Neuroscience is the study of the brain. It is an interdisciplinary field. At one level, behavioral tasks are carefully designed to understand behavior and cognition. At other levels, different devices and technologies are used to acquire neural data (the brain communicates via electrical signals) at different scales and link such data to behavior. The interdisciplinary nature is not just evident in the data acquisition but also in the processing and analyses of the data. Signal processing and other system theory-based techniques, statistical methods and computational modeling can be used to identify useful information in brain-acquired signals (electrical, blood flow, volume, etc.) and link them to behavior and cognition in healthy and diseased states.
As a field, neuroscience has made numerous meaningful discoveries primarily using the traditional hypothesis driven, top-down, deductive approach. In this approach, the goal is to identify local phenomena that can account for macroscopic (behavioral) effects. This approach provides answers to the specific questions or hypotheses tested. My early work follows this approach.
In my first deductive-approach study, I developed a behavioral task that enables controlled delivery of rodent enrichment. I invented an Obstacle Course, which is a track with built-in obstacles that rodents must overcome to complete a lap and receive reward. Comparing this track with traditional enrichment (toys in larger home-cages) we found that animals running this track outperformed animals receiving traditional enrichment. This track then improves upon the rodent model for enrichment, which is used to test the hypothesis that enriched human life experience plays a protective role against myriad debilitating neurological diseases including dementia.
In the second deductive-approach study, I analyzed data from surgically implanted depth electrode recordings of electrical brain activity in rats engaged in a behavioral task where they were tested on their memory for a sequence of odors (memory for temporal order). Spectral analysis on this data revealed that the hippocampal area CA1 (a region important for memory processing) recruits power in the 20-40 Hz range during odor-processing, and that the steady-state dynamics of this neural synchrony occurs after a decision is made about temporal order. 20-40 Hz power during this post-decision state increases with knowledge of the odor-sequence. Lastly, machine learning analysis revealed that power in this post-decision state is predictive of sequence memory response accuracy. 20-40 Hz power is generally suggested to gate information within a brain network implicated in a given cognitive process (suppress distractors and maintain representations in facilitating sensori-motor integration); here we extend its role to sequence memory processing in the hippocampus.
The third deductive study was similar in nature to the aforementioned one, but in humans. Neural electrical signals were recorded from the brains of patients suffering from epilepsy, using surgically implanted electrodes for clinical monitoring, while they engaged in a memory task. Spectral analysis revealed increased 4-5 Hz power in the hippocampus and neocortex, two key memory systems in the brain, during successful discrimination between similar memories. In this work, it was essential to make inferences about the presence and directionality of interactions between the two memory systems recruiting 4-5 Hz power. This led to my fourth deductive project -- expansion of two methodologies; 1) measuring 4-5 Hz phase transfer entropy but as a function of time, and 2) using the Kalman filter to estimate parameters of a time-varying autoregressive model for subsequent Granger causality analyses – both methods enabled inferences about the direction of communication between the two memory systems. Using these methods, I identified a pattern of directional communication between the hippocampus and neocortex that is consistent with predictions from computational models.
These deductive works led to new discoveries. However, I encountered a boundary point in deductive research – I can only know the answers to the specific hypotheses tested. But my knowledge about the general operational range and dynamics of the underlying brain circuits was absent. Given the astounding complexity of the brain, and the numerous behavioral phenomena it supports, deductive approaches alone do not suffice, as they may leave us with incomplete and perhaps misleading information regarding the underlying brain circuits. So I changed my approach to an inductive one.
An inductive, bottom-up approach, which is conspicuously absent in the field, reveals physiological rules that link biological phenomena with behavior. I therefore employed Volterra series-based system identification as an inductive approach and comprehensively, agnostically, and rigorously identified the operations of a given brain area, the hippocampal CA1. This was implemented by stimulating and recording from rodent brain slices that were kept live through an artificial physiological environment. Nearly the entirety of the operational range of the CA1 is captured in a single formula, an input-output nonlinear transfer function. Using this function alone, the CA1 output to a random input in a new set of animals can be predicted with high accuracy. Moreover, examining this formula led to new discoveries and provided insight about the CA1 system software, despite this being one of the most heavily studied nodes in the brain. Contrary to the fields popular belief, the formula revealed the presence of system nonlinearity in CA1. Additionally, intuition is gained by examining this formula regarding how the CA1 treats inputs (amplification, suppression, harmonic generation) based on the content of the input.
All in all, across species, and using different modalities and approaches I summarize work concerned with first identifying specific neural electrical activity for specific behavior. Then I transition to a different approach -- identifying the fundamental rules of a candidate brain area, capturing nearly the entirety of its processes and operations in a single input-output transfer function. Potential future work could extend this approach to other brain nodes and link them together appropriately based on known anatomical connectivity. An exciting possible outcome of such a research program would be to ultimately characterize the entire brain circuit, through iterative system identification, such that a physically realizable brain can be built.
