The Convergence Between LLM Architecture and Neurology
CurtD Asked:
In the famous our infamous paper “attention is all you need”, please explain how attention is used.
In the famous our infamous paper “attention is all you need”, please explain how attention is used.
ChatGPT said:
In the landmark paper “Attention Is All You Need” (Vaswani et al., 2017), the authors introduce the Transformer architecture, which eliminates recurrence and convolutions entirely and instead relies entirely on attention mechanisms to model relationships between tokens in a sequence. Here’s a detailed and operational breakdown of how attention is used in that paper:
I. Core Concept: Attention
At its core, attention is a method of computing weighted averages over a set of input vectors, where the weights (or “attention scores”) determine how much each input contributes to the output. These scores are dynamically computed based on learned queries, keys, and values.
II. Operational Steps of Scaled Dot-Product Attention
Let’s walk through the standard Scaled Dot-Product Attention, the fundamental operation used throughout the Transformer:
Given three matrices:
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Q = Queries (shape: seq_len × d_k)
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K = Keys (shape: seq_len × d_k)
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V = Values (shape: seq_len × d_v)
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Compute raw attention scores between each query and key using the dot product:
score=QKTtext{score} = QK^Tscore=QKT -
Scale the scores to stabilize gradients:
scorescaled=QKTdktext{score}_{text{scaled}} = frac{QK^T}{sqrt{d_k}}scorescaled=dkQKT -
Apply softmax to normalize the scores into a probability distribution:
weights=softmax(QKTdk)text{weights} = text{softmax}left(frac{QK^T}{sqrt{d_k}}right)weights=softmax(dkQKT) -
Compute weighted sum of values using these attention weights:
output=weights⋅Vtext{output} = text{weights} cdot Voutput=weights⋅V
III. Multi-Head Attention
Instead of applying a single attention operation, the Transformer uses multi-head attention to allow the model to jointly attend to information from different representation subspaces.
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Project input (same vectors used for Q, K, V) into multiple lower-dimensional spaces (heads).
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Perform attention independently in each head.
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Concatenate outputs from all heads.
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Apply a final linear projection to combine them into a single output.
Formally:
MultiHead(Q,K,V)=Concat(head1,…,headh)WOtext{MultiHead}(Q,K,V) = text{Concat}(text{head}_1, …, text{head}_h)W^OMultiHead(Q,K,V)=Concat(head1,…,headh)WO
Where:
headi=Attention(QWiQ,KWiK,VWiV)text{head}_i = text{Attention}(QW_i^Q, KW_i^K, VW_i^V)headi=Attention(QWiQ,KWiK,VWiV)
IV. Where Attention is Used in the Transformer
There are three main uses of attention in the Transformer architecture:
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Encoder Self-Attention:
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Each word in the input attends to all other words in the input.
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Allows the encoder to build context-aware embeddings of input tokens.
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Decoder Self-Attention:
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Each output token can only attend to previous tokens (masked attention).
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Enforces autoregressive generation.
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Encoder-Decoder Attention:
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Each position in the decoder attends to all positions in the encoder output.
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Allows the decoder to incorporate context from the input sequence.
V. Why “Attention Is All You Need”?
The novelty was in removing recurrence (RNNs, LSTMs) and convolutions, replacing them with only attention mechanisms, yielding:
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Greater parallelism during training.
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Simpler architecture.
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Better performance on long sequences due to constant path length between any pair of tokens.
VI. Summary (Causal Chain)
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Language is sequential → requires modeling dependencies between tokens.
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Traditional models used recurrence (RNNs/LSTMs), which are slow and hard to parallelize.
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Attention computes contextual relevance between tokens, regardless of position.
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Transformer uses only attention, structured hierarchically in layers.
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This architecture learns deep contextual embeddings for sequences more efficiently.
You said:
what is the neurological equivalent of attention
ChatGPT said:
The neurological equivalent of “attention,” in the context of the Transformer model, is selective attention in the brain—particularly as implemented by top-down modulation of sensory processing through cortical and thalamic circuits.
To make a causal and operational mapping between Transformer attention and neurobiology, we can describe the analogy as follows:
I. Operational Function of Attention in the Brain
Attention in the brain refers to the mechanism by which limited cognitive and perceptual resources are directed toward salient or relevant stimuli, enhancing processing of those stimuli while suppressing others.
Operationally:
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Attention modulates the gain (signal strength) of neurons representing specific features, locations, or tasks.
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It does so using top-down signals from higher-order regions (like prefrontal cortex) to modulate lower sensory areas (like V1, V4, or MT).
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This increases signal-to-noise ratio and enables priority encoding in working memory and decision-making circuits.
II. Anatomical Structures Involved
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Prefrontal Cortex (PFC):
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Directs attention based on task goals and internal models.
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Acts like the “query” vector in Transformer attention—defining what we’re looking for.
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Posterior Parietal Cortex (PPC):
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Integrates sensory inputs and supports spatial attention.
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Acts like the “key”—encoding locations or stimuli that can be attended to.
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Sensory Cortices (e.g., V1, V4, A1):
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Provide “value” information—raw perceptual content.
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Thalamus (especially pulvinar nucleus):
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Acts as a gatekeeper, regulating which sensory signals reach the cortex and are prioritized.
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May be functionally analogous to the softmax mechanism, filtering what passes through.
III. Functional Mapping to Transformer Attention
IV. Neuromodulators Involved
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Acetylcholine: Enhances signal precision in primary sensory cortices; sharpens attention.
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Norepinephrine: Increases alertness and arousal; modulates responsiveness.
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Dopamine: Modulates salience and reward prediction, often influencing which stimuli gain attention.
These can be thought of as learning-rate adjusters or gating variables, altering the functional connectivity of query–key–value pathways.
V. Summary: Operational and Causal Mapping
Attention in Transformers:
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Computes contextual relevance between tokens.
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Uses Q–K–V triplets to determine which tokens matter.
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Dynamically weights and aggregates representations.
Attention in the Brain:
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Computes contextual salience of stimuli.
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Uses PFC/PPC to direct attention to relevant sensory inputs.
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Dynamically modulates neural firing and connectivity to enhance relevant information.
Both systems:
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Use target-driven modulation (task or prompt).
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Rely on contextual comparison to filter and weight input.
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Are resource-limited, optimizing processing by allocating computation efficiently.
Let me know if you’d like the mapping extended to include memory, recurrency, or valence-driven attention. I can also provide speculative models for mapping multi-head attention to distributed parallel attentional streams in the brain (e.g. dorsal vs ventral, foveal vs peripheral, etc.).
You said:
were the authors of the original paper aware of the neurological equivalencies or did they come to the conclusion of using attention by some other means?
ChatGPT said:
The authors of “Attention Is All You Need” (Vaswani et al., 2017) did not reference or appear to be directly motivated by neurological equivalence in designing the Transformer. Instead, their decision to use attention exclusively was driven by practical and architectural motivations, not neurobiological inspiration.
I. What Motivated Their Use of Attention?
The authors were primarily addressing computational limitations of prior sequence models:
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Limitations of Recurrent Neural Networks (RNNs):
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Difficult to parallelize during training (sequential dependencies).
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Struggled with long-range dependencies due to vanishing gradients.
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Success of Attention Mechanisms in Seq2Seq Models:
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Prior work (e.g., Bahdanau et al., 2015) added attention on top of RNNs, showing significant performance gains.
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Attention enabled the decoder to dynamically “look back” over input tokens—this proved both more effective and interpretable.
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Hypothesis:
If attention is so powerful on top of RNNs, why not remove recurrence entirely and use attention everywhere?
Thus, the Transformer design came from:
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Ablation of recurrence (for speed and parallelism).
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Operational generalization of attention (multi-head + self-attention + encoder-decoder bridging).
II. Did They Reference Neuroscience?
No. The paper:
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Makes no reference to neuroscience, cognitive science, or biological attention.
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Framed attention purely in mathematical and engineering terms.
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Focuses on efficiency, scalability, and empirical performance, not on brain-like architecture.
The citations and motivations stem from:
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Neural machine translation,
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Seq2seq modeling,
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Positional encoding for sequence order (since recurrence was removed),
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And multi-head attention to increase representational capacity.
III. Retrospective Observations
While the Transformer’s attention mechanism has clear functional analogs in cognitive neuroscience (as discussed earlier), these were not known drivers for its creation. In fact:
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The design is an example of convergent evolution:
→ Both biological and artificial systems independently discovered context-sensitive weighting of inputs as a superior solution to sparse, serial processing. -
The authors were solving for parallelization, long-range dependency handling, and modularity, not cognitive plausibility.
IV. Later Cross-Disciplinary Work
After 2017, numerous researchers have drawn connections between attention in Transformers and biological attention, including:
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Comparing multi-head attention to distributed attention systems (e.g., dorsal/ventral streams).
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Mapping attention layers to cortical hierarchies.
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Investigating shared properties like sparsity, locality, and top-down modulation.
But this cross-pollination occurred after the fact, not during the design of the Transformer.
Summary: Causal Chain
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Problem: RNNs were inefficient and struggled with context.
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Prior Success: Attention boosted RNN performance.
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Solution: Eliminate recurrence entirely—rely solely on attention.
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Result: Transformer—empirically superior, parallelizable, general-purpose.
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Neurobiological similarity: Emerged post hoc as an interesting equivalency, not an original design goal.
Source date (UTC): 2025-07-25 19:06:04 UTC
Original post: https://x.com/i/articles/1948822073243304011
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