Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep

Golosio, Bruno; Luca, Chiara De; Capone, Cristiano; Pastorelli, Elena; Stegel, Giovanni; Tiddia, Gianmarco; Bonis, G. De; Paolucci, P.

doi:10.1371/journal.pcbi.1009045

Cited by 14 publications

(12 citation statements)

References 55 publications

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“…In perspective, we plan to integrate plastic cortical modules capable of incremental learning and sleep and demonstrating the mechanism producing the beneficial cognitive effects of sleep [6,19] on small scale networks with the methodology here described. This combination will support the construction of plastic models demonstrating the cognitive effects of cortical slow waves at the scale of larger portions of the thalamocortical system.…”

Section: Discussionmentioning

confidence: 99%

Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

Capone¹,

Luca²,

Bonis³

et al. 2021

Preprint

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Recent enhancements in neuroscience, like the development of new and powerful recording techniques of the brain activity combined with the increasing anatomical knowledge provided by atlases and the growing understanding of neuromodulation principles, allow to study the brain at a whole new level, paving the way to the creation of extremely detailed effective network models directly from observed data. Leveraging the advantages of this integrated approach, we propose a method to infer models capable of reproducing the complex spatio-temporal dynamics of the slow waves observed in the experimental recordings of the cortical hemisphere of a mouse under anesthesia. To reliably claim the good match between data and simulations, we implemented a versatile ensemble of analysis tools, applicable to both experimental and simulated data and capable to identify and quantify the spatio-temporal propagation of waves across the cortex. In order to reproduce the observed slow wave dynamics, we introduced an inference procedure composed of two steps: the inner and the outer loop. In the inner loop the parameters of a mean-field model are optimized by likelihood maximization, exploiting the anatomical knowledge to define connectivity priors. The outer loop explores "external" parameters, seeking for an optimal match between the simulation outcome and the data, relying on observables (speed, directions and frequency of the waves) apt for the characterization of cortical slow waves; the outer loop includes a periodic neuro-modulation for a better reproduction of the experimental recordings. We show that our model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the biological network. Also, the proposed method allows to infer which are the relevant modifications of parameters when the brain state changes, e.g. according to anesthesia levels.

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Section: Discussionmentioning

confidence: 99%

Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

Capone¹,

Luca²,

Bonis³

et al. 2021

Preprint

Self Cite

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“…Indeed, plastic synapses require data to be stored since their synaptic weights change their value during the simulation. The inclusion of plastic synapses is essential for many investigations, e.g., when learning or the interplay between synaptic changes and brain dynamics are of interest (Capone et al, 2019 ; Golosio et al, 2021a ). GeNN allows and supports models with synaptic plasticity, but for such models the procedural connectivity approach is thus prevented.…”

Section: Introductionmentioning

confidence: 99%

Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster

Tiddia

Golosio

Albers

et al. 2022

Front. Neuroinform.

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Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST.

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“…Behavioral Cloning can be directly implemented in a supervised learning framework. In the last years, a competition between two opposite interpretations of supervised learning is emerging: errorbased approaches [1][2][3][4][5], where the error information computed at the environment level is injected into the network and used to improve later performances, and target-based approaches [6][7][8][9][10][11][12][13], where a target for the internal activity is selected and learned. In this work, we provide a general framework, which we call GOAL (Generalized Optimization of Apprenticeship Learning), where these different approaches are reconciled and can be retrieved via a proper definition of the error propagation structure the agent receives from the environment.…”

Section: Introductionmentioning

confidence: 99%

Error-based or target-based? A unified framework for learning in recurrent spiking networks

2022

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The field of recurrent neural networks is over-populated by a variety of proposed learning rules and protocols. The scope of this work is to define a generalized framework, to move a step forward towards the unification of this fragmented scenario. In the field of supervised learning, two opposite approaches stand out, error-based and target-based. This duality gave rise to a scientific debate on which learning framework is the most likely to be implemented in biological networks of neurons. Moreover, the existence of spikes raises the question of whether the coding of information is rate-based or spike-based. To face these questions, we proposed a learning model with two main parameters, the rank of the feedback learning matrix R and the tolerance to spike timing τ⋆. We demonstrate that a low (high) rank R accounts for an error-based (target-based) learning rule, while high (low) tolerance to spike timing promotes rate-based (spike-based) coding. We show that in a store and recall task, high-ranks allow for lower MSE values, while low-ranks enable a faster convergence. Our framework naturally lends itself to Behavioral Cloning and allows for efficiently solving relevant closed-loop tasks, investigating what parameters ( R , τ ⋆ ) are optimal to solve a specific task. We found that a high R is essential for tasks that require retaining memory for a long time (Button and Food). On the other hand, this is not relevant for a motor task (the 2D Bipedal Walker). In this case, we find that precise spike-based coding enables optimal performances. Finally, we show that our theoretical formulation allows for defining protocols to estimate the rank of the feedback error in biological networks. We release a PyTorch implementation of our model supporting GPU parallelization.

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Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep

Cited by 14 publications

References 55 publications

Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse

Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster

Error-based or target-based? A unified framework for learning in recurrent spiking networks

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