486 research outputs found
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is
widespread in animal species. Sleep is known to be beneficial to cognitive and
mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the
importance of the phenomenon, a complete understanding of its functions and
underlying mechanisms is still lacking. In this paper, we show interesting
effects of deep-sleep-like slow oscillation activity on a simplified
thalamo-cortical model which is trained to encode, retrieve and classify images
of handwritten digits. During slow oscillations,
spike-timing-dependent-plasticity (STDP) produces a differential homeostatic
process. It is characterized by both a specific unsupervised enhancement of
connections among groups of neurons associated to instances of the same class
(digit) and a simultaneous down-regulation of stronger synapses created by the
training. This hierarchical organization of post-sleep internal representations
favours higher performances in retrieval and classification tasks. The
mechanism is based on the interaction between top-down cortico-thalamic
predictions and bottom-up thalamo-cortical projections during deep-sleep-like
slow oscillations. Indeed, when learned patterns are replayed during sleep,
cortico-thalamo-cortical connections favour the activation of other neurons
coding for similar thalamic inputs, promoting their association. Such mechanism
hints at possible applications to artificial learning systems.Comment: 11 pages, 5 figures, v5 is the final version published on Scientific
Reports journa
Emergent Computations in Trained Artificial Neural Networks and Real Brains
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt
to changing environments. How do cortical circuits use plasticity to acquire
functions such as decision-making or working memory? Neurons are connected in
complex ways, forming recurrent neural networks, and learning modifies the
strength of their connections. Moreover, neurons communicate emitting brief
discrete electric signals. Here we describe how to train recurrent neural
networks in tasks like those used to train animals in neuroscience
laboratories, and how computations emerge in the trained networks.
Surprisingly, artificial networks and real brains can use similar computational
strategies.Comment: International Summer School on Intelligent Signal Processing for
Frontier Research and Industry, INFIERI 2021. Universidad Aut\'onoma de
Madrid, Madrid, Spain. 23 August - 4 September 202
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep
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. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories
Design and Implementation of FPGA-based Hardware Accelerator for Bayesian Confidence Propagation Neural Network
The Bayesian confidence propagation neural network (BCPNN) has been widely used for neural computation and machine learning domains. However, the current implementations of BCPNN are not computationally efficient enough, especially in the update of synaptic state variables. This thesis proposes a hardware accelerator for the training and inference process of BCPNN. In the hardware design, several techniques are employed, including a hybrid update mechanism, customized LUT-based design for exponential operations, and optimized design that maximizes parallelism. The proposed hardware accelerator is implemented on an FPGA device. The results show that the computing speed of the accelerator can improve the CPU counterpart by two orders of magnitude. In addition, the computational modules of the accelerator can be reused to reduce hardware overheads while achieving comparable computing performance. The accelerator's potential to facilitate the efficient implementation for large-scale BCPNN neural networks opens up the possibility to realize higher-level cognitive phenomena, such as associative memory and working memory
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