|M.Sc Student||Rossenberg Oran|
|Subject||Spatial Source Separation in Random Networks of Cortical|
Neurons using Manifold Learning
|Department||Department of Electrical Engineering||Supervisors||Professor Shimon Marom|
|Professor Noam Ziv|
Groups of neurons produce action-potentials sequences that propagate across extended parts of neuronal networks. These sequences are believed to form neural representations (codes) that give rise to network functionality. Directed by sensory input or generated through interactions with a responding environment, these are affected by processes acting at lower biophysical and biological levels. These processes are characterized by high degrees of variance and non-stationarity, in contrast to the stability and invariance needed to maintain coherent behaviors. Conventional techniques used to explore ‘neural codes’ and propagation patterns tend to be supervised with strict constraints and are rather task-oriented. Therefore, they are limited in fully capturing the spatiotemporal network complexity. This study developed and applied a generic semi-supervised approach, named Rated-Time-Hidden-Manifold (RTHM), to explore and analyze neuronal activity by combining both rate-based and time-based representations paradigms to a single encoding scheme (Rated-Time scheme). Using this encoding, we find that we can expose lowdimensional, non-linear embeddings through manifold-learning dimensionality reduction techniques (e.g., Diffusion-Maps). Here, we use this approach to analyze the problem of source separation (also referred to as source identification), that is, identifying the source of input delivered to a neuronal network from the output (the network activity evoked by the input). To that end, we used large-scale random networks of rat cortical neurons developing in culture on substrate-integrated Multi-Electrode-Arrays (MEA’s) as our experimental setting. We performed experiments in which periodic stimulations were delivered from different spatial sources while continuously recording the network activity. We then used RTHM to describe network activity through low-dimensional embeddings, extracted from the Rated-Time encoding. The resulted embeddings were successful in the source-separation task. Embeddings were then used to analyze intrinsic characteristics of networks, such as sensitivity to delay-in-response and similarity of responses originating from adjacent sources. Our method is comparable with results produced by supervised methods and is successful when additional temporal and spatial constraints are introduced. RTHM can be applied online as well as in closed-loop frameworks and can be utilized in other tasks and various settings.