The Rich Lab is broadly interested in discerning functional roles for the well-established heterogeneity and diversity in the brain. These heterogeneities are seen from the level of neurons—in varied electrophysiological properties within similarly classified cells, to microcircuits—in varied synaptic connectivity strengths and probabilities, to the whole brain—where a range of measures of neuronal activity exhibit noisy dynamics.
As this diversity is seen throughout the brain, a range of neuroscientific questions can be studied using computational tools designed to account for, rather than idealize away, such heterogeneities. Current focuses in the lab are highlighted below.
Realistic oscillations through heterogeneity
Oscillatory brain activity has been implicated in a range of important neurological functions including learning and memory. While computational modeling has proposed viable mechanisms by which these oscillations might arise in neuronal microcircuits, these mechanisms tend to be highly idealized. In some cases, these idealizations yield in silico oscillations that diverge significantly from key characteristics of these rhythms as observed experimentally. Accounting for sources of heterogeneity in less idealized computational models may begin to bridge the divide between the in silico and in vitro/vivo settings.
Reduced heterogeneity and neuropathology
If heterogeneity in the brain serves a functional role, it follows that reductions of this heterogeneity may disrupt these functions and contribute to pathology. Computational neuroscience has outlined how more homogeneous microcircuits can promote oscillatory activity more reminiscent of pathological oscillations (e.g., those observed during seizure or in Parkinson's disease) than physiological ones. Building in silico models where experimentally-characterized reductions in heterogeneity are directly tied to pathological brain activity can provide important new insights into the genesis of neuropsychiatric disorders.
Vagus Nerve Stimulation in stroke rehabilitation
Vagus Nerve Stimulation (VNS) has been used for decades to treat epilepsy and depression, and recently approved for use in stroke rehabilitation. However, the mechanisms of action by which VNS achieves its therapeutic effects are poorly understood at the mechanistic level of neurons and microcircuits, particularly as it pertains to VNS-paired therapy in stroke rehabilitation. Computational neuroscience is uniquely situated to connect what is known about VNS to effects at the level of cortical microcircuits that may explain its efficacy, with one potential explanation being the promotion of neuronal heterogeneity.