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In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca2+) concentration, thus reducing the Ca2+-dependent potassium (K+) current. In this way, the GABA-mediated hyperpolarization replaces Ca2+-dependent K+ current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally.

Long-range synchronization of neural oscillations correlates with distinct behaviors, yet its causal role remains unproven. In mice, tests of anxiety-like avoidance behavior evoke increases in theta-frequency (~8 Hz) oscillatory synchrony between the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC). To test the causal role of this synchrony, we dynamically modulated vHPC-mPFC terminal activity using optogenetic stimulation. Oscillatory stimulation at 8 Hz maximally increased avoidance behavior compared to 2, 4 and 20 Hz. Moreover, avoidance behavior was selectively increased when 8 Hz stimulation was delivered in an oscillatory but not pulsatile manner. Furthermore, 8 Hz oscillatory stimulation enhanced vHPC-mPFC neurotransmission and entrained neural activity in the vHPC-mPFC network, resulting in increased synchrony between vHPC theta activity and mPFC spiking. These data suggest a privileged role for vHPC-mPFC theta-frequency communication in generating avoidance behavior, and provide direct evidence that synchronized oscillations play a role in facilitating neural transmission and behavior.

Connections between the hippocampus (HC) and medial prefrontal cortex (mPFC) are critical for working memory; however, the precise contribution of this pathway is a matter of debate. One suggestion is that it may stabilize retrospective memories of recently encountered task-relevant information. Alternatively, it may be involved in encoding prospective memories, or the internal representation of future goals. To explore these possibilities, simultaneous extracellular recordings were made from mPFC and HC of rats performing the delayed spatial win-shift on a radial maze. Each trial consisted of a training-phase (when 4 randomly chosen arms were open) and test phase (all 8 arms were open but only previously blocked arms contained food) separated by a 60-s delay. Theta power was highest during the delay, and mPFC units were more likely to become entrained to hippocampal theta as the delay progressed. Training and test phase performance were accurately predicted by a linear classifier, and there was a transition in classification for training-phase to test-phase activity patterns throughout the delay on trials where the rats performed well. These data suggest that the HC and mPFC become more strongly synchronized as mPFC circuits preferentially shift from encoding retrospective to prospective information.

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network., Author Summary We recorded the electrical activity of hundreds of neurons simultaneously in brain tissue from mice and we analyzed these signals using state-of-the-art tools from information theory. These tools allowed us to ascertain which neurons were transmitting information to other neurons and to characterize the computations performed by neurons using the inputs they received from two or more other neurons. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to be recipients of information from neurons with a large number of outgoing connections. Interestingly, the number of incoming connections to a neuron was not related to the amount of information that neuron computed. To better understand these results, we built a network model to match the data. Unexpectedly, the model also maximized information transfer in the presence of network-wide correlations. This suggested a way that networks of cortical neurons could deal with common random background input. These results are the first to show that the amount of information computed by a neuron depends on where it is located in the surrounding network.

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar toin vivoreports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recordedin vivofrom rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory.SIGNIFICANCE STATEMENTMany studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.

The local circuitry of the tegmental area (VTA) consists primarily of dopamine (DA) and GABA neurons. Interactions between DA and GABA neurons are critical for regulating DA neuron activity, and thus DA efflux throughout the brain. One striking example that demonstrates the significance of local interactions between DA and GABA neurons is related to nicotine reinforcement. Experimentally, it was shown that activation of nicotinic acetylcholine (nAch) receptors on GABA neurons by Ach leads to an increase in both firing and bursting of the DA neuron, while nicotine produces an opposite effect [1]. In order to investigate the mechanism of this GABA-mediated effect, we created a biologically plausible model of local VTA microcircuitry. The model network consists of a population of GABA neurons innervating one DA neuron. DA neuron dynamics are described by a conductance based model; which includes intrinsic and synaptic currents conducted by NMDA, AMPA, GABA, and nAch receptors. GABA neurons that are described by Wang-Buszaki equations provide inhibitory drive to the DA neuron. Excitatory inputs (Glu and Ach) to DA and GABA neurons are modeled as Poisson-distributed spike trains. Ach pulses act as synchronizing inputs to a population of GABA neurons, due to a transient activation of GABA nAch receptors, while nicotine persistently activates nAch receptor, causing an increase in firing frequency of GABA neurons. Modeling revealed that synchronization of GABA neurons by cholinergic input could provide a mechanism for the elevation of DA neuron firing frequency and bursting. Synchronized GABA inputs act via phasic disinhibition that promotes rebound spikes, but is conditioned on the presence of depolarizing currents to the DA neuron. The opposite effects of nicotine applied to the GABA neuron only (decrease in firing and bursting of DA neuron), could be the result of desynchronization in population of GABA neurons, produced by tonic activation of nAch receptor. A desynchronized GABA population provides constant inhibition to the DA neuron that suppresses firing (see Figure ​Figure1).1). These data highlight the important and powerful role local circuit dynamics VTA. Furthermore, synchrony amongst GABA neurons seems to be a critical intermediary that regulates DA neuron activity and, by extension, DA release throughout the brain. Figure 1 A) Quantification of Ach mediated firing of DA neuron (firing rate and %SWB) B) Nicotine-elicited modifications in firing rate of DA neuron. Vertical black lines indicate nicotine injection. Horizontal dashed lines indicate the baseline firing rate. ...

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons.

The radial arm maze (RAM) is a foraging task that is often used to assess executive function guided decision-making [1-3]. Optimal foraging strategies on this task require the integration of executive and memory systems, which include retrospective and prospective codes. To explore the neural basis of decision-making during RAM performance, the current study acquired multielectrode single-unit and local field potential (LFP) recordings simultaneously in hippocampus (HC) and anterior cingulate (ACC). Initially, rats were presented with four of the eight total maze arms open at once ("training phase"), and the rest of the arms were opened up after the subjects collected the rewards and a one-minute delay was completed ("test phase"). Any arm re-entries were counted as errors, and only trials with one test phase error or less were used in the analysis. ACC unit firing was elevated at choice points and reduced at reward points. Moreover, both ACC and the HC showed elevated theta power shortly prior to the reward point and sharp wave ripples shortly after reward acquisition on correct choices only, which is consistent with findings in other choice tasks [4,5]. These observations held during test phase (Figure ​(Figure1,1, right panels) and were disrupted during the training phase in ACC, but not the HC (Figure ​(Figure1,1, left panels). It has been suggested that HC sharp wave ripples contain episodes of replay of visited locations and theta of locations ahead of the animal [4,5] - information necessary for decision-making using both prospective and retrospective codes. Theta and ripples are present in the HC during both training and test phases, consistent with lesion evidence that hippocampus is necessary for both prospective and retrospective strategies [1]. In ACC, theta and ripples are only present during test phase and not during training phase, which is in line the evidence showing that prefrontal cortex is only necessary for prospective coding [1]. These task phase-dependent observations may help explain how HC and ACC networks integrate information related to prospective and retrospective codes. Figure 1 Average hippocampal and prefrontal theta and SWR power centered on reward acquisition. During test choices (B, D), PFC (D) activity resembles that in HC (B). On the other hand, during training (A, C), HC (A) shows the pattern of activity associated with ...