Papers
The temporal generalization matrix (TGM) - train a linear classifier on neural population activity at one time and test it at another - is a standard tool in cognitive neuroscience, where off-diagonal generalization is read as a 'stable, maintained' representation and a diagonal-only pattern as a 'dynamic' code. We show analytically that this interpretation is not identifiable. Within the linear-Gaussian model in which TGMs are actually computed, the cross-temporal d-prime depends on the discriminative mean direction mu_t and the trial-by-trial noise covariance Sigma_t only through whitened quantities, so changes in mu_t (the code) and changes in Sigma_t (the noise geometry) enter inseparably. We give two fully worked 2x2 counterexamples: a perfectly constant coding direction whose temporal generalization nonetheless decays purely because the noise covariance rotates, and a pair of exactly orthogonal coding directions that nonetheless generalize at ~95 percent because anisotropic training-time noise rotates the Fisher decoder onto the future code. We state the exact confound, its assumptions and limits, and propose - but do not run - three disambiguating analyses that estimate signal and noise geometry separately. This is a theoretical/methodological contribution; no neural data are collected or analyzed.
Working memory maintenance relies on persistent activity in prefrontal pyramidal neurons, local inhibition, and thalamocortical loops, all modulated by dopamine. However, how these mechanisms interact dynamically to resist distractor interference remains unclear. Here, we synthesize recent promising hypotheses into a unified network model that integrates cellular D1-NMDA receptor interactions, dopaminergic modulation of parvalbumin-positive interneurons for distractor filtering, and thalamocortical synchrony to stabilize attractor dynamics. This theoretical framework proposes that coordinated dopamine release in the prefrontal cortex enhances both recurrent excitation and perisomatic inhibition, while strengthening thalamic drive to maintain representations against interference. We outline testable predictions and discuss implications for cognitive deficits in schizophrenia and ADHD. Although direct experimental validation is pending, the model offers a cohesive account of working memory resilience and flexibility.
Working memory maintenance relies on persistent activity in prefrontal pyramidal neurons, local inhibition, and thalamocortical loops, all modulated by dopamine. However, how these mechanisms interact dynamically to resist distractor interference remains unclear. Here, we synthesize recent promising hypotheses into a unified network model that integrates cellular D1-NMDA receptor interactions, dopaminergic modulation of parvalbumin-positive interneurons for distractor filtering, and thalamocortical synchrony to stabilize attractor dynamics. This theoretical framework proposes that coordinated dopamine release in the prefrontal cortex enhances both recurrent excitation and perisomatic inhibition, while strengthening thalamic drive to maintain representations against interference. We outline testable predictions and discuss implications for cognitive deficits in schizophrenia and ADHD. Although direct experimental validation is pending, the model offers a cohesive account of working memory resilience and flexibility.