One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC) in the brain, and plays a major role in the memory consolidation process. It consists of different types of pyramidal neurons. Based on their firing behavior, these excitatory neurons are classified into strong burst firing (SBF), weak burst firing (WBF) and regular firing (RF) neurons. In the first part of the work, morphological differences in the different neuronal subtypes was explored by biocytin staining after classifying the neurons based on the differences in electrophysiological properties. Detailed morphological properties of these three neuronal subtypes were analyzed using Neurolucida neuron reconstruction method. Unlike the differences in their electrophysiological properties, no difference was found in the morphometric properties of these neuronal subtypes. In the second part of the thesis, experimental results on spike- timing- dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of the juvenile (P15-P19) rat are described. The STDP was studied in the WBF and RF neurons. Causal pairing of a single EPSP with a single back propagating action potential (bAP) at a time interval of 10 ms failed to induce plasticity. However, increasing the number of bAPs in such EPSP-bAP pair to three at 50 Hz (bAP burst) induced LTD in both, the RF, as well as the WBF neurons. Increasing the frequency of action potentials to 150 Hz in the bAP burst during causal pairing also induced LTD in both the neuronal subtypes. However, all other STDP related experiments were performed only with the bAP bursts consisting of 3 bAPs evoked at 50 Hz. Amplitude of the causal pairing induced LTD decreased with increasing time interval between EPSP and the bAP burst. Reversing the order of the EPSP and the bAP burst in the pair induced LTP only with a short time interval of 10 ms. This finding is in contrast to most of the reports on excitatory synapses, wherein the pre-before post (causal) pairing induced LTP and vice-versa. The results of causal and anti-causal pairing were used to plot the STDP curve for the WBF neurons. In the STDP curve observed in these synapses, LTD was observed upto a causal time interval of 30 ms, while LTP was limited to 10 ms time interval. Hence, the STDP curve was biased towards LTD. These results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of STDP curves. Next, the mechanism of non-Hebbian LTD was studied in both, the RF and WBF neurons. The involvement of calcium in the postsynaptic neuron in plasticity induction was studied by chelating intracellular calcium with BAPTA. The results indicate that the LTD induction in WBF neurons required postsynaptic calcium, while LTD induction in the RF neurons was independent of postsynaptic calcium. Paired pulse ratio (PPR) experiments suggested the involvement of a presynaptic mechanism in the induction of LTD in the RF neurons, and not in the WBF neurons since the PPR was unaffected by the induction protocol only in the WBF neurons. LTD induction in the WBF neurons required activity of the NMDA receptors since LTD was not observed in the presence of the NMDA receptor blocker in the WBF neurons, while it was unaffected in the RF neurons. However, the RF neurons required the activity of L-type calcium channels for plasticity induction, since LTD was affected in the presence of the L-type calcium channel blockers, although the WBF neurons did not require the L-type calcium channel activity for plasticity induction. Hence, in addition to a non-Hebbian STDP curve, a novel mechanism of LTD induction has been reported, where L-type calcium channels are involved in a synaptic plasticity that is expressed via change in the release probability. The findings on the STDP in subicular pyramidal neurons may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in it.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

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The size reduction of transistors has been the main reason for a successful development of semiconductor integrated circuits over the last decades. Because of the physically limited downscaling of transistors, alternative technologies namely the information processing and nonvolatile resistive switches (also termed memristors) have come into focus. Memristors reveal a fast switching speed, long retention time, and stable endurance. Nonvolatile analog bipolar resistive switching with a considerable large On/Off ratio is reported in BiFeO3 (BFO)-based resistive switches. So far resistive switches are mainly applied in memory applications or logic operations. Given the excellent properties of BFO based memristors, the further exploration of functionalities for memristive devices is required. A new approach for hardware based cryptographic system was developed within the framework of this dissertation. By studying the power conversion efficiencies on BFO memristor at various harmonics, it has been shown that two sets of clearly distinguishable power ratios are achievable when the BFO memristor is set into high or into low resistance state. Thus, a BFO-based binary encoding system can be established. As an example the unrecoverable seizure information from encoded medical data suggests the proper functioning of the proposed encryption system. Aside from cryptographic functionality, the single pairing spike timing dependent plasticity (STDP) in BFO-based artificial synapses is demonstrated, which can be considered as the cornerstone for energy-efficient and fast hardware-based neuromorphic networks. In comparison to the biological driven realistic way, only single one pairing of pre- and postsynaptic spikes is applied to the BFO-based artificial synapse instead of 60-80 pairings. Thus, the learning time constant of STDP function can be reduced from 25 ms to 125 us. / In den letzten Jahrzehnten war die Größenreduktion von Transistoren einer der Hauptgründe für die Leistungssteigerung von integrierten Halbleiterschaltungen. Aufgrund des physikalisch beschränkten Skalierungspotentials, werden alternative Technologien für Halbleiterschaltungen entwickelt. Dazu zählen neuartige Widerstandsschalter, sogenannte Memristoren, welche wegen ihrer schnellen Schaltgeschwindigkeit, langen Speicherzeit und stabilen Haltbarkeit in den Fokus der Forschung gerückt sind. Das nichtflüchtige analoge bipolare Schalten des Widerstandwertes mit einem On/Off Verhältnis größer als 100 wurde in BiFeO 3 (BFO)-basierten Widerstands-schaltern beobachtet. Bisher wurden Widerstandsschalter hauptsächlich als Speicher oder in rekonfigurierbaren Logikschaltungen verwendet. Aufgrund der ausgezeichneten Eigenschaften von BFO-basierten Memristoren, ist die Untersuchung weiterer neuer Funktionalitäten vielversprechend. Als neuer Ansatz für ein Hardware-basiertes Kryptosystem wird in der vorliegenden Arbeit die Ausnutzung des Leistungsübertragungskoeffizienten in BFO Memristoren vorgeschlagen. Mit Hilfe der unterschiedlichen Oberschwingungen, welche von einem BFO Memristor im ON und OFF Zustand generiert werden, wurde ein Kryptosystem zum Kodieren binärer Daten entwickelt. Ein Test des Hardware-basierten Kryptosystems an Biodaten ergab, dass die kodierten Biodaten keine vorhersagbare Korrelation mehr enthielten. In der vorliegenden Arbeit wurden darüberhinaus BFO-basierte künstliche Synapsen mit einer Aktionspotentials-Intervall abhängigen Plastizität (STDP) für Einzelpulse entwickelt. Diese Einzelpuls-STDP legt den Grundstein für energieffiziente und schnelle neuromorphe Netzwerke mit künstlichen Synapsen. Im Vergleich zu biologischen Synapsen mit einer 60-80-Puls-STDP und einem Lernfenster auf der ms-Zeitskale, konnte das Lernfenster von BFO-basierten künstlichen Synapsen von 25 ms auf 125 μs reduziert werden. Solch ein schnelles Lernen ermöglicht auch die extreme Reduzierung des Leistungsverbrauchs in neuromorphen Netzwerken.

The size reduction of transistors has been the main reason for a successful development of semiconductor integrated circuits over the last decades. Because of the physically limited downscaling of transistors, alternative technologies namely the information processing and nonvolatile resistive switches (also termed memristors) have come into focus. Memristors reveal a fast switching speed, long retention time, and stable endurance. Nonvolatile analog bipolar resistive switching with a considerable large On/Off ratio is reported in BiFeO3 (BFO)-based resistive switches. So far resistive switches are mainly applied in memory applications or logic operations. Given the excellent properties of BFO based memristors, the further exploration of functionalities for memristive devices is required. A new approach for hardware based cryptographic system was developed within the framework of this dissertation. By studying the power conversion efficiencies on BFO memristor at various harmonics, it has been shown that two sets of clearly distinguishable power ratios are achievable when the BFO memristor is set into high or into low resistance state. Thus, a BFO-based binary encoding system can be established. As an example the unrecoverable seizure information from encoded medical data suggests the proper functioning of the proposed encryption system. Aside from cryptographic functionality, the single pairing spike timing dependent plasticity (STDP) in BFO-based artificial synapses is demonstrated, which can be considered as the cornerstone for energy-efficient and fast hardware-based neuromorphic networks. In comparison to the biological driven realistic way, only single one pairing of pre- and postsynaptic spikes is applied to the BFO-based artificial synapse instead of 60-80 pairings. Thus, the learning time constant of STDP function can be reduced from 25 ms to 125 us. / In den letzten Jahrzehnten war die Größenreduktion von Transistoren einer der Hauptgründe für die Leistungssteigerung von integrierten Halbleiterschaltungen. Aufgrund des physikalisch beschränkten Skalierungspotentials, werden alternative Technologien für Halbleiterschaltungen entwickelt. Dazu zählen neuartige Widerstandsschalter, sogenannte Memristoren, welche wegen ihrer schnellen Schaltgeschwindigkeit, langen Speicherzeit und stabilen Haltbarkeit in den Fokus der Forschung gerückt sind. Das nichtflüchtige analoge bipolare Schalten des Widerstandwertes mit einem On/Off Verhältnis größer als 100 wurde in BiFeO 3 (BFO)-basierten Widerstands-schaltern beobachtet. Bisher wurden Widerstandsschalter hauptsächlich als Speicher oder in rekonfigurierbaren Logikschaltungen verwendet. Aufgrund der ausgezeichneten Eigenschaften von BFO-basierten Memristoren, ist die Untersuchung weiterer neuer Funktionalitäten vielversprechend. Als neuer Ansatz für ein Hardware-basiertes Kryptosystem wird in der vorliegenden Arbeit die Ausnutzung des Leistungsübertragungskoeffizienten in BFO Memristoren vorgeschlagen. Mit Hilfe der unterschiedlichen Oberschwingungen, welche von einem BFO Memristor im ON und OFF Zustand generiert werden, wurde ein Kryptosystem zum Kodieren binärer Daten entwickelt. Ein Test des Hardware-basierten Kryptosystems an Biodaten ergab, dass die kodierten Biodaten keine vorhersagbare Korrelation mehr enthielten. In der vorliegenden Arbeit wurden darüberhinaus BFO-basierte künstliche Synapsen mit einer Aktionspotentials-Intervall abhängigen Plastizität (STDP) für Einzelpulse entwickelt. Diese Einzelpuls-STDP legt den Grundstein für energieffiziente und schnelle neuromorphe Netzwerke mit künstlichen Synapsen. Im Vergleich zu biologischen Synapsen mit einer 60-80-Puls-STDP und einem Lernfenster auf der ms-Zeitskale, konnte das Lernfenster von BFO-basierten künstlichen Synapsen von 25 ms auf 125 μs reduziert werden. Solch ein schnelles Lernen ermöglicht auch die extreme Reduzierung des Leistungsverbrauchs in neuromorphen Netzwerken.