The problem of catastrophic URLetting can be traced back to the 1980s, but it has not been completely solved . Since human brains are good at continual lifelong learning, brain-inspired methods may provide solutions to this problem. The end result of learning different objects in different categories is the formation of concepts in the brain. Experiments showed that concepts are likely encoded by concept cells in the medial temporal lobe (MTL) of the human brain. Furthermore, concept cells encode concepts sparsely and are responsive to multi-modal stimuli. However, it is unknown how concepts are formed in the MTL. Here we assume that the integration of audio and visual perceptual information in the MTL during learning is a crucial step to form concepts and make continual learning possible , and we propose a biological plausible audio-visual integration model (AVIM), which is a spiking neural network with multi-compartmental neuron model and a calcium based synaptic tagging and capture plasticity model, as a possible mechanism of concept formation . We then build such a model and run on different datasets to test its ability of continual learning. Our simulation results show that the AVIM not only achieves state-of-the-art  performance compared with other advanced methods but also the output of AVIM for each concept has stable representations during the continual learningprocess . These results support our assumption that concept formation is essential for continuous lifelong learning , and suggest the AVIM we propose here is a possible mechanism of concept formation , and hence is a brain-like solution to the problem of catastrophic URLetting .
The problem of catastrophic URLetting has a history of more than 30 years and has not been completely solved yet. Since the human brain has natural ability to perform continual lifelong learning, learning from the brain may provide solutions to this problem. In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption that the integration of audio and visual perceptual information in the medial temporal lobe during learning is crucial to form concepts and make continual learning possible . Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based synaptic tagging and capture as the model's learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain . We then test our model under this new paradigm. Our experimental results show that the proposed AVIM can achieve state-of-the-art continual learning performance compared with other advanced methods such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning . These results support our assumption that concept formation is essential for continuous lifelong learning and suggest the proposed AVIM is a possible concept formation mechanism .
R
can be traced back to the 1980s, but it has
has a history of more than 30 years and has
R
. Since human brains are good at
yet. Since the human brain has natural ability to perform
R
brain-inspired methods
learning from the brain
R
The end result of learning different objects in different categories is the formation of concepts in the brain. Experiments showed that concepts are likely encoded by concept cells in the medial temporal lobe (MTL) of the human brain. Furthermore, concept cells encode concepts sparsely and are responsive to multi-modal stimuli. However, it is unknown how concepts are formed in the MTL. Here we assume
In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption
R
MTL
medial temporal lobe
R
a crucial step
crucial
R
, and we propose a biological plausible audio-visual integration model (AVIM), which is a spiking neural network with multi-compartmental neuron model and a calcium based
. Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based
R
plasticity model, as a possible mechanism of concept formation
as the model's learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain
R
build such a model and run on different datasets to test its ability of continual learning. Our simulation
test our model under this new paradigm. Our experimental
R
AVIM not only achieves
proposed AVIM can achieve
A
continual learning
R
but also the output of AVIM for each concept has stable representations during the continual learningprocess
such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning
D
,
R
AVIM we propose here
proposed AVIM
R
mechanism of concept formation , and hence is a brain-like solution to the problem of catastrophic URLetting
concept formation mechanism