Biomedical subjects

Leonard Maler

Publications and source records attributed to Leonard Maler.

22 records · Page 2Linked to original sources

PubMedDate not supplied

Oscillatory burst discharge generated through conditional backpropagation of dendritic spikes.

Gamma frequencies of burst discharge (>40 Hz) have become recognized in select cortical and non-cortical regions as being important in feature extraction, neural synchrony and oscillatory discharge. Pyramidal cells of the electrosensory lateral line lobe (ELL) of Apteronotus leptorhynchus generate burst discharge in relation to specific features of sensory input in vivo that resemble those recognized as gamma frequency discharge when examined in vitro. We have shown that these bursts are generated by an entirely novel mechanism termed conditional backpropagation that involves an intermittent failure of dendritic Na+ spike conduction. Conditional backpropagation arises from a frequency-dependent broadening of dendritic spikes during repetitive discharge, and a mismatch between the refractory periods of somatic and dendritic spikes. A high threshold class of K+ channel, AptKv3.3, is expressed at high levels and distributed over the entire soma-dendritic axis of pyramidal cells. AptKv3.3 channels are shown to contribute to the repolarization of both somatic and dendritic spikes, with pharmacological blockade of dendritic Kv3 channels revealing an important role in controlling the threshold for burst discharge. The entire process of conditional back-propagation and burst output is successfully simulated using a new compartmental model of pyramidal cells that incorporates a cumulative inactivation of dendritic K+ channels during repetitive discharge. This work is important in demonstrating how the success of spike backpropagation can control the output of a principle sensory neuron, and how this process is regulated by the distribution and properties of voltage-dependent K+ channels.

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PubMedDate not supplied

Ghostbursting: a novel neuronal burst mechanism.

Pyramidal cells in the electrosensory lateral line lobe (ELL) of weakly electric fish have been observed to produce high-frequency burst discharge with constant depolarizing current (Turner et al., 1994). We present a two-compartment model of an ELL pyramidal cell that produces burst discharges similar to those seen in experiments. The burst mechanism involves a slowly changing interaction between the somatic and dendritic action potentials. Burst termination occurs when the trajectory of the system is reinjected in phase space near the "ghost" of a saddle-node bifurcation of fixed points. The burst trajectory reinjection is studied using quasi-static bifurcation theory, that shows a period doubling transition in the fast subsystem as the cause of burst termination. As the applied depolarization is increased, the model exhibits first resting, then tonic firing, and finally chaotic bursting behavior, in contrast with many other burst models. The transition between tonic firing and burst firing is due to a saddle-node bifurcation of limit cycles. Analysis of this bifurcation shows that the route to chaos in these neurons is type I intermittency, and we present experimental analysis of ELL pyramidal cell burst trains that support this model prediction. By varying parameters in a way that changes the positions of both saddle-node bifurcations in parameter space, we produce a wide gallery of burst patterns, which span a significant range of burst time scales.

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PubMedDate not supplied

Type I burst excitability.

We introduce the concept of "type I burst excitability", which is a generalization of the "normal" excitability that is well-known in cardiac and neural systems. We demonstrate this type of burst excitability in a specific model system, a pyramidal cell from the electrosensory lateral line lobe of the weakly electric fish Apteronotus leptorhynchus. As depolarizing current is increased, a saddle-node bifurcation of periodic orbits occurs, which separates tonic and burst activity. This bifurcation is responsible for the excitable nature of the system, and is the basis for the "type I" designation. We verify the existence of this transition from in vitro recordings of a number of actual pyramidal cells. A scaling relationship between the magnitude and duration of a current pulse required to induce a burst is derived. We also observe this type of burst excitability and the scaling relationships in a multicompartmental model that is driven by realistic stochastic synaptic inputs mimicking sensory input. We conclude by discussing the relevance of burst excitability to communication between weakly electric fish.

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PubMedDate not supplied

To burst or not to burst?

It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.

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