Search

Summary Brain size scales with body weight, but a new study has discovered that key circuits in the visual cortex of one of the world’s smallest primates, the mouse lemur, Microcebus murinus, are built at the same scale as their equivalents in larger-brained primates.

Thirty-four years since the small nervous system of the nematode C. elegans was manually reconstructed in the electron microscope (EM)1, ‘high-throughput’ EM techniques now enable the dense reconstruction of neural circuits within increasingly large brain volumes at synaptic resolution2–6. As with C. elegans, however, a key limitation for inferring brain function from neuronal wiring diagrams is that it remains unknown how the structure of a synapse seen in EM relates to its physiological transmission strength. Here, we related structure and function of the same synapses to bridge this gap: we combined paired whole-cell recordings of synaptically connected pyramidal neurons in slices of mouse somatosensory cortex with correlated light microscopy and high-resolution EM of all putative synaptic contacts between the neurons. We discovered a linear relationship between synapse size (postsynaptic density area) and synapse strength (excitatory postsynaptic potential amplitude), which provides an experimental foundation for assigning the actual physiological weights to synaptic connections seen in the EM. Furthermore, quantal analysis revealed that the number of vesicle release sites exceeded the number of anatomical synapses formed by a connection by a factor of at least 2.6, which challenges the current understanding of synaptic release in neocortex and suggests that neocortical synapses operate with multivesicular release, like hippocampal synapses7–11. Thus, neocortical synapses are more complex computational devices and may modulate their strength more flexibly than previously thought, with the corollary that the canonical neocortical microcircuitry possesses significantly higher computational power than estimated by current models., bioRxiv

Pyramidal cells in the superficial layers of the neocortex provide a major excitatory projection to layer 5, which contains the pyramidal cells that project to subcortical motor-related targets. Both structurally and functionally rather little is known about this interlaminar pathway, especially in higher mammals. Here, we made sparse ultrastructural reconstructions of the projection to layer 5 of three pyramidal neurons from layer 3 in cat V1 whose morphology, physiology, and synaptic connections with layers 2 and 3 were known. The dominant targets of the 74 identified synapses in layer 5 were the dendritic spines of pyramidal cells. The fractions of target spiny dendrites were 59, 61, and 84% for the three cells, with the remaining targets being dendrites of smooth neurons. These fractions were similar to the distribution of targets of unlabeled asymmetric synapses in the surrounding neuropil. Serial section reconstructions revealed that the target dendrites were heterogenous in morphology, indicating that different cell types are innervated. This new evidence indicates that the descending projection from the superficial layer pyramidal cells does not simply drive the output pyramidal cells that project to cortical and subcortical targets, but participates in the complex circuitry of the deep cortical layers., Brain Structure and Function, 223 (4)

The projections to individual cortical areas from extrinsic sources are a major determinant of the area’s function, but we lack comprehensive quantitative input maps even for primary sensory areas in most model species. To quantify all input sources to the mouse primary auditory cortex (Au1), we made localized injections of modified rabies virus (SADΔG-mCherry) into Au1 of five C57BL/6 mice and identified all the cortical and subcortical areas containing retrogradely labeled cells. Of all neurons projecting to Au1 from extrinsic areas, 27 % were located in the ipsilateral cortex, 14 % in the contralateral cortex, and 58 % in subcortical regions (almost exclusively ipsilateral, predominantly in the medial geniculate nucleus). Although 90 % of the labeled cells in the ipsilateral cortex were located within 1 mm of Au1, most cortical areas projected to Au1, including visual, somatosensory, motor, rhinal, cingulate and piriform cortices. The hierarchical relations of the cortical areas projecting to Au1 were determined based on the proportion of cell bodies in superficial versus deep layers. Feedback projections (from deep layers 5/6) dominated, but temporal association and auditory cortices were on the same hierarchical level, providing input from both superficial and deep layers. Au1 is embedded in a densely connected network that involves a high degree of cross-modal integration.

Recordings were made from pairs of neurons in cat striate visual cortex in vitro to study the AMPA-channel-mediated components of intracortical excitatory synaptic connections between layer 4 spiny neurons and between layer 6 and layer 4 spiny neurons. Forty-six of the 72 cells recorded were identified morphologically. They consisted of spiny stellate and pyramidal cells in layer 4, and pyramidal cells in layer 6. Connections between layer 4 excitatory cells involve excitatory postsynaptic potentials (EPSPs) averaging 949 microV, with an average coefficient of variation of 0.21 (n = 30). The synapses operate at very high release probabilities (0.69-0.98). With repetitive stimulation these EPSPs show varying degrees of depression, largely mediated by presynaptic changes in release probability. Four pairs of layer 4 cells were reciprocally connected. The connections from layer 6 to layer 4 involve smaller, more variable EPSPs, with an average amplitude of 214 microV, and average coefficient of variation 0.72 (n = 7). These synapses operate at moderately high release probabilities (0.37-0.56). They show facilitation with repetitive stimulation, mediated largely by presynaptic changes in release probability. One excitatory connection from a layer 4 neuron to a layer 6 pyramidal cell was also detected. Thus, layer 4 spiny neurons receive effective excitation from two intracortical sources that have different synaptic dynamics and are likely to contribute significantly to the temporal properties of these cells in vivo.

The traditional classification of primary motor cortex (M1) as an agranular area has been challenged recently when a functional layer 4 (L4) was reported in M1. L4 is the principal target for thalamic input in sensory areas, which raises the question of how thalamocortical synapses formed in M1 in the mouse compare with those in neighboring sensory cortex (S1). We identified thalamic boutons by their immunoreactivity for the vesicular glutamate transporter 2 (VGluT2) and performed unbiased disector counts from electron micrographs. We discovered that the thalamus contributed proportionately only half as many synapses to the local circuitry of L4 in M1 compared with S1. Furthermore, thalamic boutons in M1 targeted spiny dendrites exclusively, whereas ∼9% of synapses were formed with dendrites of smooth neurons in S1. VGluT2+boutons in M1 were smaller and formed fewer synapses per bouton on average (1.3 vs 2.1) than those in S1, but VGluT2+synapses in M1 were larger than in S1 (median postsynaptic density areas of 0.064 μm2vs 0.042 μm2). In M1 and S1, thalamic synapses formed only a small fraction (12.1% and 17.2%, respectively) of all of the asymmetric synapses in L4. The functional role of the thalamic input to L4 in M1 has largely been neglected, but our data suggest that, as in S1, the thalamic input is amplified by the recurrent excitatory connections of the L4 circuits. The lack of direct thalamic input to inhibitory neurons in M1 may indicate temporal differences in the inhibitory gating in L4 of M1 versus S1.SIGNIFICANCE STATEMENTClassical interpretations of the function of primary motor cortex (M1) emphasize its lack of the granular layer 4 (L4) typical of sensory cortices. However, we show here that, like sensory cortex (S1), mouse M1 also has the canonical circuit motif of a core thalamic input to the middle cortical layer and that thalamocortical synapses form a small fraction (M1: 12%; S1: 17%) of all asymmetric synapses in L4 of both areas. Amplification of thalamic input by recurrent local circuits is thus likely to be a significant mechanism in both areas. Unlike M1, where thalamocortical boutons typically form a single synapse, thalamocortical boutons in S1 usually formed multiple synapses, which means they can be identified with high probability in the electron microscope without specific labeling.

The present study is the first to describe quantitatively the patterns of synaptic connections made by the patchy network of pyramidal cell axons in the superficial layers of cat V1 in relation to the orientation map. Intrinsic signal imaging of the orientation map was combined with 3D morphological reconstructions of physiologically-characterized neurons at light and electron microscope levels. A Similarity Index (SI) expressed the similarity of the orientation domain of a given bouton cluster to that of its parent dendritic tree. Six pyramidal cells whose axons had a wide range of SIs were examined. Boutons were sampled from five local and five distal clusters, and from the linear segments that link the clusters. The synaptic targets were reconstructed by serial section electron microscopy. Of the 233 synapses examined, 182 synapses were formed with spiny neurons, the remainder with smooth neurons. The proportion of smooth neurons that were synaptic targets varied greatly (from 0 to 50%) between the cluster samples, but was not correlated with the SI. The postsynaptic density sizes were similar for synapses in local and distal clusters, regardless of their SI. This heterogeneity in the synaptic targets of single cells within the superficial layers is a network feature well-suited for context-dependent processing., Brain Structure and Function, 222 (7)

The humble epithet ‘Inventor of the tungsten microelectrode’ should be enough to secure David Hubel’s place in the neuroscience pantheon: his invention has been a ubiquitous tool for over half a century. One only needs to read a few key papers, however, to discover that not only was David Hubel that rarity in neuroscience — a wordsmith — but, in tandem with Torsten Wiesel, with whom he shared the Nobel Prize in 1981, he shaped an experimental and conceptual landscape we still traverse.

Inside one voxel of a cubic millimeter of neocortex, fifty to hundred thousand neurons use 4 km of axonal cable to form three to fifteen hundred million synapses with each other. While in the human, such voxel is a small fragment of a cortical area, in the mouse an entire cortical area, like the primary auditory cortex, can be contained in a voxel of this size. This raises the fundamental question of what happens inside such a voxel? Are the circuits contained in this voxel, and their operations, different in every area, or are there general principles that are conserved across cortical areas and species? Such questions go to the heart of understanding how the neocortex wires itself and works. One proposal is to answer these questions by mapping the entire circuit at synaptic resolution to produce a ‘connectome’ — of the cortical column, or even of the entire brain. However, such a high-resolution connectome is self-evidently unachievable with the tools available and as a strategy it still leaves us short of understanding the ‘principles of neural engineering’. We offer an alternative route that uses physiology and computational modeling as a means of generating ‘predictive anatomy’, where the questions about underlying structure are directed to fundamental principles of organization and operation of the cortical circuits. This approach involves ‘sparse’ rather than ‘dense’ reconstructions at light and electron microscope resolution to keep the questions well-matched to current experimental tools. Rather than providing a snap-shot of an entire wiring diagram, our strategy provides for a statistical description of the circuit and integrates theory, function, and structure in a common framework.

In common with other areas of the prefrontal cortex, activity in frontopolar area 10 is probably modulated by dopamine. We studied the dopaminergic innervation of monkey prefrontal area 10 by immunostaining with tyrosine hydroxylase (TH) antibodies. TH-positive axons in layer 3 were examined by electron microscopy of series of ultrathin sections. TH-positive boutons containing vesicles were sparse (2 × 10(-4) per μm(3)) and the majority (94%, n = 52) had no identifiable synaptic specialization, which supports the hypothesis that dopamine is released non-synaptically and raises the question of whether the local microenvironment surrounding the boutons is special. Compared with unlabelled boutons TH-positive boutons had a higher proportion of their perimeter in contact with dendritic shafts and were more often in continuous contact with pairs of pre- and postsynaptic structures. However, this may result from exclusion from sites preferred by glutamatergic and GABAergic synapses as the density of all synapses in the closer vicinity was no different from any randomly selected site in the neuropil. This quantitative ultrastructural study presents basic features of the dopaminergic innervation in prefrontal area 10 and provides a more detailed understanding of the structural basis of dopamine signalling in the cortex.

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP–spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.SIGNIFICANCE STATEMENTThe local field potential (LFP) is hypothesized to play a vital role in the efficient communication between neuronal populations, as well as in the efficient coding of information. Underlying these roles is the assumption that spikes can be strongly and reliably locked to certain phases of oscillations in the LFP. Gamma oscillations are thought to be the best candidate mechanism exerting the hypothesized roles of the LFP. They occur most reliably in response to specific artificial stimuli, but are usually very weak in response to natural movies or images. The current study finds that spikes exhibited weak phase locking when the power of gamma oscillations is weak and thus casts doubt on a general relevance of phase locking for neural communication and coding.

The frontal eye field (FEF) of the primate neocortex occupies a pivotal position in the matrix of inter-areal projections. In addition to its role in directing saccadic eye movements, it is the source of an attentional signal that modulates the activity of neurons in extrastriate and parietal cortex. Here, we tested the prediction that FEF preferentially excites inhibitory neurons in target areas during attentional modulation. Using the anterograde tracer biotinylated dextran amine, we found that the projections from FEF terminate in all cortical layers of area 46, lateral intraparietal area (LIP), and visual area V4. Axons in layer 1 spread extensively, those in layer 2/3 were most numerous, individual axons in layer 4 formed sprays of collaterals, and those of the deep layers were the finest caliber and irregular. All labeled synapses were the typical asymmetric morphology of excitatory synapses of pyramidal neurons. Dendritic spines were the most frequent synaptic target in all areas (95% in area 46, 89% in V4, 84% in LIP, 78% intrinsic local FEF). The remaining targets were one soma and dendritic shafts, most of which showed characteristics of inhibitory neurons with smooth dendrites (5% of all targets in area 46, 2% in V4, 9% in LIP, and 13% in FEF).

The claustrum is a subcortical structure reciprocally connected with most areas of neocortex. This strategic location suggests an integrative role of the claustrum across different sensory modalities. However, our knowledge of the synaptic relationship between the neocortex and the claustrum is basic. In this study, we address this question through a structural investigation of the claustral projection to the ipsilateral primary visual cortex of the cat. Light microscopic reconstructions of axons from the entire thickness of cortex showed a very sparse innervation of the entire cortical depth, with most synaptic boutons in layers 2/3 and 6. Axons bearing numerous boutonsterminauxand boutonsen passantbranched in these laminae. The sparse innervation did not seem to be compensated by particularly large synapses, given that the postsynaptic densities in the superficial layers are of comparable sizes (0.1 μm2) to other cortical synapses. All claustral synapses were asymmetric and in most cases targeted spines (87% in layer 4, 94% in layers 2/3 and 97% in layer 6). The pattern of innervation together with the known physiology of this projection suggests that the claustrum has a modulatory effect on visual cortex.

We simulated the shape and amplitude of extracellular action potentials (APs or “spikes”) using biophysical models based on detailed reconstructions of single neurons from the cat's visual cortex. We compared these predictions with spikes recorded from the cat's primary visual cortex under a standard protocol. The experimental data were derived from a large number of neurons throughout all layers. The majority of spikes were biphasic, with a dominant negative peak (mean amplitude, −0.11 mV), whereas a minority of APs had a dominant positive peak of +0.54-mV mean amplitude, with a maximum of +1.5 mV. The largest positive amplitude spikes were recorded in layer 5. The simulations demonstrated that a pyramidal neuron under known biophysical conditions may generate a negative peak with amplitude up to −1.5 mV, but that the amplitude of the positive peak may be at most 0.5 mV. We confirmed that spikes with large positive peaks were not produced by juxtacellular patch recordings. We conclude that there is a significant gap in our present understanding of either the spike-generation process in pyramidal neurons, the biophysics of extracellular recording, or both.

Pyramidal cells of layer 6 in cat visual cortex are the source of the corticothalamic projection, and their recurrent collaterals provide substantially more excitatory synapses in layer 4 than does the thalamic input. They have predominantly simple receptive fields and can be driven monosynaptically by electrically stimulating thalamic relay cells. Layer 6 cells could thus provide a significant disynaptic amplification of the thalamic input to layer 4, particularly since their synapses facilitate, unlike the thalamic afferents whose synapses depress. However, purely geometric considerations of the relation of their dendritic trees to the thalamic input indicate that they should form a far smaller number of synapses with thalamic afferents than do the simple cells of layer 4. We thus analyzed quantitatively the thalamic input to identified corticothalamic cells by labeling the thalamic afferents and corticothalamic cellsin vivo. We made a correlated light and electron microscopic study of 73 “contacts” between thalamic afferents and five corticothalamic cells. The electron microscope revealed that only 24 of the contacts identified at light microscope level were indeed synapses and, contrary to geometric predictions, virtually all were located on spines on the basal dendrites. Our quantitative estimates indicate that the corticothalamic cells form even fewer synapses with the thalamic afferents than predicted by geometric considerations and only 1/10 as many as do the layer 4 simple cells. These data strongly suggest it is the collective computation of cortical neurons, not the monosynaptic thalamic input, that determines the output of the corticothalamic cells.

The connection between the dorsal lateral geniculate nucleus (dLGN) and area 17 of the cat is a classical model for studying thalamocortical relations. We investigated the proportion of asymmetric synapses in layer 4 of area 17 of cats formed by axons of the dLGN, because this is an important morphological parameter in understanding the impact of dLGN axons on their target neurons. Although the present consensus is that this proportion is small, the exact percentage remains in doubt. Most previous work estimated that the thalamus contributes less than 10% of excitatory synapses in layer 4, but one estimate was as high as 28%. Two issues contribute to these widely different estimates, one being the tracers used, the other being the use of biased stereological approaches. We have addressed both of these issues. Thalamic axons were labeled in vivo by injections of biotinylated dextran amine into the A lamina of the dLGN of anesthetized cats. After processing, the brain was cut serially and prepared for light and electron microscopy. The density of asymmetric synapses in the neuropil and the density of synapses formed by labeled dLGN boutons were measured by using an unbiased sampling method called the physical disector. Our counts indicate that, in the fixed cat brain, there are 5.9 × 108 ± 0.9 × 108 asymmetric synapses per cubic millimeter of layer 4 in area 17, and the dLGN input provides only 6% of all asymmetric synapses in layer 4. The vast majority of synapses of layer 4 probably originate from other neurons in area 17. J. Comp. Neurol. 516:264-276, 2009. © 2009 Wiley-Liss, Inc.

The neocortex is a major component of the most sophisticated and economically significant computer in existence, nevertheless the organisation and operation of its computational circuit is not yet understood. Here we make some steps toward relating anatomical structure to computational function. We use methods of quantitative neuroanatomy to estimate the cortical circuit by defining the projection matrix between the various cells types of the neocortex of the cat, and then we consider the implications of this connectivity for cortical signal processing. Our analyses show that for a reasonable choice of the ratio between excitatory and inhibitory efficacy, the overall cortical circuit lies near the border of dynamical stability. We discuss a model of co-operative competitive processing that is consistent with the observed connectivity in the superficial layers of the cortex, and consider also how the topology of the overall cortical circuit could be configured dynamically through average inhibition.

The primary visual cortex (V1) and V2 together form approximately 24% of the total neocortex of the macaque monkey and have each other as their major partners. The major target of the V1 projection to V2 is layer 4, where it forms clusters of boutons, which form asymmetric (excitatory) synapses mainly with dendritic spines (75%). The remainder form synapses with dendritic shafts. The synapses found on spines were often more complex, perforated postsynaptic densities than those found on dendritic shafts. The reciprocal projection from V2 to V1 targeted layers 1, 2/3, and 5 and was formed of axons of different morphologies. One axon type, originating from superficial layer pyramidal cells, had a morphology resembling those of local pyramidal cell collaterals. These axons arborized in layers 1, 2/3, and 5 of V1. Another type of axon, arborizing in layer 1, was slender (0.3 microm), unbranched, unmyelinated, and uniformly covered with boutons terminaux and formed asymmetric synapses mainly with slender spines. Yet a third type of axon also confined to layer 1, was thick (>1 microm), branched, heavily myelinated, and formed separate small clusters of large ( approximately 1 microm) en passant multisynaptic boutons that formed asymmetric synapses mainly with large flat spines. These data show the existence of a reciprocal excitatory loop between V1 and V2 that is formed by different axonal types, each with preferred layers of termination.

Excitatory synapses arising from local neurons in the cat visual cortex are much more numerous than the thalamocortical synapses, which provide the primary sensory input. Many of these local circuit synapses are involved in the connections between cortical layers, but lateral connections within layers provide a major component of the local circuit synapses. We tested the influence of these lateral connections in the primary visual cortex of cats by inactivating small patches of cortex about 450 microm lateral from the recording pipette. By use of the neurotransmitter gamma-aminobutyric acid (GABA), small patches of cortex were inhibited and released from inhibition in seconds. Orientation tuning curves derived from responses to oriented drifting gratings were obtained during short control periods interleaved with periods of GABA inactivation. About 30% of the cells (18/62, recorded in all layers) changed their orientation tuning when a small portion of their lateral input was silenced. There was no broadening of the orientation tuning curve during lateral inactivation. Instead, the recorded cells shifted their preferred orientation towards the orientation of the inactivated site. One explanation is that the GABA inactivation alters the balance of excitatory and inhibitory inputs to a cell, which results in a shift of the cell's preferred orientation.

It is worth noting that while early anatomists like Ramon y Cajal, and later Lorente de No, described many of the cell types of the cerebral cortex, and they knew of Sherrington's evidence for active inhibition, they made no attempt to differentiate inhibitory from excitatory cells. That step only came much later when the fundamental details of the underlying biophysics and ionic basis of inhibitory synapses were worked out by Eccles and his colleagues and their functional data were correlated with the morphology and ultrastructure of the synapses of the neurons.For Sherrington, excitatory and inhibitory neurons were always hand-in-glove and together they provided the algebra of the nervous system: “The net change which results there when the two areas are stimulated concurrently is an algebraic sum of the plus and minus effects producible separately by stimulating singly the two antagonistic nerves” [18xOn the reciprocal innervation of antagonistic muscles. Thirteenth note. On the antagonism between reflex inhibition and reflex excitation. Sherrington, C. Proc. R. Soc. Lond. B. 1908; 80: 565–578CrossrefSee all References[18]. In recurrent circuits, there is an inherent balance between excitation and inhibition and because these recurrent excitatory circuits offer the means of amplification, small changes in the timing or strength of inhibition can effectively change the response of the entire network.We have emphasised the importance of the spike threshold, because neurons that are below threshold no longer contribute to the network activity. By keeping the membrane potential below threshold, inhibitory neurons can control dynamically the configuration of the circuit. While past research has focused almost exclusively on the output of the inhibitory neurons, one crucial aspect of future circuit analysis is to determine the source of the inputs to the inhibitory neurons and to then combine this with knowledge of the dynamics of spiking patterns and synaptic plasticity.

Synapses can only be morphologically identified by electron microscopy and this is often a very labor-intensive and time-consuming task. When quantitative estimates are required for pathways that contribute a small proportion of synapses to the neuropil, the problems of accurate sampling are particularly severe and the total time required may become prohibitive. Here we present a sampling method devised to count the percentage of rarely occurring synapses in the neuropil using a large sample (approximately 1000 sampling sites), with the strong constraint of doing it in reasonable time. The strategy, which uses the unbiased physical disector technique, resembles that used in particle physics to detect rare events. We validated our method in the primary visual cortex of the cat, where we used biotinylated dextran amine to label thalamic afferents and measured the density of their synapses using the physical disector method. Our results show that we could obtain accurate counts of the labeled synapses, even when they represented only 0.2% of all the synapses in the neuropil.

In all species examined, with the exception of rodents, the axons of neocortical neurons form boutons in multiple separate clusters. Most descriptions of clusters are anecdotal, so here we developed an objective method for identifying clusters. We applied a mean-shift cluster-algorithm to three-dimensional reconstructions of 39 individual neurons and three thalamic afferents from the cat primary visual cortex. Both spiny (20 of 26) and smooth (7 of 13) neurons formed at least two distinct ellipsoidal clusters (range, 2–7). For all cell types, cluster formation is heterogenous, but is regulated so that cluster size and the number of boutons allocated to a cluster equalize with increasing number of clusters formed by a neuron. The bouton density within a cluster is inversely related to the spatial scale of the axon, resulting in a four times greater density for smooth neurons than for spiny neurons. Thus, the inhibitory action of the smooth neurons is much more concentrated and focal than the excitatory action of spiny neurons. The cluster with the highest number of boutons (primary cluster) was typically located around or above the soma of the parent neuron. The distance to the next cluster was proportional to the diameter of the primary cluster, suggesting that there is an optimal distance and spatial focus of the lateral influence of a neuron. The lateral spread of clustered axons may thus support a spoke-like network architecture that routes signals to localized sites, thereby reducing signal correlation and redundancy.

The relationship between structure and function in the brain has an interesting counterpart in the scientific relationship of Santiago Ramon y Cajal and Charles Sherrington. In their search for the principles of organization of the nervous system, both men met at the synapse. For Sherrington, who coined the word 'synapse', the neuron was the functional unit that integrated excitatory and inhibitory input. For Cajal, the synapse was the explanation for how neurons could be individual elements, yet connected to form circuits. Both men were primarily concerned with local circuits in spinal cord and brain, but imaginatively extrapolated their discoveries on simple circuits to higher cognitive functions. Both men wrote poetically about their discoveries and so provided neuroscience with a rich vocabulary, vivid and memorable images, and concepts that remain part of the currency of 21st century neuroscience.

Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. The model also includes synaptic properties of these inputs. The cortical input served as sources of background activity, and visual stimuli was modeled as sinusoidal grating. For all contrasts, strong synaptic depression of the dLGN feedforward afferents compresses the firing rates in response to orthogonal stimuli, keeping these rates at practically the same low level. However, at preferred orientations, despite synaptic depression, firing rate changes as a function of contrast. Thus, when embedded in an active network, strong synaptic depression can explain contrast-invariant orientation tuning of simple cells. This is true also when the dLGN inputs are partially depressed as a result of their spontaneous activity and to some extent also when parameters were fitted to a more moderate level of synaptic depression. The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated.

The primate’s large brain-to-body weight ratio and high complexity are unusual in the animal kingdom. There is compelling evidence that it is an evolutionary adaptation that allows its owner to live a long life because of its competence in solving a wide range of problems. How primates use their brain to achieve such competence is of course of central interest to us. Here we review some key aspects of the neocortex that can be explored in nonhuman primates. Studies of the cortical circuits in the visual cortex reveal that the two major types of pathways, called feedforward and feedback, involve a very small fraction of the total synapses that any area contains. Nevertheless these pathways may be critical for some important forms of cortical plasticity, like perceptual learning and tasks involving perception and action.

Sensory neurons encode stimulus intensity in their instantaneous spike rate and adjust the set-points of the stimulus–response relationships by adaptation. In the visual cortex, adaptation is crucial because the mechanism of fast gain control (normalization) increases the contrast sensitivity of individual neurons at the cost of encoding a far narrower range of contrasts than is encountered in natural scenes. The mechanism of adaptation, however, is a slow process and has a time constant of seconds. Here we use two-photon calcium imaging of identified excitatory and inhibitory neurons in superficial layers of cat primary visual cortex to answer two questions: for a given set-point, what is range of contrasts represented within a local pool of neurons, and what accounts for the slow time constant of contrast adaptation? We found that a local patch of excitatory neurons has a large diversity of contrast tunings, which effectively extends the range of contrast that can be encoded instantaneously in cortex. Additionally, we identified a pool of parvalbumin-positive GABAergic neurons and neurons in the upper tier of imaging sites that showed a paradoxical slow increase in activity during adaptation, thus implicating them in the slow set-point adaptation of the excitatory population. Our results provide new insights into the circuits and mechanisms underlying cortical adaptation and gain control. SIGNIFICANCE STATEMENT Neurons in the primary visual cortex (V1) respond near instantaneously over a limited range of contrasts but can also shift their operating range according to the average contrast of the scene. This “contrast adaptation” takes 5–10 s and ensures that a full range of contrasts can be encoded in V1, while remaining sensitive to small changes in local contrast. By optically recording many layer 2 neurons simultaneously, we discovered that networks of neurons collectively code for a much wider range of contrasts. Whereas most neurons responded to sustained increases in contrast by decreasing their spike firing rates, two types of inhibitory neurons in the cat9s visual cortex paradoxically increased their firing rates and so could inhibit other neurons to produce contrast adaptation.

We show that when temporal summation takes place, depression of postsynaptic responses may ensue when the underlying synaptic conductance change is constant or even facilitatory. We term this phenomenon “apparent depression.” Such apparent depression is most notable for slow synaptic conductance changes, for high frequency, and when the synapse is located at distal dendritic sites. We show that, when temporal summation ensues, the erroneous estimation of short-term synaptic plasticity arises partially from the conventional measurement of synaptic dynamics at postsynaptic potential peak time. This can be corrected when measuring overlapping synaptic responses at fixed intervals after stimulus time. Somatic voltage clamp also helps to partially correct for the apparent depression, but a good model of the neuron can do even better in providing a more accurate view of the underlying synaptic conductances.

Summary The recent Cell Symposium ‘Genes, Circuits and Behavior' brought together researchers working on neural circuits in vertebrate and invertebrate species. In the interest of fostering communication across the ‘backbone-divide', we asked a number of neuroscientists from both camps for their views on the extent to which insights obtained from studies on neural circuits in one type of species are transferable to the other.

The issue of the relation of eye position to perceptual reversals of the ambiguous figure of the ‘Necker cube’ dates back to Necker's original article [L.A. Necker (1832) The London & Edinburgh Philosophical Magazine and Journal of Science, 1, 329–337]. Despite the investigations of many distinguished psychophysicists since then, the question of whether perceptual switching is a cause or a consequence of associated changes in eye position has remained a matter of debate. In the present study we overcame methodological problems that have bedevilled many previous studies. We avoided any instruction that could interfere with the human subjects' free viewing of the Necker cube, tracked the eye position precisely and used biased versions of the cube that produced unambiguous percepts to determine how each subject actually looked at the cube. We show that, under these free-viewing conditions, there is a close link between the perception of the Necker cube and eye position. The average eye position of most subjects is at an extreme value at about the time when the subject's perception switches. From the biased cube trials we can infer that the polarity of the extreme corresponds to the percept which the subject had before the switch. These data indicate a bidirectional coupling between eye position and perceptual switching so that, after a subject's perceptual state changes, their eye position shifts to view the newly established percept. When the eye position approaches the corresponding extreme, the percept, in turn, becomes more and more likely to switch. This result suggests that the changed eye position itself might provide a negative feedback signal that suppresses the percept.

Neurons in the dorsal lateral geniculate nucleus (dLGN) of the anaesthetized cat were activated with test stimuli (flashing spots, counterphased gratings and moving bars) in the presence of a moving background texture. Moving texture alone produced mild excitation, as a result of stimulation of the receptive field centre. Fast moving coarse textures were more effective than fine slow moving textures. The predominant effect of texture motion, however, was to reduce the response to all test stimuli displayed in the receptive field centre. The effects were similar for X- and Y-like cells. In the case of flashed spots, the sustained response was more strongly suppressed than the transient response. The direction of motion of the texture and differences in the relative motion of bar and texture had no influence on the degree of suppression. These observations are similar to effects seen on cat retinal ganglion cells, and are probably a form of gain control. Such suppressive effects are transmitted to the cortex and are likely to be evoked by large gratings, textures and by natural stimuli, all of which activate extensive regions of the receptive field surround.

1Instituto Cajal (CSIC), Madrid, Spain; 2Department of Physiology and Pharmacology, Vision, Touch and Hearing Research Centre, School of Biomedical Sciences, The University of Queensland, Brisbane, Australia; 3Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan; 4Neuropsychiatric Institute and Brain Research Institute, UCLA School of Medicine Los Angeles, California, USA; 5Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA; 6Fishberg Research Center for Neurobiology, Kastor Neurobiology of Aging Laboratories, and Advanced Imaging Program, Mount Sinai School of Medicine, New York, USA; 7Division of Cerebral Circuitry, National Institute for Physiological Sciences, Okazaki, Japan; 8Institute of Neuroinformatics, University Zurich and ETH Zurich, Switzerland; 9Laboratory for Cortical Organization and Systematics, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan; 10Department of Pharmacology, The School of Pharmacy, London University, UK; 11Department of Morphology, Faculty of Health Sciences and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer Sheva, Israel; 12Department of Biological Sciences, Columbia University, New York, USA

To understand the rules by which axons lay down their synaptic boutons we analyzed the linear bouton distributions in 39 neurons (23 spiny, 13 smooth) and 3 thalamic axons, which were filled intracellularly with horseradish peroxidase (HRP) during in vivo experiments in cat area 17. The variation of the total number of boutons and the total axonal length was large (789–7912 boutons, 12–126 mm). The overall linear bouton density for smooth cells was higher than that of spiny cells and thalamic afferents (mean ± sd, 110 ± 21 and 78 ± 27 boutons per mm of axonal length). The distribution of boutons varied according to their location on the tree. Distal axon collaterals (first and second order segments in Horton-Strahler ordering) of smooth neurons had a 3.5 times higher, spiny cells and thalamic afferents a 2 times higher bouton density compared to the higher order (more proximal) segments. The distribution of interbouton intervals was positively skewed and similar for cells of the same type. In most cases a γ-distribution fitted well, but the distributions had a tendency to have a heavier tail. To a first approximation these bouton distributions are consistent with both diffuse and specific models of interneuronal connections. Quite simple rules can explain these distributions and the connections between the different classes of neurons.

SummaryThe mouse neocortex is now the focus of research using twenty-first century techniques of circuit analyses, which are revealing different wiring strategies for excitatory and inhibitory connections and providing important insights into the possible computations of cortical circuits.

The axons of pyramidal neurons in the superficial layers of the neocortex of higher mammals form lateral networks of discrete clusters of synaptic boutons. In primary visual cortex the clusters are reported to link domains that share the same orientation preferences, but how individual neurons contribute to this network is unknown. Here we performed optical imaging to record the intrinsic signal, which is an indirect measure of neuronal firing, and determined the global map of orientation preferences in the cat primary visual system. In the same experiment, single cells were recorded and labelled intracellularly. We found that individual axons arborise within the retinotopic representation of the classical receptive field, but their bouton clusters were not aligned along their preferred axis of orientation along the retinotopic map. Axon clusters formed in a variety of different orientation domains, not just the like-orientation domains. This topography and heterogeneity of single-cell connectivity provides circuits for normalization and context-dependent feature processing of visual scenes., Nature Communications, 5, ISSN:2041-1723

The extrastriate visual area of the macaque monkey called MT or V5, receives its input from multiple sources. We have previously examined the synaptic connections made by V1 cells that project to MT (Anderson et al., 1998). Here, we provide a similar analysis of the projection from V2 to MT. The major target of the V2 projection in MT is layer 4, where it forms clusters of asymmetric (excitatory) synapses. Unlike the V1 projection, it also forms synapses in layers 1 and 2 and does not form synapses in layer 6. The most frequently encountered targets of boutons labeled from V2 were spines (67% in layer 4; 82% in layer 2/3). Unusually, only 5/12 boutons examined in layer 1 actually formed synapses. Unlike the V1 projection, multisynaptic boutons were rare (mean, 1.1 synapses per bouton vs. 1.7 for the V1 projection). Like the V1 projection, the input to MT from any point in V2 is sparse (contributing approximately 4-6% of the asymmetric synapses in the densest clusters in layer 4). The synapses of the V2 projection were similar in size to those of the V1 projection (0.1 microm(2) vs. 0.09 microm(2)) and both formed more complex postsynaptic densities on spines than on dendritic shafts. The clear differences between the V1 and V2 projection to MT indicate that their functions are complementary rather than completely overlapping.

Kevan AC Martin is a Director of the Institute of Neuroinformatics, a joint Institute of the University of Zurich and the Swiss Federal Institute of Technology (ETH). With Rodney Douglas and David Whitteridge he proposed and elaborated the ‘canonical circuit’ for neocortex. This circuit has been a conceptual fulcrum and leitmotiv for a variety of experimental and theoretical investigations across species, including monkey and man, and in neuromorphic silicon circuits.

The goal of this present study was to derive a new estimate of the synaptic contribution of the dorsal lateral geniculate nucleus (dLGN) to the subdivisions of its main recipient layer, layer 4C, of striate cortex of macaque monkey. The projection from the dLGN and its terminal boutons within layer 4C were visualized by immunodetection of the calcium binding protein, parvalbumin (PV), which is expressed in relay cells of the dLGN. The proportion of asymmetric synapses formed by PV-positive boutons within the alpha and beta sublayers of 4C was estimated by using a nonbiased stereological counting method. The proportion of asymmetric synapses contributed by the PV-positive boutons to layer 4Calpha is 8.7%; to 4Cbeta is 6.9%. Assuming all the PV-positive asymmetric synapses derive from the dLGN relay cells, this gives a ratio of dLGN synapses per neuron of 192 in layer 4Calpha and 128 in layer 4Cbeta. Thus, the recurrent excitatory input from neighboring cortical neurons must play an important part in responses of the neurons lying at the input stage of the cortical circuit.

A simple model was proposed to account for the direction selectivity of neurons in the primary visual cortex, area V1. In this model, the temporal asymmetries in the summation of inhibition and excitation that produce directionality were generated by structural asymmetries in the tangential organization of the basal dendritic tree of cortical neurons. We reconstructed dendritic trees of neurons with known direction preferences and found no correlation between the small biases of a neuron's dendritic morphology and its direction preference. Detailed simulations indicated that even when the electrotonic asymmetries in the dendrites were extreme, as in cortical Meynert cells, the biophysical properties of single neurons could contribute only partially to the directionality of cortical neurons.

1. Dual intracellular recording was used to examine the interactions between neighbouring spiny (excitatory) and smooth (inhibitory) neurones in layer 4 of cat visual cortex in vitro. Synaptic connections were found in seventeen excitatory-inhibitory neurone pairs, along with one inhibitory-inhibitory connection. 2. Fast excitatory inputs onto smooth neurones (basket cells) from spiny cells (spiny stellate or pyramidal cells) (n = 6) produce large excitatory postsynaptic potentials (EPSPs) of up to 4 mV mean amplitude, whereas basket cells evoke slower inhibitory postsynaptic potentials (IPSPs) in their postsynaptic targets (n = 17), of smaller amplitude (up to 1.6 mV at membrane potentials of -60 mV). 3. Both types of PSP appear to be multiquantal, and both may exhibit depression of up to 60 % during short trains of presynaptic spikes. This depression can involve presynaptic and/or postsynaptic factors. 4. One-third (n = 5) of the spiny cell-smooth cell pairs tested were reciprocally connected, and in the one pair for which the suprathreshold interactions were comprehensively investigated, the pattern of basket cell firing was strongly influenced by the activity in the connected excitatory neurone. The basket cell was only effective in inhibiting spiny cell firing when the excitatory neurone was weakly driven.

Extracellular recordings indicate that mechanisms that control contrast gain of neuronal discharge are found in the retina, thalamus and cortex. In addition, the cortex is able to adapt its contrast response function to match the average local contrast. Here we examine the neuronal mechanism of contrast adaptation by direct intracellular recordings in vivo. Both simple (n = 3) and complex cells (n = 4) show contrast adaptation during intracellular recording. For simple cells, that the amplitude of fluctuations in membrane potential induced by a drifting grating stimulus follows a contrast response relation similar to lateral geniculate relay cells, and does not reflect the high gain and adaptive properties seen in the action potential discharge of the neurons. We found no evidence of significant shunting inhibition that could explain these results. In complex cells there was no change in the mean membrane potential for different contrast stimuli or different states of adaptation, despite marked changes in discharge rate. We use a simplified electronic model to discuss the central features of our results and to explain the disparity between the contrast response functions of the membrane potential and action potential discharge in simple cells.

The pattern of excitatory and inhibitory inputs to the inhibitory neurons is largely unknown. We have set out to quantify the major excitatory and inhibitory inputs to layer 4 basket cells from the primary visual cortex of the cat. The synapses formed with the soma, and proximal and distal dendrites, were examined at the light and electron microscopic levels in four basket cells, recorded in vivo and filled with horseradish peroxidase. The major afferents of layer 4 have been well characterised, both at the light and electron microscopic levels. The sizes of the synaptic boutons of the major excitatory inputs to layer 4 from the thalamic relay cells, spiny stellate cells, and layer 6 pyramidal neurons are statistically different. Their distributions were compared to those of the boutons forming asymmetric contacts onto the basket cells, which were assumed to be provided by the same set of excitatory afferents. The best-fit results showed that about equal numbers of synapses were provided by the layer 6 pyramids (43%) and the spiny stellates (44%), whereas the thalamic afferents contributed only 13%. A similar analysis on the symmetric synaptic input to the basket cells indicated that as much as 79% of the symmetric synapses could have originated from other layer 4 basket cells. Thalamic and spiny stellate synapses were preferentially located on the soma and proximal dendrites, regions that also had 76% of all the symmetric contacts. J. Comp. Neurol. 380: 230–242, 1997. © 1997 Wiley-Liss, Inc.

Light and electron microscopy of the primary visual cortex of mice indicates that pyramidal neurons connect preferentially to inhibitory neurons., One of the hallmarks of neocortical circuits is the predominance of recurrent excitation between pyramidal neurons, which is balanced by recurrent inhibition from smooth GABAergic neurons. It has been previously described that in layer 2/3 of primary visual cortex (V1) of cat and monkey, pyramidal cells filled with horseradish peroxidase connect approximately in proportion to the spiny (excitatory, 95% and 81%, respectively) and smooth (GABAergic, 5% and 19%, respectively) dendrites found in the neuropil. By contrast, a recent ultrastructural study of V1 in a single mouse found that smooth neurons formed 51% of the targets of the superficial layer pyramidal cells. This suggests that either the neuropil of this particular mouse V1 had a dramatically different composition to that of V1 in cat and monkey, or that smooth neurons were specifically targeted by the pyramidal cells in that mouse. We tested these hypotheses by examining similar cells filled with biocytin in a sample of five mice. We found that the average composition of the neuropil in V1 of these mice was similar to that described for cat and monkey V1, but that the superficial layer pyramidal cells do form proportionately more synapses with smooth dendrites than the equivalent neurons in cat or monkey. These distributions may underlie the distinct differences in functional architecture of V1 between rodent and higher mammals., Author Summary The mammalian visual cortex, which is part of the cerebral cortex, contains 50 to 100 thousands of neurons per cubic millimetre, most of which are excitatory (85%) and the minority, inhibitory (15%). Unlike neurons in the retina, neurons in the visual cortex are preferentially activated by lines or edges of a particular orientation. This is termed a neuron's “orientation preference.” In the visual cortex of higher mammals like cats and monkeys, neurons that share an orientation preference are clustered in functional columns. However, in rodents like mice, orientation preferences are randomly distributed. In this study, we investigate whether the differences between columnar and non-columnar cortex is correlated with differences in the connectivity patterns between excitatory and inhibitory neurons. Using light and electron microscopy, we mapped the connectivity of pyramidal neurons—the primary excitatory neurons—in the superficial layers of the primary visual cortex (V1) of mice. Our results show that the ratio of excitatory-inhibitory neurons in mouse V1 is similar to that of cat or monkey V1, but in mouse V1 local pyramidal neurons target proportionately many more inhibitory neurons compared to what other studies found in cat or monkey. This difference may indicate the significance of inhibition in maintaining orientation selectivity in the non-columnar visual cortex of rodents like mice and is a distinct difference in the architecture of V1 between mice and higher mammals.

Neurons in primary visual cortex of many mammals are clustered according to their preference to stimulus parameters such as orientation and spatial frequency. Nevertheless, responses to complex visual stimuli are highly heterogeneous between adjacent neurons. To investigate the relation between these observations, we recorded from pairs of neighboring neurons in area 17 of anesthetized cats in response to stimuli of differing complexity: sinusoidal drifting gratings, binary dense noise, and natural movies. Comparisons of the tuning curves revealed similar orientation and direction preferences for neighboring neurons, but large differences in preferred phase, direction selectivity, and tuning width of spatial frequency. No pair was similar across all tuning properties. The neurons' firing rates averaged across multiple stimulus repetitions (the “signal”) were also compared. Binned between 10 and 200 ms, the correlation between these signals was close to zero in the median across all pairs for all stimulus classes. Signal correlations agreed poorly with differences in tuning properties, except for receptive field offset and relative modulation (i.e., the strength of phase modulation). Nonetheless, signal correlations for different stimulus classes were well correlated with each other, even for gratings and movies. Conversely, trial-to-trial fluctuations (termed “noise”) were poorly correlated between neighboring neurons, suggesting low degrees of common input. In response to gratings and visual noise, signal and noise correlations were well correlated with each other, but less so for responses to movies. These findings have relevance for our understanding of the processing of natural stimuli in a functionally heterogeneous cortical network.

In layer 4 of cat visual cortex, the monocular, concentric receptive fields of thalamic neurons, which relay retinal input to the cortex, are transformed into 'simple' cortical receptive fields that are binocular and selective for the precise orientation, direction of motion, and size of the visual stimulus. These properties are thought to arise from the pattern of connections from thalamic neurons, although anatomical studies show that most excitatory inputs to layer 4 simple cells are from recurrently connected circuits of cortical neurons. We examined single fibre inputs to spiny stellate neurons. We examined single fibre inputs to spiny stellate neurons in slices of cat visual cortex, and conclude that thalamocortical synapses are powerful and the responses they evoke are unusually invariant for central synapses. However, the responses to intracortical inputs, although less invariant, are strong enough to provide most of the excitation to simple cells in vivo. Our results suggest that the recurrent excitatory circuits of cortex may amplify the initial feedforward thalamic signal, subserving dynamic modifications of the functional properties of cortical neurons.

The synapse, first introduced as a physiological hypothesis by C. S. Sherrington at the close of the nineteenth century, has, 100 years on, become the nexus for anatomical and functional investigations of interneuronal communication. A number of hypotheses have been proposed that give local synaptic interactions specific roles in generating an algebra or logic for computations in the neocortex. Experimental work, however, has provided little support for such schemes. Instead, both structural and functional studies indicate that characteristically cortical functions, e. g., the identification of the motion or orientation of objects, involve computations that must be achieved with high accuracy through the collective action of hundreds or thousands of neurons connected in recurrent microcircuits. Some important principles that emerge from this collective action can effectively be captured by simple electronic models. More detailed models explain the nature of the complex computations performed by the cortical circuits and how the computations remain so remarkably robust in the face of a number of sources of noise, including variability in the anatomical connections, large variance in the synaptic responses and in the tria-to-trial output of single neurons, and weak or degraded input signals.

Summary The effortless ability of vertebrates to explore and exploit their environment is strongly correlated with the evolution of the most anterior part of their nervous system, the forebrain, where data from autonomic (visceral), limbic (emotive), and internal and peripheral sensors of the external world are combined to develop, decide, and deploy advantageous behaviors. The correlation of behavioral performance with forebrain expansion suggests that evolution has discovered the developmental means of building vertebrate brains to produce a scalable, special-purpose architecture for efficiently processing and expressing behavior. In mammals, the exuberant expansion of this forebrain is dominated by the growth of their cortex — the two-dimensional sheet that is the major source of their intelligent behavior, especially for primates.