Dynamical cell assemblies in the rat auditory cortex in a reaction-time task

A simple model of the electrosensory electromotor loop in Gymnotus omarorum

This article introduces and tests a simple model that describes a neural network found in nature, the electrosensory control of an electromotor pacemaker. The cornerstone of the model is an early-stage filter based on the subtraction of a feedforward integrated version of the recent sensory past from the present input signal. The output of this filter governs the modulation of a premotor pacemaker command driving the sensory signal carrier generation and, in consequence, the timing of subsequent electrosensory input. This early filter has a biological parallel in the known connectivity of the first electrosensory relay within the brain stem of the weakly electric fish Gymnotus omarorum. Our biomimetic model of this active, perception-driven action-sensation cycle was contrasted with previously published and here provided new data. When the amplitude of the electrosensory input was manipulated to mimic previous experiments on the novelty detection characteristics, the model reproduces them rather faithfully. In addition, when we applied continuous variations to the input it shows that increases in stimulus amplitudes are followed by increases in the EOD rate, but decreases do not cause rate modulation suggesting a rectification in some stage of the loop. These behavioral experiments confirmed results generated the simulations suggesting that beyond explaining the novelty detection process this simple model is a good description of the electrosensory -electromotor loop in pulse weakly electric fish.

Getting the news in milliseconds: The role of early novelty detection in active electrosensory exploration

The pulse emitting weakly electric fish Gymnotus omarorum shows stereotyped "novelty responses" consisting of a transient acceleration of the rhythm of a self-emitted electric organ discharge that carries electrosensory signals. Here we show that rapid increases in electric image amplitude cause a "novelty detection potential" in the first electrosensory relay. This sign precedes and its amplitude predicts, the amplitude of the subsequent behavioral novelty response. Current source density analyses indicates its origin ar the layers of the electrosensory lobe where the main output neurons occur. Two types of units, referred to as "ON" and "OFF". Were recorded there in decerebrated fish. Firing probability of "OFF" units drastically decreased after a stepwise increase in electric image. By contrast, the very first novel stimuli after the increase evoked a sharp peak in firing rate of "ON" units followed by a very fast adaptation phase that contrasted with the slow adaptation observed in previous recordings of primary afferents. The amplitudes of this peak, the novelty detection potential, and the behavioral novelty responses, show the same dependence on the departure of the newest stimulus intensity from the weighted average of preceding ones suggesting that the signals encoded by "ON" neurons underlay the novelty detection potential, propagates through the hierarchical organization of the electromotor control, and finally contribute to accelerate the electric organ discharge rate. This suggests that detecting novelty at the very early processing stage of electrosensory signals is essential to adapt the electrosensory sampling rate to exploration requirements as they change dynamically.

State Transitions During Discrimination Learning in the Gerbil Auditory Cortex Analyzed by Network Causality Metrics

This work studies the evolution of cortical networks during the transition from escape strategy to avoidance strategy in auditory discrimination learning in Mongolian gerbils trained by the well-established two-way active avoidance learning paradigm. The animals were implanted with electrode arrays centered on the surface of the primary auditory cortex and electrocorticogram (ECoG) recordings were made during performance of an auditory Go/NoGo discrimination task. Our experiments confirm previous results on a sudden behavioral change from the initial naïve state to an avoidance strategy as learning progresses. We employed two causality metrics using Granger Causality (GC) and New Causality (NC) to quantify changes in the causality flow between ECoG channels as the animals switched to avoidance strategy. We found that the number of channel pairs with inverse causal interaction significantly increased after the animal acquired successful discrimination, which indicates structural changes in the cortical networks as a result of learning. A suitable graph-theoretical model is developed to interpret the findings in terms of cortical networks evolving during cognitive state transitions. Structural changes lead to changes in the dynamics of neural populations, which are described as phase transitions in the network graph model with small-world connections. Overall, our findings underscore the importance of functional reorganization in sensory cortical areas as a possible neural contributor to behavioral changes.

Canine sense and sensibility: tipping points and response latency variability as an optimism index in a canine judgement bias assessment

Recent advances in animal welfare science used judgement bias, a type of cognitive bias, as a means to objectively measure an animal's affective state. It is postulated that animals showing heightened expectation of positive outcomes may be categorised optimistic, while those showing heightened expectations of negative outcomes may be considered pessimistic. This study pioneers the use of a portable, automated apparatus to train and test the judgement bias of dogs. Dogs were trained in a discrimination task in which they learned to touch a target after a tone associated with a lactose-free milk reward and abstain from touching the target after a tone associated with water. Their judgement bias was then probed by presenting tones between those learned in the discrimination task and measuring their latency to respond by touching the target. A Cox's Proportional Hazards model was used to analyse censored response latency data. Dog and Cue both had a highly significant effect on latency and risk of touching a target. This indicates that judgement bias both exists in dogs and differs between dogs. Test number also had a significant effect, indicating that dogs were less likely to touch the target over successive tests. Detailed examination of the response latencies revealed tipping points where average latency increased by 100% or more, giving an indication of where dogs began to treat ambiguous cues as predicting more negative outcomes than positive ones. Variability scores were calculated to provide an index of optimism using average latency and standard deviation at cues after the tipping point. The use of a mathematical approach to assessing judgement bias data in animal studies offers a more detailed interpretation than traditional statistical analyses. This study provides proof of concept for the use of an automated apparatus for measuring cognitive bias in dogs.

Integration and transmission of distributed deterministic neural activity in feed-forward networks

A ten layer feed-forward network characterized by diverging/converging patterns of projection between successive layers of regular spiking (RS) neurons is activated by an external spatiotemporal input pattern fed to Layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and three types of neuron models for the network, i.e. either a network formed either by neurons modeled by exponential integrate-and-fire dynamics (RS-EIF, Fourcaud-Trocmé et al., 2003), or by simple spiking neurons (RS-IZH, Izhikevich, 2004) or by multiple-timescale adaptive threshold neurons (RS-MAT, Kobayashi et al., 2009), given five intensities for the background activity. The assessment of the temporal structure embedded in the output spike trains was carried out by detecting the preferred firing sequences for the reconstruction of de-noised spike trains (Asai and Villa, 2008). We confirmed that the RS-MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input. We observed that this structure could be propagated not only up to the 10th layer but in some cases it was retained better beyond the 4th downstream layers. This study suggests that diverging/converging network structures, by the propagation of synfire activity, could play a key role in the transmission of complex temporal patterns of discharges associated to deterministic nonlinear activity. This article is part of a Special Issue entitled Neural Coding.

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings

This study demonstrates the practical application of the pattern grouping algorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 2000; accompanying article), to data sets including up to 30 simultaneously recorded spike trains. The analysis of a large network of simulated neurons shows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cortex was reversibly inactivated suggest that widespread cell assemblies contribute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal cortex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period preceding the cue was associated with changes in reaction time. In conclusion, neuronal network interactions may generate spatiotemporal firing patterns detectable by PGA. We provide evidence of such patterned activity associated with specific animal's behavior, thus suggesting the existence of complex temporal coding schemes in the higher nervous centers of the brain.

Long-term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex

This paper describes a detailed protocol for obtaining chronic, multi-site unit recordings in cerebral cortex of awake animals for periods of three months or more. The protocol includes details for making relatively simple and inexpensive implantable multichannel electrodes that consist of arrays of separate microwires. The results reported in this paper suggest that a viable implant will have discriminable unit activity on about 80% of the electrodes, resulting in, on average, the simultaneous unit recording of upwards of 60 units during a daily recording session. The active electrodes during one recording session tend to remain active in subsequent recording sessions for several weeks. Using the methods described here, implants have been constructed which incorporate several different electrode materials, coatings, sizes, and electrode separation within a single array. These microwire electrode arrays provide the basic technology for obtaining unit recordings for several months. This provides a model system for studying biocompatibility of neural implants, which is a critical component for the development of neural implants that have an indefinite working span.

c-Fos expression in the auditory pathways related to the significance of acoustic signals in rats performing a sensory-motor task

Neuronal activity was established in the auditory pathways in relation to behavioural response and cognitive information processing during a sensory-motor acoustic learning. Rats were trained in three consecutive phases. The first phase was an association between an auditory stimulus and a food reward; the second phase a simple discrimination between two sounds of different frequency components, and the third phase a more complex discrimination involving both spectral and spatial sound dimensions. Auditory stimuli were bursts of complex sounds lasting 500 ms. Neuronal activity related to the behaviourally relevant stimuli was established in 20 "learning" rats undergoing this protocol, which were progressively sacrificed at the beginning, middle and end of each phase. For comparison, activity was also established in four "control" rats exposed to the same stimuli delivered pseudo-randomly, thus carrying no behavioural meaning. Neuronal activity was assessed immunocytochemically using the functional marker Fos. To establish a baseline, two rats were unexposed to controlled acoustic stimulation ("unstimulated" rats). In the superior olivary complex (SOC), inferior colliculus (IC) and medial geniculate body (MGB), the number of Fos-like immunopositive cells was comparable in "learning" and "control" animals, but higher than in the "unstimulated" rats. In the auditory cortex (AC), most prominently in the secondary area Te2, the number of Fos-like positive cells differed between "learning" and "control" rats, suggesting that the auditory cortical areas may be involved in the encoding of the behavioural significance of the acoustic stimuli.

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.

Computer assisted neurophysiology by a distributed Java program

We have developed a distributed software package which allows neurophysiologists to have at hand a set of network and graphic tools in order to perform the analysis of data collected from the electrical activity of neurons, studied in the experimental laboratory. This system is built around three major components. First, the experimental data are processed in order to select specific data files from the networked computer and display the spike trains as raster dots. The second part selects one type of analysis (e.g., cross-correlation, out of a set of possible choices) after configuration of some specific parameters. Finally, the third component deals with the results of data analyses, which are selected and displayed on a multi-parameter graphic "ring binder." We have used the Java programming language to implement this application. This recent object-oriented language offers platform-independent software tools including graphic package and standard network protocols.