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Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes., In order to address whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, Prince et al. used optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. They demonstrated that FS and RS interneurons had differential sensitivity to changes in synchrony and rate, which advances our understanding of neural processing in the neocortex.

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as they use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral (“what”) and dorsal (“where”) pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. According to this conjecture, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of information. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. In line with this conjecture, different responses to expected and unexpected sensory features have been observed in spiking and somatic calcium events. However, it remains unknown whether these unexpected event signals occur in the distal apical dendrites where many top-down signals are received, and whether these signals govern subsequent changes in the brain’s stimulus representations. Here, we show that both somata and distal apical dendrites of cortical pyramidal neurons exhibit distinct unexpected event signals that systematically change over days. These findings were obtained by tracking the responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons over multiple days in primary visual cortex of awake, behaving mice using two-photon calcium imaging. Many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. Interestingly, these responses evolved in opposite directions in the somata and distal apical dendrites. These differences between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively.

Sensory information–driven spikes are synchronized across cortical layers by distinct subtypes of interneurons., Synchronization of precise spike times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of parvalbumin (PV)– and somatostatin (SST)–positive interneurons to the synchronization of spike times across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contributions to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in the spatiotemporal synchronization of precise spike times.

Background Abnormal accumulation of amyloid β1–42 oligomers (AβO1–42), a hallmark of Alzheimer’s disease, impairs hippocampal theta-nested gamma oscillations and long-term potentiation (LTP) that are believed to underlie learning and memory. Parvalbumin-positive (PV) and somatostatin-positive (SST) interneurons are critically involved in theta-nested gamma oscillogenesis and LTP induction. However, how AβO1–42 affects PV and SST interneuron circuits is unclear. Through optogenetic manipulation of PV and SST interneurons and computational modeling of the hippocampal neural circuits, we dissected the contributions of PV and SST interneuron circuit dysfunctions on AβO1–42-induced impairments of hippocampal theta-nested gamma oscillations and oscillation-induced LTP. Results Targeted whole-cell patch-clamp recordings and optogenetic manipulations of PV and SST interneurons during in vivo-like, optogenetically induced theta-nested gamma oscillations in vitro revealed that AβO1–42 causes synapse-specific dysfunction in PV and SST interneurons. AβO1–42 selectively disrupted CA1 pyramidal cells (PC)-to-PV interneuron and PV-to-PC synapses to impair theta-nested gamma oscillogenesis. In contrast, while having no effect on PC-to-SST or SST-to-PC synapses, AβO1–42 selectively disrupted SST interneuron-mediated disinhibition to CA1 PC to impair theta-nested gamma oscillation-induced spike timing-dependent LTP (tLTP). Such AβO1–42-induced impairments of gamma oscillogenesis and oscillation-induced tLTP were fully restored by optogenetic activation of PV and SST interneurons, respectively, further supporting synapse-specific dysfunctions in PV and SST interneurons. Finally, computational modeling of hippocampal neural circuits including CA1 PC, PV, and SST interneurons confirmed the experimental observations and further revealed distinct functional roles of PV and SST interneurons in theta-nested gamma oscillations and tLTP induction. Conclusions Our results reveal that AβO1–42 causes synapse-specific dysfunctions in PV and SST interneurons and that optogenetic modulations of these interneurons present potential therapeutic targets for restoring hippocampal network oscillations and synaptic plasticity impairments in Alzheimer’s disease.

Synchronization of precise spike-times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of two classes of inhibitory interneurons: parvalbumin (PV)- and somatostatin (SST)-positive interneurons to spike-timing synchronization across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike-times when instantaneous firing-rates are low (12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in spatio-temporal synchronization of precise spike-times.

Anxiety is an adaptive response to potentially threatening situations. Exaggerated and uncontrolled anxiety responses become maladaptive and lead to anxiety disorders. Anxiety is shaped by a network of forebrain structures, including the hippocampus, septum, and prefrontal cortex. In particular, neural inputs arising from the ventral hippocampus (vHPC) to the lateral septum (LS) and medial prefrontal cortex (mPFC) are thought to serve as principal components of the anxiety circuit. However, the role of vHPC-to-LS and vHPC-to-mPFC signals in anxiety is unclear, as no study has directly compared their behavioral contribution at circuit level. We targeted LS-projecting vHPC cells and mPFC-projecting vHPC cells by injecting the retrogradely propagating canine adenovirus encoding Cre recombinase into the LS or mPFC, and injecting a Cre-responsive AAV (AAV8-hSyn-FLEX-hM3D or hM4D) into the vHPC. Consequences of manipulating these neurons were examined in well-established tests of anxiety. Chemogenetic manipulation of LS-projecting vHPC cells led to bidirectional changes in anxiety: activation of LS-projecting vHPC cells decreased anxiety whereas inhibition of these cells produced opposite anxiety-promoting effects. The observed anxiety-reducing function of LS-projecting cells was in contrast with the function of mPFC-projecting cells, which promoted anxiety. In addition, double retrograde tracing demonstrated that LS- and mPFC-projecting cells represent two largely anatomically distinct cell groups. Altogether, our findings suggest that the vHPC houses discrete populations of cells that either promote or suppress anxiety through differences in their projection targets. Disruption of the intricate balance in the activity of these two neuron populations may drive inappropriate behavioral responses seen in anxiety disorders.

Guaranteeing that synaptic plasticity leads to effective learning requires a means for assigning credit to each neuron for its contribution to behavior. The 'credit assignment problem' refers to the fact that credit assignment is non-trivial in hierarchical networks with multiple stages of processing. One difficulty is that if credit signals are integrated with other inputs, then it is hard for synaptic plasticity rules to distinguish credit-related activity from non-credit-related activity. A potential solution is to use the spatial layout and non-linear properties of dendrites to distinguish credit signals from other inputs. In cortical pyramidal neurons, evidence hints that top-down feedback signals are integrated in the distal apical dendrites and have a distinct impact on spike-firing and synaptic plasticity. This suggests that the distal apical dendrites of pyramidal neurons help the brain to solve the credit assignment problem.

SummaryThe ability to generate action potentials (spikes) in response to synaptic input determines whether a neuron participates in information processing. How a developing neuron becomes an active participant in a circuit or whether this process is activity dependent is not known, especially as spike-dependent plasticity mechanisms would not be available to non-spiking neurons. Here we use the optic tectum of awake Xenopus laevis tadpoles to determine how a neuron becomes able to generate sensory-driven spikes in vivo. At the onset of vision, many tectal neurons do not exhibit visual spiking behavior, despite being intrinsically excitable and receiving visuotopically organized synaptic inputs. However, a brief period of visual stimulation can drive these neurons to start generating stimulus-driven spikes. This conversion relies upon a selective increase in glutamatergic input and requires depolarizing GABAergic transmission and NMDA receptor activation. This permissive form of experience-dependent plasticity enables a neuron to start contributing to circuit function.

Following learning, increased coupling between spindle oscillations in the medial prefrontal cortex (mPFC) and ripple oscillations in the hippocampus is thought to underlie memory consolidation. However, whether learning-induced increases in ripple-spindle coupling are necessary for successful memory consolidation has not been tested directly. In order to decouple ripple-spindle oscillations, here we chemogenetically inhibited parvalbumin-positive (PV+) interneurons, since their activity is important for regulating the timing of spiking activity during oscillations. We found that contextual fear conditioning increased ripple-spindle coupling in mice. However, inhibition of PV+ cells in either CA1 or mPFC eliminated this learning-induced increase in ripple-spindle coupling without affecting ripple or spindle incidence. Consistent with the hypothesized importance of ripple-spindle coupling in memory consolidation, post-training inhibition of PV+ cells disrupted contextual fear memory consolidation. These results indicate that successful memory consolidation requires coherent hippocampal-neocortical communication mediated by PV+ cells., eLife digest Sleep contributes to the strengthening of memories. During non-dreaming sleep, neurons in regions of the brain that form and store memories – such as the hippocampus and prefrontal cortex – fire in rhythmic waves. The neurons in the hippocampus tend to fire during a wave that repeats up to 250 times per second, called sharp-wave ripples. Meanwhile, in the prefrontal cortex, the neurons tend to fire during a lower frequency wave that repeats 12 to 15 times per second, called spindles. During sleep and quiet wakefulness, hippocampal ripples often synchronize with prefrontal spindles; that is, both waves tend to occur at approximately the same time. Many neuroscientists think this allows the brain regions to better communicate with one another, which in turn should help the brain to strengthen memories. Consistent with this possibility, rodents that learn a new task show more synchrony between ripples and spindles afterwards. But no one had actually tested whether this increase in ripple-spindle synchrony does strengthen the rodent’s memory of the task. It was also unclear how the brain achieves such an increase. Xia et al. suspected that this process involved a group of inhibitory brain cells called parvalbumin-positive interneurons. These cells act like timekeepers, and help to synchronize the firing of groups of neurons. Xia et al. now show that training mice to associate an environment with a mild electric shock made it more likely that the animals would show ripple-spindle synchrony. Yet, inhibiting the activity of parvalbumin-positive interneurons in either the hippocampus or prefrontal cortex blocked this effect. It also prevented sleep from strengthening the animals’ memory of the link between the environment and the shock. Patients with Alzheimer’s disease have fewer parvalbumin-positive interneurons. By showing that these neurons help strengthen new memories, these findings may explain why losing them can impair memory. Restoring or replacing interneuron activity could be a promising therapeutic avenue to explore.

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment (“relevance”), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to “gate” both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia., Author summary Individuals with schizophrenia have difficulty ignoring ideas and experiences that most people would treat as unimportant. There is evidence that this may be due to changes in neuronal inhibition, suggesting that inhibitory neurons may be involved in learning to ignore irrelevant inputs. By developing a computational model that learns relevance and irrelevance through changes in the strength of feedforward inhibition, we are able to simulate many specific effects of inhibitory neuron dysfunction on behavior. We also show two computational advantages to this mechanism: (1) if relevance is signaled by the level of excitatory activity, then downstream circuits can easily avoid learning from irrelevant stimuli, (2) relevance learning can occur simultaneously with other types of learning. The model therefore offers insight into the relationships between neural inhibition and behavior, including symptoms of schizophrenia.

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions to cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which inputs are relevant and which irrelevant. Here, we develop an abstract but biologically plausible neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes expected magnitude of reward or punishment (”relevance”), which can be trained using a temporal difference learning mechanism acting on feed-forward inputs to inhibitory interneurons. The model exhibits learned irrelevance and blocking, which become impaired following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer, the relevance code is capable of modulating learning and activity. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

Synaptic inhibition is a critical regulator of neuronal excitability, and in the mature brain the majority of synaptic inhibition is mediated by Cl−-permeable GABAA receptors. Unlike other physiologically relevant ions, Cl− is dynamically regulated, and alterations in the Cl− gradient can have significant impact on neuronal excitability. Due to changes in the neuronal Cl− concentration, GABAergic transmission can bidirectionally regulate the induction of excitatory synaptic plasticity and gate the closing of the critical period for monocular deprivation in visual cortex. GABAergic circuitry can also provide a powerful restraining mechanism for the spread of excitation, however Cl− extrusion mechanisms can become overwhelmed and GABA can paradoxically contribute to pathological excitation such as the propagation of seizure activity.

During the development of sensory systems, receptive fields are modified by stimuli in the environment. This is thought to rely on learning algorithms that are sensitive to correlations in spike timing between cells, but the manner in which developing circuits selectively exploit correlations that are related to sensory inputs is unknown. We recorded from neurons in the developing optic tectum of Xenopus laevis and found that repeated presentation of moving visual stimuli induced receptive field changes that reflected the properties of the stimuli and that this form of learning was disrupted when GABAergic transmission was blocked. Consistent with a role for spike timing-dependent mechanisms, GABA blockade altered spike-timing patterns in the tectum and increased correlations between cells that would affect plasticity at intratectal synapses. This is a previously unknown role for GABAergic signals in development and highlights the importance of regulating the statistics of spiking activity for learning.

The retinotectal pathway of Xenopus laevis is a well-established experimental model for studying activity-dependent processes during visual system development. Such processes can be guided by stimulus-evoked activity patterns, which depend on the refractive characteristics of the eye. Previous work has shown that many animals are hyperopic at early developmental stages due to immature refractive properties. Whether this is also the case for Xenopus laevis is unknown. Here, we measure the focal length of the lens and the size of the eye of embryos at different stages and find that Xenopus laevis exhibits a similar shift from hyperopia to emmetropia. At early stages, immediately after innervation of the tectum by the optic nerve, Xenopus embryos are hyperopic. Soon afterwards the focal length of the lens decreases and the eye converges to a state of emmetropia. Despite being hyperopic we find that some visuospatial information is available to the young circuit. Calculations based on the optical properties of the eye show that even when the animals are hyperopic the blurred retinal image provides a crude spatial resolution. Furthermore, using whole-cell recordings in the optic tectum combined with visual stimulation through the intact eye, we show that tectal neurons in hyperopic embryos have spatially restricted glutamatergic receptive fields. Our data demonstrate that Xenopus laevis eyes undergo a process of developmental emmetropization, and suggest that despite an initial stage of suboptimal image formation there is potentially enough information to guide activity-dependent refinements of the retinotectal pathway from the onset of vision. © 2011 Wiley Periodicals, Inc. Develop Neurobiol 72: 507–519, 2012

In vivo measurement of cortical thickness is a sensitive representation of pathology in neurodegenerative disorders which primarily target the gray mantle. In this study we used magnetic resonance images to describe the patterns of cortical thinning in 11 frontotemporal dementia (FTD), 38 Alzheimer's disease (AD) and 34 healthy elderly (H E ) subjects. AD and FTD displayed significant thinning of the cortical mantle compared to the H E group, but with distinctive distributions. AD subjects had significantly thinner cortex in all lobes whereas FTD compared to H E showed significant differences only in specific regions of frontal and temporal lobes. When compared to AD, the FTD subjects had a trend of thinner cortex in the anterior cingulate region and in selective regions of anterior frontal and temporal regions. In conclusion, the cortical thinning in dementia when compared to H E , is disease specific whereby FTD subjects display a pattern distinct than that seen in Alzheimer's disease.

The present investigation sought to identify which brain regions distinguish pedophilic from nonpedophilic men, using unbiased, automated analyses of the whole brain. T1-weighted magnetic resonance images (MRIs) were acquired from men who demonstrated illegal or clinically significant sexual behaviors or interests (n = 65) and from men who had histories of nonsexual offenses but no sexual offenses (n = 62). Sexual interest in children was assessed by participants' admissions of pedophilic interest, histories of committing sexual offenses against children, and psychophysiological responses in the laboratory to erotic stimuli depicting children or adults. Automated parcellation of the MRIs revealed significant negative associations between pedophilia and white matter volumes of the temporal and parietal lobes bilaterally. Voxel-based morphometry corroborated the associations and indicated that the regions of lower white matter volumes followed, and were limited to, two major fiber bundles: the superior fronto-occipital fasciculus and the right arcuate fasciculus. No significant differences were found in grey matter or in cerebrospinal fluid (CSF). Because the superior fronto-occipital and arcuate fasciculi connect the cortical regions that respond to sexual cues, these results suggest (1) that those cortical regions operate as a network for recognizing sexually relevant stimuli and (2) that pedophilia results from a partial disconnection within that network. Language: en

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the brain optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, the neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network can learn to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful representations---the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the dendritic morphology of neocortical pyramidal neurons., Comment: 41 pages, 11 figures

SummaryMemories are thought to be sparsely encoded in neuronal networks, but little is known about why a given neuron is recruited or allocated to a particular memory trace. Previous research shows that in the lateral amygdala (LA), neurons with increased CREB are selectively recruited to a fear memory trace. CREB is a ubiquitous transcription factor implicated in many cellular processes. Which process mediates neuronal memory allocation? One hypothesis is that CREB increases neuronal excitability to bias neuronal recruitment, although this has not been shown experimentally. Here we use several methods to increase neuronal excitability and show this both biases recruitment into the memory trace and enhances memory formation. Moreover, artificial activation of these neurons alone is a sufficient retrieval cue for fear memory expression, showing that these neurons are critical components of the memory trace. These results indicate that neuronal memory allocation is based on relative neuronal excitability immediately before training.Video Abstract

OBJECTIVE. Abnormalities in cognitive function and brain structure have been reported in acutely ill adolescents with anorexia nervosa, but whether these abnormalities persist or are reversible in the context of weight restoration remains unclear. Brain structure and cognitive function in female subjects with adolescent-onset anorexia nervosa assessed at long-term follow-up were studied in comparison with healthy female subjects, and associations with clinical outcome were investigated.PATIENTS AND METHODS. Sixty-six female subjects (aged 21.3 ± 2.3 years) who had a diagnosis of adolescent-onset anorexia nervosa and treated 6.5 ± 1.7 years earlier in a tertiary care hospital and 42 healthy female control subjects (aged 20.7 ± 2.5 years) were assessed. All participants underwent a clinical examination, magnetic resonance brain scan, and cognitive evaluation. Clinical data were analyzed first as a function of weight recovery (n = 14, RESULTS. Compared with control subjects, participants with anorexia nervosa who remained at low weight had larger lateral ventricles. Twenty-four–hour urinary free-cortisol levels were positively correlated with volumes of the temporal horns of the lateral ventricles and negatively correlated with volumes of the hippocampi in clinical participants. Participants who were amenorrheic or had irregular menses showed significant cognitive deficits across a broad range of many domains.CONCLUSIONS. Female subjects with adolescent-onset anorexia nervosa showed abnormal cognitive function and brain structure compared with healthy individuals despite an extended period since diagnosis. To our knowledge, this is the first study to report a specific relationship between menstrual function and cognitive function in this patient population. Possible mechanisms underlying neural and cognitive deficits with anorexia nervosa are discussed. Additional examination of the effects of estrogen on cognitive function in female subjects with anorexia nervosa is necessary.