Len Maler

Spike-Frequency Adaptation Separates Transient Communication Signals from Background Oscillations

Spike-frequency adaptation is a prominent feature of many neurons. However, little is known about its computational role in processing behaviorally relevant natural stimuli beyond filtering out slow changes in stimulus intensity. Here, we present a more complex example in which we demonstrate how spike-frequency adaptation plays a key role in separating transient signals from slower oscillatory signals. We recorded in vivo from very rapidly adapting electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus. The firing-frequency response of electroreceptors to fast communication stimuli (“small chirps”) is strongly enhanced compared with the response to slower oscillations (“beats”) arising from interactions of same-sex conspecifics. We are able to accurately predict the electroreceptor afferent response to chirps and beats, using a recently proposed general model for spike-frequency adaptation. The parameters of the model are determined for each neuron individually from the responses to step stimuli. We conclude that the dynamics of the rapid spike-frequency adaptation is sufficient to explain the data. Analysis of additional data from step responses demonstrates that spike-frequency adaptation acts subtractively rather than divisively as expected from depressing synapses. Therefore, the adaptation dynamics is linear and creates a high-pass filter with a cutoff frequency of 23 Hz that separates fast signals from slower changes in input. A similar critical frequency is seen in behavioral data on the probability of a fish emitting chirps as a function of beat frequency. These results demonstrate how spike-frequency adaptation in general can facilitate extraction of signals of different time scales, specifically high-frequency signals embedded in slower oscillations.

Organization of galanin-like immunoreactive neuronal systems in weakly electric fish (Apteronotus leptorhynchus)

Immunohistochemical procedures were used to investigate the distribution of galanin-like immunoreactive neuronal somata, fiber pathways and apparent termination fields in the gymnotiform brain. Immunoreactive somata were observed only in the hypothalamus and were confined to preoptic, lateral and caudal hypothalamic regions. Within these areas, positive cells tended to be most concentrated in juxtaventricular nuclei. Dense immunoreactive fiber systems originating from hypothalamic regions were seen to project in separate or coalescing fiber bundles to the basal telencephalan, thalamus/tuberal diencephalon, midbrain and brainstem. The density of positive axons and boutons was quite variable, but regions which displayed the most massive network of axons included structures within the hypothalamus itself (anterior periventricular preoptic nucleus, caudal and lateral hypothalamus), ventral telencephalon (superior and ventral subdivisions), thalamic/tuberal areas (central posterior nucleus and tuberal neuropil within the ventral territory of the prepacemaker nucleus) and brainstem nuclei (dorsal reticular nucleus and the medial paralemniscal nucleus). Within these areas axons appeared more randomly distributed and varicose than along fiber tracs, and in counterstained sections were occasionally seen in apposition to unstained neuronal cell bodies and dendrites. In addition, a system of fibers was seen in the neurointermediate lobe of the pituitary. It is concluded that galanin-like immunoreactive neurons in the gymnotiform brain have a more restricted distribution than those in mammals, and that the major fiber systems emanating from the hypothalamus resemble the diverse projections of the tuberomammillary nucleus of higher vertebrates. The anatomy of galanin-like immunoreactive systems in the apteronotid brain suggests a role in neuroendocrine regulation and an involvement with anatomical areas controlling aggressive and courtship behaviour.

Regulated expression of N‐methyl‐D‐aspartate receptors and associated proteins in teleost electrosensory system and telencephalon

Several types of N‐methyl‐D‐aspartate (NMDA) receptor‐dependent synaptic plasticity are characterized by differences in polarity, induction parameters, and duration, which depend on the interactions of NMDARs with intracellular synaptic and signaling proteins. Here, we examine the NMDAR signaling components in the brain of the weakly electric fish Apteronotus leptorhynchus. Compared with mammalian orthologs, high levels of sequence conservation for known functional sites in both NMDAR subunits (NR1, NR2A–C) and signaling proteins (fyn tyrosine kinase, RasGRF‐1 and ‐2) were found. In situ hybridization analysis demonstrated that, similar to the case in the adult mammal brain, NR2A and NR2B are expressed at moderate levels in most brain regions and at very high levels in the dorsal telencephalon. RasGRF‐1 and fyn have a similar distribution and appear to be coexpressed with NR2B in telencephalic regions known to support learning and long‐term memory. Both NR2A and NR2B are highly expressed in pyramidal cells of the electrosensory lateral line lobe (ELL) known to exhibit the short‐term synaptic plasticity that underlies adaptive feedback cancellation of redundant sensory input. In contrast, nonplastic pyramidal cells expressed only the NR2A subunit. Furthermore, field recordings show that ifenprodil‐sensitive NR2B‐containing NMDARs predominate for the plastic feedback input to ELL pyramidal cells. However, RasGRF‐1 and fyn are expressed only at low levels in a subset of these pyramidal cells. Our data suggest that NMDAR functions are highly conserved between fish and mammals and that synaptic plasticity dynamics in different brain regions are related to the expression patterns of the synaptic signaling proteins interacting with NMDARs. J. Comp. Neurol. 505:644–668, 2007.

A novel time-stamp mechanism transforms egocentric encounters into an allocentric spatial representation

Learning the spatial organization of the environment is essential for most animals’ survival. This often requires the animal to derive allocentric information about the environment from egocentric sensory and motor experience. The neural circuits and mechanisms underlying this transformation are currently unknown. We addressed this problem in electric fish, which can precisely navigate in complete darkness and whose requisite brain circuitry is relatively simple. We conducted the first neural recordings in the preglomerular complex, the thalamic region exclusively connecting the optic tectum with the spatial learning circuits in the dorsolateral pallium. While tectal egocentric information was eliminated in preglomerular neurons, the time-intervals between object encounters were precisely encoded. We show that this highly-reliable temporal information, combined with a speed signal, can permit accurate path-integration that then enables computing allocentric spatial relations. Our results suggest that similar mechanisms are involved in spatial learning via sequential encounters in all vertebrates.

Electrical localization of weakly electric fish using neural networks

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.