Modeling neuropathologies as disruption of normal sequence generation in working memory networks

Area of Science:

• Computational neuroscience
• Cognitive neuroscience
• Systems neuroscience

Background:

• Recurrent cortico-cortical networks are crucial for working memory and cognitive functions.
• Understanding pathological network behavior is key to explaining neurological disorders.

Purpose of the Study:

• To investigate how specific parameter changes in a computational working memory model lead to pathological dynamics.
• To link these dynamics to cognitive symptoms in epilepsy, schizophrenia, and obsessive-compulsive disorder.

Main Methods:

• Utilized a biologically plausible computational model of working memory.
• Systematically altered parameters including network inhibition, excitation, connection delays, and plasticity.
• Analyzed the resulting network dynamics.

Main Results:

• Reductions in network inhibition and altered plasticity induced pathological dynamics.
• These dynamics showed potential correlations with cognitive symptoms observed in neuropathologies.
• Demonstrated that similar mechanisms could underlie diverse cognitive symptoms.

Conclusions:

• Pathological dynamics in working memory networks may arise from specific parameter alterations.
• These dynamics offer a potential unifying mechanism for cognitive symptoms across various neurological disorders.
• Suggests overlapping pathological states within normal network function.