Do Retinal Neurons Also Represent Somatosensory Inputs? On Why Neuronal Responses Are Not Sufficient to Determine What Neurons Do

How does neuronal activity give rise to cognitive capacities? To address this question, neuroscientists hypothesize about what neurons "represent," "encode," or "compute," and test these hypotheses empirically. This process is similar to the assessment of hypotheses in other fields of science and as such is subject to the same limitations and difficulties that have been discussed at length by philosophers of science. In this paper, we highlight an additional difficulty in the process of empirical assessment of hypotheses that is unique to the cognitive sciences. We argue that, unlike in other scientific fields, comparing hypotheses according to the extent to which they explain or predict empirical data can lead to absurd results. Other considerations, which are perhaps more subjective, must be taken into account. We focus on one such consideration, which is the purposeful function of the neurons as part of a biological system. We believe that progress in neuroscience critically depends on properly addressing this difficulty.

The growth of cognition: Free energy minimization and the embryogenesis of cortical computation

The assumption that during cortical embryogenesis neurons and synaptic connections are selected to form an ensemble maximising synchronous oscillation explains mesoscopic cortical development, and a mechanism for cortical information processing is implied by consistency with the Free Energy Principle and Dynamic Logic. A heteroclinic network emerges, with stable and unstable fixed points of oscillation corresponding to activity in symmetrically connected, versus asymmetrically connected, sets of neurons. Simulations of growth explain a wide range of anatomical observations for columnar and non-columnar cortex, superficial patch connections, and the organization and dynamic interactions of neurone response properties. An antenatal scaffold is created, upon which postnatal learning can establish continuously ordered neuronal representations, permitting matching of co-synchronous fields in multiple cortical areas to solve optimization problems as in Dynamic Logic. Fast synaptic competition partitions equilibria, minimizing "the curse of dimensionality", while perturbations between imperfectly partitioned synchronous fields, under internal reinforcement, enable the cortex to become adaptively self-directed. As learning progresses variational free energy is minimized and entropy bounded.

Dorsomedial prefrontal cortex and hippocampus represent strategic context even while simultaneously changing representation throughout a task session

Dorsomedial prefrontal cortex (dmPFC) and hippocampus (HPC) are thought to play complementary roles in a spatial working memory and decision-making network, where spatial information from HPC informs representations in dmPFC, and contextual information from dmPFC biases how HPC recalls that information. We recorded simultaneously from neural ensembles in rodent dmPFC and HPC as rats performed a rule-switching task, and found that ensembles in dmPFC and HPC simultaneously encoded task contingencies and other time-varying information. While ensembles in HPC transitioned to represent new contingencies at the same time as rats updated their strategies to be consistent with the new contingency, dmPFC ensembles transitioned earlier. Neural representations of other time-varying information also changed faster in dmPFC than in HPC. Our results suggest that HPC and dmPFC represent contingencies while simultaneously representing other information which changes over time, and that this contextual information is integrated into hippocampal representations more slowly than in dmPFC.