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conventional approaches such as factor analysis or linear dynamical systems. These findings suggest that supervised learning methods could extract more beneficial information oriented to kinematics from neural population activity rather than unsupervised learning methods. In the third study, described in Chapter 4, I proposed a new approach to finding kinematics-dependent latent factors from M1 population activity, combining intrinsic latent factors and diverse kinematic variables: velocity, acceleration, speed, and jerk. I also investigated the effects of the kinematics-dependent latent factors on neural trajectories, which explicitly reflect movement status by jPCA, and a simple decoding model.
As a result, it revealed that the kinematics-dependent latent factors more clearly stand out kinematic information compared to the other neural representations obtained by conventional approaches: FA, GPFA, and LFADS. It also showed that kinematics-dependent latent factors yield better decoding performance than the other neural representations. These findings suggest that kinematics-dependent latent factors could provide a new means of finding neural representations for motor decoding alternative to unsupervised learning methods.
Despite the interesting findings demonstrated in this dissertation, it should be noted that these studies have several limitations to consider. In the first study, I dealt with only an aspect of directional decoding, excluding other kinematic parameters such as position, velocity, acceleration, and jerk. Also, not using real data to investigate the effects of neuronal ensemble properties on decoding models may be a critical issue for comparing realistic decoding condition. Thus, I should consider investigating neuronal ensemble properties for diverse kinematic variables and using real data for the validity of the simulation study. In the second and third studies, I did not examine the effectiveness of the latent factors on online BMIs. So, clinical verification is not available yet. However, since one of the key factors for clinical demands in BMIs is reliable decoding performance, our approach might be advantageous even for clinical BMIs. In addition, considering clinical conditions where actual arm movements are often not available in patients with paralysis, one can build virtual object movements for BMI training through the investigation of which kinematic variables adequately describe latent factors given on a day. It will make BMI training and motor decoding more effective according to kinematics-related neural information available on the day.
One of the key aspects representing the temporal evolution of noisy neural population activity is the dynamical system. The dynamical systems suggest feasibility for BMI decoder under the assumption that motor decoding models consider only movement-related factors, even if movements are not the only factor affecting M1 population activity. Recent studies have dealt with decoding approaches to finding neural representations, combining latent factors and their dynamics [10], [164]. The notable advantage of these approaches is that modeling latent factors can further clarify movement-related information, considering various effects on neural activity. It also enables estimating more natural movements because the noise of high-dimensional neuronal population activity is minimized. These
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studies commonly follow the assumptions that dynamical systems could trace the temporal evolution of M1 neural states. Hence, I will pursue low noise neural representations for BMIs, considering the kinematics-dependent latent factors in the dynamic perspective.
Another advantage of the dynamical systems is that low-dimensional neural representations could be reliably shared between sessions with different recording conditions [41], [165]. However, occurring neuronal loss and small movements of electrodes over time cause decoding instability in the actual BMI [166]–[168]. Although many studies are being attempted to overcome these problems [72], [169], [170], it remains challenging to stably maintain the kinematic information in the initially recorded neural structures under new recording conditions while meeting the clinical requirements for high- performance neural prostheses. Dynamical systems of the intrinsic neural states could be useful for mapping to low-dimensional neural dynamics of the other ensemble structures [171], [172]. Therefore, I will seek an approach that enables stable decoding performance between sessions recorded independently by establishing the dynamical properties of kinematics-dependent latent factors.
Finally, this dissertation has demonstrated that kinematics-dependent latent factors can be successfully decoded to predict various kinematic variables in M1 neuronal ensemble of the NHP brain.
The findings suggest that our approach can guarantee high decoding performance of predicting arm movements. Therefore, I would like to underline that our current approach may be extended to extract specific neural representations related to any behavioral covariates, including cognitive and sensory functions, which would promise to offer a useful means to explore neural mechanisms of behavior on a single-trial basis.
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