Search

Numerous bats species have evolved biosonar to obtain information from their habitats with dense vegetation. Different from man-made sensors, such as stereo cameras and LiDAR, bats' biosonar has much lower spatial resolution and sampling rates. Their biosonar is capable of reliably finding narrow gaps in foliage to serve as a passageway to fly through. To investigate the sensory information under such capability, we have used a biomimetic sonar robot to collect the narrow gap echoes from an artificial hedge in a laboratory setup and from the natural foliage in outdoor environments respectively. The work in this dissertation presents the performance of a conventional energy approach and a deep-learning approach in the classification of echoes from foliage and gap. The deep-learning approach has better foliage versus passageway classification accuracy than the energy approach in both experiments, and it also shows good robustness than the latter one when dealing with data with great varieties in the outdoor experiments. A class activation mapping approach indicates that the initial rising flank inside the echo spectrogram contains critical information. This result corresponds to the neuromorphic spiking model which could be simplified as times where the echo amplitude crosses a certain threshold in a certain frequency range. With these findings, it could be demonstrated that the sensory information in clutter echoes plays an important role in detecting passageways in foliage regardless of the wider beamwith than the passageway geometry.

Numerous bats species have evolved biosonar to obtain information from their habitats with dense vegetation. Different from man-made sensors, such as stereo cameras and LiDAR, bats' biosonar has much lower spatial resolution and sampling rates. Their biosonar is capable of reliably finding narrow gaps in foliage to serve as a passageway to fly through. To investigate the sensory information under such capability, we have used a biomimetic sonar robot to collect the narrow gap echoes from an artificial hedge in a laboratory setup and from the natural foliage in outdoor environments respectively. The work in this dissertation presents the performance of a conventional energy approach and a deep-learning approach in the classification of echoes from foliage and gap. The deep-learning approach has better foliage versus passageway classification accuracy than the energy approach in both experiments, and it also shows good robustness than the latter one when dealing with data with great varieties in the outdoor experiments. A class activation mapping approach indicates that the initial rising flank inside the echo spectrogram contains critical information. This result corresponds to the neuromorphic spiking model which could be simplified as times where the echo amplitude crosses a certain threshold in a certain frequency range. With these findings, it could be demonstrated that the sensory information in clutter echoes plays an important role in detecting passageways in foliage regardless of the wider beamwith than the passageway geometry.

The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment., QC 20210611

The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems.