio, Gianmarco Tiddia, Chiara De Luca, Elena Pastorelli, Francesco Simula, Pier Stanislao Paolucci
In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on a balanced network of excitatory and inhibitory neurons, using AdEx neurons and conductance-based synapses.
no code implementations • 26 Mar 2020 • Bruno Golosio, Chiara De Luca, Cristiano Capone, Elena Pastorelli, Giovanni Stegel, Gianmarco Tiddia, Giulia De Bonis, Pier Stanislao Paolucci
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions.
no code implementations • 13 Feb 2020 • Paolo Muratore, Cristiano Capone, Pier Stanislao Paolucci
We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spiking pattern that encodes the solution to complex temporal tasks.
1 code implementation • 22 Feb 2019 • Giulia De Bonis, Miguel Dasilva, Antonio Pazienti, Maria V. Sanchez-Vives, Maurizio Mattia, Pier Stanislao Paolucci
Cortical slow oscillations are an emergent property of the cortical network, a hallmark of low complexity brain states like sleep, and represent a default activity pattern.
Neurons and Cognition
no code implementations • 22 Feb 2019 • Elena Pastorelli, Cristiano Capone, Francesco Simula, Maria V. Sanchez-Vives, Paolo del Giudice, Maurizio Mattia, Pier Stanislao Paolucci
Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW).
1 code implementation • 12 Dec 2018 • Francesco Simula, Elena Pastorelli, Pier Stanislao Paolucci, Michele Martinelli, Alessandro Lonardo, Andrea Biagioni, Cristiano Capone, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Luca Pontisso, Piero Vicini, Roberto Ammendola
We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption.
1 code implementation • 28 Nov 2018 • Marco Celotto, Chiara De Luca, Paolo Muratore, Francesco Resta, Anna Letizia Allegra Mascaro, Francesco Saverio Pavone, Giulia De Bonis, Pier Stanislao Paolucci
Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia.
no code implementations • 24 Oct 2018 • Cristiano Capone, Elena Pastorelli, Bruno Golosio, Pier Stanislao Paolucci
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species.
no code implementations • 10 Apr 2018 • Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Pier Stanislao Paolucci, Elena Pastorelli, Luca Pontisso, Francesco Simula, Piero Vicini
DPSNN can be configured to stress either the networking or the computation features available on the execution platforms.
no code implementations • 23 Mar 2018 • Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Luca Pontisso, Piero Vicini, Roberto Ammendola
We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays.
no code implementations • 20 Aug 2014 • Pier Stanislao Paolucci, Iuliana Bacivarov, Devendra Rai, Lars Schor, Lothar Thiele, Hoeseok Yang, Elena Pastorelli, Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Francesco Simula, Laura Tosoratto, Piero Vicini
The EURETILE project required the selection and coding of a set of dedicated benchmarks.
no code implementations • 7 May 2013 • Pier Stanislao Paolucci, Iuliana Bacivarov, Gert Goossens, Rainer Leupers, Frédéric Rousseau, Christoph Schumacher, Lothar Thiele, Piero Vicini
Furthermore, EURETILE investigates and implements the innovations for equipping the elementary HW tile with high-bandwidth, low-latency brain-like inter-tile communication emulating 3 levels of connection hierarchy, namely neural columns, cortical areas and cortex, and develops a dedicated cortical simulation benchmark: DPSNN-STDP (Distributed Polychronous Spiking Neural Net with synaptic Spiking Time Dependent Plasticity).