Week 9 reading

Does neuroscience work?

• Scala et al. (2023) “Phenotypic variation of transcriptomic cell types in mouse motor cortex”
• Jun et al. (2017) “Fully integrated silicon probes for high-density recording of neural activity”
• Steinmetz et al. (2021) “Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings”
• Lazebnik (2002) “Can a biologist fix a radio?—Or, what I learned while studying apoptosis”
• Jonas & Kording (2017) “Could a Neuroscientist Understand a Microprocessor?”

What are spikes for?

• Romain Brette’s blog on rate versus timing
• Brette (2015) “Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain”
• Manwani and Koch (1999) “Detecting and estimating signals in noisy cable structures, II: information theoretical analysis”
• Shadlen and Newsome (1998) “The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding”
• Rossant et al. (2011) “Sensitivity of Noisy Neurons to Coincident Inputs”
• Deneve (2018) “Bayesian Spiking Neurons I: Inference”

Further reading on open issues in neuroscience

• Rule et al. (2019) “Causes and consequences of representational drift”. Synapses are in a state of continual turnover in the brain, unlike our theories of learning. How can we understand stable perception and behaviour in the light of this?
• Pastuzyn et al. (2018) “The Neuronal Gene Arc Encodes a Repurposed Retrotransposon Gag Protein that Mediates Intercellular RNA Transfer”. Neurons can transfer packets of RNA to each other. How does this change our picture of learning?
• Stringer et al. (2019) “Spontaneous behaviors drive multidimensional, brainwide activity”. A huge amount of the variability of neural responses in visual cortex can be explained by knowing the facial muscle movements (in mice). Does this mean that the brain is much more entangled than previously thought? See also Pessoa (2022) “The Entangled Brain”.