Multihead self-attention in cortico-thalamic circuits
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916889164251136 |
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| author | Granier, Arno Senn, Walter |
| author_facet | Granier, Arno Senn, Walter |
| contents | Both biological cortico-thalamic networks and artificial transformer networks use canonical computations to perform a wide range of cognitive tasks. In this work, we propose that the structure of cortico-thalamic circuits is well suited to realize a computation analogous to multihead self-attention, the main algorithmic innovation of transformer networks. We assign distinct computational roles to superficial and deep pyramidal cells of the cortex: while superficial pyramidal cells maintain a key-value memory, deep pyramidal cells encode the current query, gain-modulated by the key-value memory in the superficial layer. We show that the structure of this computation matches the fine-grained structure of core and matrix projections from the thalamus to the cortex. We then suggest the parallel between one head of attention and a cortical area, and propose that a thalamo-cortico-thalamic pathway implements a computation akin to a multihead, unnormalized, linear self-attention block. Cross-attention corresponds to the key-value memory of one cortical area being used for retrieval by the query in another cortical area. Finally, as a first step towards a mechanistic theory of synaptic learning of cortical transformers, we derive the formal gradients of a typical loss function with respect to the parameters of such computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multihead self-attention in cortico-thalamic circuits Granier, Arno Senn, Walter Neurons and Cognition Neural and Evolutionary Computing Both biological cortico-thalamic networks and artificial transformer networks use canonical computations to perform a wide range of cognitive tasks. In this work, we propose that the structure of cortico-thalamic circuits is well suited to realize a computation analogous to multihead self-attention, the main algorithmic innovation of transformer networks. We assign distinct computational roles to superficial and deep pyramidal cells of the cortex: while superficial pyramidal cells maintain a key-value memory, deep pyramidal cells encode the current query, gain-modulated by the key-value memory in the superficial layer. We show that the structure of this computation matches the fine-grained structure of core and matrix projections from the thalamus to the cortex. We then suggest the parallel between one head of attention and a cortical area, and propose that a thalamo-cortico-thalamic pathway implements a computation akin to a multihead, unnormalized, linear self-attention block. Cross-attention corresponds to the key-value memory of one cortical area being used for retrieval by the query in another cortical area. Finally, as a first step towards a mechanistic theory of synaptic learning of cortical transformers, we derive the formal gradients of a typical loss function with respect to the parameters of such computation. |
| title | Multihead self-attention in cortico-thalamic circuits |
| topic | Neurons and Cognition Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.06354 |