In the study, the researchers used mice trained to run as directed by flashing lights and sweeping audio tones. They then simultaneously presented the animals with conflicting commands from the lights and tones, but also cued them about which signal to disregard. As expected, the prefrontal cortex, which issues high-level commands to other parts of the brain, was crucial.
But the team also observed that if a trial required the mice to attend to vision, turning on neurons in the visual TRN interfered with their performance. And when those neurons were silenced, the mice had more difficulty paying attention to sound.
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In effect, the network was turning the knobs on inhibitory processes, not excitatory ones, with the TRN inhibiting information that the prefrontal cortex deemed distracting. If the mouse needed to prioritize auditory information, the prefrontal cortex told the visual TRN to increase its activity to suppress the visual thalamus—stripping away irrelevant visual data. Despite the success of the study, the researchers recognized a problem. Some part of the circuit was missing. Until now. Halassa and his colleagues have finally put the rest of the pieces in place, and the results reveal much about how we should be approaching the study of attention.
With tasks similar to those they used in , the team probed the functional effects of various brain regions on one another, as well as the neuronal connections between them. The full circuit, they found, goes from the prefrontal cortex to a much deeper structure called the basal ganglia often associated with motor control and a host of other functions , then to the TRN and the thalamus, before finally going back up to higher cortical regions.
When the mice were cued to pay attention to certain sounds, the TRN helped suppress irrelevant background noise within the auditory signal. Tadin studies this kind of background suppression in other processes that happen more quickly and automatically than selective attention does.
Work by Fiebelkorn suggests that the brain has a way to hedge against those risks. When people think about the searchlight of attention, Fiebelkorn says, they think of it as a steady, continuously shining beam that illuminates where an animal should direct its cognitive resources. According to his findings, the focus of the attentional spotlight seems to get relatively weaker about four times a second, presumably to prevent animals from staying overly focused on a single location or stimulus in their environment.
These studies mark a crucial shift: Attentional processes were once understood to be the province of the cortex alone.
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Halassa is particularly intrigued by what the connection between attention and the basal ganglia might reveal about conditions such as attention deficit hyperactivity disorder and autism, which often manifest as hypersensitivity to certain kinds of inputs. But perhaps the most profoundly interesting point about the involvement of the basal ganglia is that the structure is usually associated with motor control, although research has increasingly implicated it in reward-based learning, decision making, and other motivation-based types of behavior as well. The reverse also happens, with body movements as small as the flicker of an eye also guiding perception.
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Read: A new theory explains how consciousness evolved. Slagter is now studying the role that the basal ganglia might play in consciousness. While Attention does have its application in other fields of deep learning such as Computer Vision, its main breakthrough and success comes from its application in Natural Language Processing NLP tasks. This is due to the fact that Attention was introduced to address the problem of long sequences in Machine Translation , which is also a problem for most other NLP tasks as well.
Most articles on the Attention Mechanism will use the example of sequence-to-sequence seq2seq models to explain how it works.oofs.ru/public/66.php
This is because Attention was originally introduced as a solution to address the main issue surrounding seq2seq models, and to great success. If you are unfamiliar with seq2seq models, also known as the Encoder-Decoder model, I recommend having a read through this article to get you up to speed. The standard seq2seq model is generally unable to accurately process long input sequences, since only the last hidden state of the encoder RNN is used as the context vector for the decoder.
On the other hand, the Attention Mechanism directly addresses this issue as it retains and utilises all the hidden states of the input sequence during the decoding process. It does this by creating a unique mapping between each time step of the decoder output to all the encoder hidden states. This means that for each output that the decoder makes, it has access to the entire input sequence and can selectively pick out specific elements from that sequence to produce the output. Before we delve into the specific mechanics behind Attention, we must note that there are 2 different major types of Attention:.
While the underlying principles of Attention are the same in these 2 types, their differences lie mainly in their architectures and computations. The first type of Attention, commonly referred to as Additive Attention, came from a paper by Dzmitry Bahdanau , which explains the less-descriptive original name. The paper aimed to improve the sequence-to-sequence model in machine translation by aligning the decoder with the relevant input sentences and implementing Attention.
The encoder over here is exactly the same as a normal encoder-decoder structure without Attention. For these next 3 steps, we will be going through the processes that happen in the Attention Decoder and discuss how the Attention mechanism is utilised. After obtaining all of our encoder outputs, we can start using the decoder to produce outputs. At each time step of the decoder, we have to calculate the alignment score of each encoder output with respect to the decoder input and hidden state at that time step. The alignment scores for Bahdanau Attention are calculated using the hidden state produced by the decoder in the previous time step and the encoder outputs with the following equation:.
The decoder hidden state and encoder outputs will be passed through their individual Linear layer and have their own individual trainable weights.
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Thereafter, they will be added together before being passed through a tanh activation function. The decoder hidden state is added to each encoder output in this case. Lastly, the resultant vector from the previous few steps will undergo matrix multiplication with a trainable vector, obtaining a final alignment score vector which holds a score for each encoder output.
After generating the alignment scores vector in the previous step, we can then apply a softmax on this vector to obtain the attention weights. The softmax function will cause the values in the vector to sum up to 1 and each individual value will lie between 0 and 1, therefore representing the weightage each input holds at that time step.
After computing the attention weights in the previous step, we can now generate the context vector by doing an element-wise multiplication of the attention weights with the encoder outputs. Due to the softmax function in the previous step, if the score of a specific input element is closer to 1 its effect and influence on the decoder output is amplified, whereas if the score is close to 0, its influence is drowned out and nullified.
The context vector we produced will then be concatenated with the previous decoder output. It is then fed into the decoder RNN cell to produce a new hidden state and the process repeats itself from step 2. The final output for the time step is obtained by passing the new hidden state through a Linear layer, which acts as a classifier to give the probability scores of the next predicted word. Steps 2 to 4 are repeated until the decoder generates an End Of Sentence token or the output length exceeds a specified maximum length. The second type of Attention was proposed by Thang Luong in this paper.
It is often referred to as Multiplicative Attention and was built on top of the Attention mechanism proposed by Bahdanau. The two main differences between Luong Attention and Bahdanau Attention are:. Also, the general structure of the Attention Decoder is different for Luong Attention, as the context vector is only utilised after the RNN produced the output for that time step. We will explore these differences in greater detail as we go through the Luong Attention process, which is:.
As we can already see above, the order of steps in Luong Attention is different from Bahdanau Attention. The code implementation and some calculations in this process is different as well, which we will go through now.
Just as in Bahdanau Attention, the encoder produces a hidden state for each element in the input sequence. Unlike in Bahdanau Attention, the decoder in Luong Attention uses the RNN in the first step of the decoding process rather than the last. The RNN will take the hidden state produced in the previous time step and the word embedding of the final output from the previous time step to produce a new hidden state which will be used in the subsequent steps. In Luong Attention, there are three different ways that the alignment scoring function is defined- dot, general and concat.
These scoring functions make use of the encoder outputs and the decoder hidden state produced in the previous step to calculate the alignment scores. However, the difference lies in the fact that the decoder hidden state and encoder hidden states are added together first before being passed through a Linear layer.
This means that the decoder hidden state and encoder hidden state will not have their individual weight matrix, but a shared one instead, unlike in Bahdanau Attention. After being passed through the Linear layer, a tanh activation function will be applied on the output before being multiplied by a weight matrix to produce the alignment score. Similar to Bahdanau Attention, the alignment scores are softmaxed so that the weights will be between 0 to 1. Again, this step is the same as the one in Bahdanau Attention where the attention weights are multiplied with the encoder outputs.
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