Long-Term Recordings Improve the Detection of Weak Excitatory–Excitatory Connections in Rat Prefrontal Cortex

Abstract

Characterization of synaptic connectivity is essential to understanding neural circuit dynamics. For extracellularly recorded spike trains, indirect evidence for connectivity can be inferred from short-latency peaks in the correlogram between two neurons. Despite their predominance in cortex, however, significant interactions between excitatory neurons (E) have been hard to detect because of their intrinsic weakness. By taking advantage of long duration recordings, up to 25 h, from rat prefrontal cortex, we found that 7.6% of the recorded pyramidal neurons are connected. This corresponds to ϳ70% of the local E-E connection probability that has been reported by paired intracellular recordings (11.6%). This value is significantly higher than previous reports from extracellular recordings, but still a substantial underestimate. Our analysis showed that long recording times and strict significance thresholds are necessary to detect weak connections while avoiding false-positive results, but will likely still leave many excitatory connections undetected. In addition, we found that hyper-reciprocity of connections in prefrontal cortex that was shown previously by paired intracellular recordings was only present in short-distance, but not in long distance (ϳ300 micrometers or more) interactions. As hyper-reciprocity is restricted to local clusters, it might be a minicolumnar effect. Given the current surge of interest in very high-density neural spike recording (e.g., NIH BRAIN Project) it is of paramount importance that we have statistically reliable methods for estimating connectivity from cross-correlation analysis available. We provide an important step in this direction.

Network‐selectivity and stimulus‐discrimination in the primary visual cortex: cell‐assembly dynamics

“…F) and are indicative of time‐locked activity between the neurons. Because neurons physically close together are densely innervated to each other (Gilbert & Wiesel, ; Binzegger et al ., ; Stepanyants et al ., ; Kampa et al ., ), these time‐relationships may be inferred as putative synaptic links between neurons (Barthó et al ., ; Fujisawa et al ., ; Bharmauria et al ., ; Schwindel et al ., ). Neuron 2 (reference) projects onto neuron 1 (target), as the CCG significantly peaks ( p = 2.5%) within 5 ms after the zero mark.…”

“…The cumulative histogram (above CCG) of the discharge rate of the target cell further illustrates that once the reference neuron produced a spike, the firing rate of the target unit was considerably modulated, thus signifying the functional link between neurons. Neurons 1 (reference) and 4 (target) are reciprocally connected to each other, implying the recurrent excitation between them (Schwindel et al ., ). First, the target neuron excites the reference neuron with a probability of 2% and then after a brief period of inhibition from third neuron(s) (the trough at zero in the CCG) the reference neuron back‐projects and excites the target neuron with a probability of 1.7%.…”

“…Furthermore, we labelled these interactions as excitatory or inhibitory depending upon the position of peaks or troughs in CCGs (Barthó et al ., ; Schwindel et al ., ). Out the RS–RS interactions, 134 were deemed unidirectional (excitatory in one direction, e.g.…”

“…H), the strength of the bidirectional connections (mean ± SEM: 3.04 ± 0.25%) was significantly (unpaired t ‐test, P = 0.0007) higher than the unidirectional connections (2.16 ± 0.12%). In brief, the bidirectional connections indicate strong recurrent excitation between neuronal circuits, suggesting that recurrent excitation between neurons in an assembly plays a pivotal role in feature encoding, as has been reported in the literature (Bopp et al ., ; Schwindel et al ., ; Cossell et al ., ). Note that the calculations for the bidirectional connections were performed over all possible connections representing bidirectional connections (i.e.…”

Comparative effects of adaptation on layers II–III and V–VI neurons in cat V1

“…The closer the OSI is to 1, the stronger the orientation selectivity. Finally, the sorted neurons were classified into regular and fast spikes on the basis of the spike width (Bartho et al, 2004;Schwindel et al, 2014;Bharmauria et al, 2015). Spikes with a width ≤ 0.3 ms were identified as fast spiking neurons (FS) and > 0.3 ms as regular spiking (RS) neurons.…”