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Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification.
Inter-areal coherence has been hypothesized as a mechanism for inter-areal communication. Indeed, empirical studies have observed an increase in inter-areal coherence with attention. Yet, the mechanisms underlying changes in coherence remain largely unknown. Both attention and stimulus salience are associated with shifts in the peak frequency of gamma oscillations in V1, which suggests that the frequency of oscillations may play a role in facilitating changes in inter-areal communication and coherence. In this study, we used computational modeling to investigate how the peak frequency of a sender influences inter-areal coherence. We show that changes in the magnitude of coherence are largely determined by the peak frequency of the sender. However, the pattern of coherence depends on the intrinsic properties of the receiver, specifically whether the receiver integrates or resonates with its synaptic inputs. Because resonant receivers are frequency-selective, resonance has been proposed as a mechanism for selective communication. However, the pattern of coherence changes produced by a resonant receiver is inconsistent with empirical studies. By contrast, an integrator receiver does produce the pattern of coherence with frequency shifts in the sender observed in empirical studies. These results indicate that coherence can be a misleading measure of inter-areal interactions. This led us to develop a new measure of inter-areal interactions, which we refer to as Explained Power. We show that Explained Power maps directly to the signal transmitted by the sender filtered by the receiver, and thus provides a method to quantify the true signals transmitted between the sender and receiver. Together, these findings provide a model of changes in inter-areal coherence and Granger-causality as a result of frequency shifts.
Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons’ capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.
The function of the cerebral cortex essentially depends on the ability to form functional assemblies across different cortical areas serving different functions. Here we investigated how developmental hearing experience affects functional and effective interareal connectivity in the auditory cortex in an animal model with years-long and complete auditory deprivation (deafness) from birth, the congenitally deaf cat (CDC). Using intracortical multielectrode arrays, neuronal activity of adult hearing controls and CDCs was registered in the primary auditory cortex and the secondary posterior auditory field (PAF). Ongoing activity as well as responses to acoustic stimulation (in adult hearing controls) and electric stimulation applied via cochlear implants (in adult hearing controls and CDCs) were analyzed. As functional connectivity measures pairwise phase consistency and Granger causality were used. While the number of coupled sites was nearly identical between controls and CDCs, a reduced coupling strength between the primary and the higher order field was found in CDCs under auditory stimulation. Such stimulus-related decoupling was particularly pronounced in the alpha band and in top–down direction. Ongoing connectivity did not show such a decoupling. These findings suggest that developmental experience is essential for functional interareal interactions during sensory processing. The outcomes demonstrate that corticocortical couplings, particularly top-down connectivity, are compromised following congenital sensory deprivation.
Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics
(2020)
GABAergic inhibition plays an important role in the establishment and maintenance of cortical circuits during development. Neuregulin 1 (Nrg1) and its interneuron-specific receptor ErbB4 are key elements of a signaling pathway critical for the maturation and proper synaptic connectivity of interneurons. Using conditional deletions of the ERBB4 gene in mice, we tested the role of this signaling pathway at two developmental timepoints in parvalbumin-expressing (PV) interneurons, the largest subpopulation of cortical GABAergic cells. Loss of ErbB4 in PV interneurons during embryonic, but not late postnatal, development leads to alterations in the activity of excitatory and inhibitory cortical neurons, along with severe disruption of cortical temporal organization. These impairments emerge by the end of the second postnatal week, prior to the complete maturation of the PV interneurons themselves. Early loss of ErbB4 in PV interneurons also results in profound dysregulation of excitatory pyramidal neuron dendritic architecture and a redistribution of spine density at the apical dendritic tuft. In association with these deficits, excitatory cortical neurons exhibit normal tuning for sensory inputs, but a loss of state-dependent modulation of the gain of sensory responses. Together these data support a key role for early developmental Nrg1/ErbB4 signaling in PV interneurons as powerful mechanism underlying the maturation of both the inhibitory and excitatory components of cortical circuits.
The traditional view on coding in the cortex is that populations of neurons primarily convey stimulus information through the spike count. However, given the speed of sensory processing, it has been hypothesized that sensory encoding may rely on the spike-timing relationships among neurons. Here, we use a recently developed method based on Optimal Transport Theory called SpikeShip to study the encoding of natural movies by high-dimensional ensembles of neurons in visual cortex. SpikeShip is a generic measure of dissimilarity between spike train patterns based on the relative spike-timing relations among all neurons and with computational complexity similar to the spike count. We compared spike-count and spike-timing codes in up to N > 8000 neurons from six visual areas during natural video presentations. Using SpikeShip, we show that temporal spiking sequences convey substantially more information about natural movies than population spike-count vectors when the neural population size is larger than about 200 neurons. Remarkably, encoding through temporal sequences did not show representational drift both within and between blocks. By contrast, population firing rates showed better coding performance when there were few active neurons. Furthermore, the population firing rate showed memory across frames and formed a continuous trajectory across time. In contrast to temporal spiking sequences, population firing rates exhibited substantial drift across repetitions and between blocks. These findings suggest that spike counts and temporal sequences constitute two different coding schemes with distinct information about natural movies.
Spatial attention increases both inter-areal synchronization and spike rates across the visual hierarchy. To investigate whether these attentional changes reflect distinct or common mechanisms, we performed simultaneous laminar recordings of identified cell classes in macaque V1 and V4. Enhanced V4 spike rates were expressed by both excitatory neurons and fast-spiking interneurons, and were most prominent and arose earliest in time in superficial layers, consistent with a feedback modulation. By contrast, V1-V4 gamma-synchronization reflected feedforward communication and surprisingly engaged only fast-spiking interneurons in the V4 input layer. In mouse visual cortex, we found a similar motif for optogenetically identified inhibitory-interneuron classes. Population decoding analyses further indicate that feedback-related increases in spikes rates encoded attention more reliably than feedforward-related increases in synchronization. These findings reveal distinct, cell-type-specific feedforward and feedback pathways for the attentional modulation of inter-areal synchronization and spike rates, respectively.
Sensory processing relies on interactions between excitatory and inhibitory neurons, which are often coordinated by 30-80Hz gamma oscillations. However, the specific contributions of distinct interneurons to gamma synchronization remain unclear. We performed high-density recordings from V1 in awake mice and used optogenetics to identify PV+ (Parvalbumin) and Sst+ (Somatostatin) interneurons. PV interneurons were highly phase-locked to visually-induced gamma oscillations. Sst cells were heterogeneous, with only a subset of narrow-waveform cells showing strong gamma phase-locking. Interestingly, PV interneurons consistently fired at an earlier phase in the gamma cycle (≈6ms or 60 degrees) than Sst interneurons. Consequently, PV and Sst activity showed differential temporal relations with excitatory cells. In particular, the 1st and 2nd spikes in burst events, which were strongly gamma phase-locked, shortly preceded PV and Sst activity, respectively. These findings indicate a primary role of PV interneurons in synchronizing excitatory cells and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. convey information beyond what can be performed by isolated signals. In other words, any two signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks, and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics exhibit redundancy and synergy for auditory prediction error signals. We observed multiple patterns of redundancy and synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between lower and higher areas in the frontal cortex. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.