There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processing.

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

The composite face paradigm (Young, Hellawell, & Hay, 1987) is widely used to demonstrate holistic perception of faces (Rossion, 2013). In the paradigm, parts from different faces (usually the top and bottom halves) are recombined. The principal criterion for holistic perception is that responses involving the component parts of composites in which the parts are aligned into a face-like configuration are slower and less accurate than responses to the same parts in a misaligned (not face-like) format. This is often taken as evidence that seeing a whole face in the aligned condition interferes with perceiving its separate parts, but it remains unclear to what extent the composite face effect also reflects contributions from other potential sources of interference. We present a new variant of the paradigm involving composites created from top and bottom parts of familiar faces drawn from orthogonal social categories of gender and occupation. This allows us to examine the contributions of differences in relatively visual properties (gender) or relatively semantic properties (occupation) to composite interference and to measure whether variation in a task-irrelevant category (e.g., differences in gender across the parts of the composite when the task is to categorize the occupation of one of the parts) will influence the size of the composite effect. Our findings show that the composite face effect can be modulated by task-irrelevant social categories and that this interference is primarily visual in nature because the influence of face gender is more direct and more consistent than the influence of occupation.

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo. Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.

It is well known that memory can be modulated by emotional stimuli at the time of encoding and consolidation. For example, happy faces create better identity recognition than faces with certain other expressions. However, the influence of facial expression at the time of retrieval remains unknown in the literature. To separate the potential influence of expression at retrieval from its effects at earlier stages, we had participants learn neutral faces but manipulated facial expression at the time of memory retrieval in a standard old/new recognition task. The results showed a clear effect of facial expression, where happy test faces were identified more successfully than angry test faces. This effect is unlikely due to greater image similarity between the neural training face and the happy test face, because image analysis showed that the happy test faces are in fact less similar to the neutral training faces relative to the angry test faces. In the second experiment, we investigated whether this emotional effect is affected by the expression at the time of learning. We employed angry or happy faces as learning stimuli, and angry, happy, and neutral faces as test stimuli. The results showed that the emotional effect at retrieval is robust across different encoding conditions with happy or angry expressions. These findings indicate that emotional expressions do not only affect the stages of encoding and consolidation, but also the retrieval process in identity recognition.

250 words) 14 Information transmission in neural networks is influenced by both short-term synaptic plasticity 15(STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in 16 excitability. Although these effects have been widely characterized in vitro using intracellular 17 recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-18 term dynamics of spike transmission that aims to disentangle the contributions of synaptic and 19 non-synaptic effects based only on observed pre-and postsynaptic spiking. The model includes a 20 dynamic functional connection with short-term plasticity as well as effects due to the recent history 21 of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike 22recordings, we find that the model accurately describes the short-term dynamics of in vivo spike 23 transmission at a diverse set of identified and putative excitatory synapses, including a 24 thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an 25 auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach 26by showing how the spike transmission patterns captured by the model may be sufficient to account 27for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of 28 Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such 29 an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. 30Although short-term synaptic plasticity parameters estimated from ongoing pre-and postsynaptic 31 spiking are highly uncertain, our results are partially consistent with previous intracellular 32 observations in these synapses. 33Significance Statement (120 words) 34 Although synaptic dynamics have been extensively studied and modeled using intracellular 35 recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular 36 spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking 37 depends not only on the amplitude of the current, but also on many other factors, including the 38 activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic 39 neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, 40using only observations of pre-and postsynaptic spiking, aims to describe the dynamics of in vivo 41 spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This 42 approach may provide a novel description of fast, structured changes in spike transmission. 43

8Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. 9While previous methods often treat the detection of each putative connection as a separate hypothesis test, here 10 we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned 11 from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-12 correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to 13 background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all 14 putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory 15 or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the 16 presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from 17 simulated networks, this model outperforms two previously developed synapse detection methods, especially on 18 the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse 19 somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, 20 and the properties of the putative connections are largely consistent with previous research. 21 22 Using in vivo or in vitro multielectrode arrays, the extracellular spiking of hundreds of neurons can be recorded 24 simultaneously. These recordings are allowing new, large-scale studies of neuronal networks (Hahn et al. 2019; 25 Harris et al. 2003; Levenstein et al. 2019; Okun et al. 2015; Tingley and Buzsáki 2018), and the number of 26 neurons that can be simultaneously recorded is increasing approximately exponentially (Stevenson and Kording 27 2011). Depending on the species, brain area, and electrode configuration, these simultaneously recorded 28 neurons can have tens of thousands of potential synapses between them. Detecting and characterizing these 29 synapses represents a major challenge for neural data analysis. Here, we develop a model-based method 30 incorporating network-level constraints on 1) the presynaptic neuron type and 2) the synaptic latencies between 31 pre-and postsynaptic neurons. We examine whether these constraints can improve synapse detection using 32 simulated data and large-scale in vitro multielectrode array recordings. 33 34Detecting synaptic connections from extracellular spike observations is a difficult statistical problem. Since both 35 spiking and synapses themselves are sparse, it is often difficult to distinguish between changes in spike 36 probability that are due to a specific synaptic input, changes that are due other (typically unobserved) inputs, or 37 due to chance. Using extracellular spike data, researchers often identify putative monosynaptic connections by 38 examining cross-correlograms between the spiking of two neurons. If two neu...

Functional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, Granger causality showed that neurons in the rich-club are the dominant drivers of overall population activity and do so in a frequency-specific manner. Moreover, early activity ofinhibitory neurons, along with excitatory neurons within the rich-club, synergistically predicts the duration of neuronal cascades. Together, these results reveal an unexpected role of a highly connected core of inhibitory neurons in driving and sustaining activity in local cortical networks.