Computational Principles of Neural Processing: modulating neural systems through temporally structured stimuli

In order to understand how the neural system encodes and processes information, research has focused on the study of neural representations of simple stimuli, paying no particular attention to it's temporal structure, with the assumption that a deeper understanding of how the neural system processes simpli fied stimuli will lead to an understanding of how the brain functions as a whole [1]. However, time is intrinsically bound to neural processing as all sensory, motor, and cognitive processes are inherently dynamic. Despite the importance of neural and stimulus dynamics, little is known of how the neural system represents rich spatio-temporal stimulus, which ultimately link the neural system to a continuously changing environment. The purpose of this thesis is to understand whether and how temporally-structured neural activity modulates the processing of information within the brain, proposing in turn that, the precise interaction between the spatio-temporal structure of the stimulus and the neural system is particularly relevant, particularly when considering the ongoing plasticity mechanisms which allow the neural system to learn from experience. In order to answer these questions, three studies were conducted. First, we studied the impact of spiking temporal structure on a single neuron spiking response, and explored in which way the functional connections to pre-synaptic neurons are modulated through adaptation. Our results suggest that, in a generic spiking neuron, the temporal structure of pre-synaptic excitatory and inhibitory neurons modulate both the spiking response of that same neuron and, most importantly, the speed and strength of learning. In the second, we present a generic model of a spiking neural network that processes rich spatio-temporal stimuli, and explored whether the processing of stimulus within the network is modulated due to the interaction with an external dynamical system (i.e. extracellular media), as well as several plasticity mechanisms. Our results indicate that the memory capacity, that re ects a dynamic short-term memory of incoming stimuli, can be extended on the presence of plasticity and through the interaction with an external dynamical system, while maintaining the network dynamics in a regime suitable for information processing. Finally, we characterized cortical signals of human subjects (electroencephalography, EEG) associated to a visual categorization task. Among other aspects, we studied whether changes in the dynamics of the stimulus leads to a changes in the neural processing at the cortical level, and introduced the relevance of large-scale integration for cognitive processing. Our results suggest that the dynamic synchronization across distributed cortical areas is stimulus specific and specifically linked to perceptual grouping. Taken together, the results presented here suggest that the temporal structure of the stimulus modulates how the neural system encodes and processes information within single neurons, network of neurons and cortical areas. In particular, the results indicate that timing modulates single neuron connectivity structures, the memory capability of networks of neurons, and the cortical representation of a visual stimuli. While the learning of invariant representations remains as the best framework to account for a number of neural processes (e.g. long-term memory [2]), the reported studies seem to provide support the idea that, at least to some extent, the neural system functions in a non-stationary fashion, where the processing of information is modulated by the stimulus dynamics itself. Altogether, this thesis highlights the relevance of understanding adaptive processes and their interaction with the temporal structure of the stimulus, arguing that a further understanding how the neural system processes dynamic stimuli is crucial for the further understanding of neural processing itself, and any theory that aims to understand neural processing should consider the processing of dynamic signals. 1. Frankish, K., and Ramsey, W. The Cambridge Handbook of Cognitive Science. Cambridge University Press, 2012. // 2. McGaugh, J. L. Memory{a Century of Consolidation. Science 287, 5451 (Jan. 2000), 248{251