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Activation of a neuronal pathway is often associated with inhibition of surrounding pathways. How locally coordinated synaptic plasticity occurs in vivo is not known, nor is its role in shaping neuronal responses. El-Boustani et al. paired optogenetic stimulation of single neurons with a visual input and were able to shift the neuron's receptive field toward the target location. Spines that expressed structural long-term potentiation had receptive fields overlapping the target stimulus but were surrounded by spines that expressed receptive fields away from the target. Science , this issue p. 1349 Arc-mediated local synaptic plasticity reorganizes responses on dendrites to mediate functional neuronal plasticity in vivo. Plasticity of cortical responses in vivo involves activity-dependent changes at synapses, but the manner in which different forms of synaptic plasticity act together to create functional changes in neurons remains unknown. We found that spike timing–induced receptive field plasticity of visual cortex neurons in mice is anchored by increases in the synaptic strength of identified spines. This is accompanied by a decrease in the strength of adjacent spines on a slower time scale. The locally coordinated potentiation and depression of spines involves prominent AMPA receptor redistribution via targeted expression of the immediate early gene product Arc. Hebbian strengthening of activated synapses and heterosynaptic weakening of adjacent synapses thus cooperatively orchestrate cell-wide plasticity of functional neuronal responses.

Subdivisions of mouse whisker somatosensory thalamus project to cortex in a region-specific and layer-specific manner. However, a clear anatomical dissection of these pathways and their functional properties during whisker sensation is lacking. Here, we use anterograde trans-synaptic viral vectors to identify three specific thalamic subpopulations based on their connectivity with brainstem. The principal trigeminal nucleus innervates ventral posterior medial thalamus, which conveys whisker-selective tactile information to layer 4 primary somatosensory cortex that is highly sensitive to self-initiated movements. The spinal trigeminal nucleus innervates a rostral part of the posterior medial (POm) thalamus, signaling whisker-selective sensory information, as well as decision-related information during a goal-directed behavior, to layer 4 secondary somatosensory cortex. A caudal part of the POm, which apparently does not receive brainstem input, innervates layer 1 and 5A, responding with little whisker selectivity, but showing decision-related modulation. Our results suggest the existence of complementary segregated information streams to somatosensory cortices. The thalamus provides sensory input to the cortex, but many aspects of thalamocortical signaling remain unknown. Here, the authors reveal parallel non-overlapping thalamic pathways with distinct representations of tactile and decision-related information during a goal-directed sensorimotor task.

Adapting goal-directed behaviors to changing sensory conditions is a fundamental aspect of intelligence. The brain uses abstract representations of the environment to generalize learned associations across sensory modalities. The circuit organization that mediates such cross-modal generalizations remains, however, unknown. Here, we demonstrate that mice can bidirectionally generalize sensorimotor task rules between touch and vision by using abstract representations of peri-personal space within the cortex. Using large-scale mapping in the dorsal cortex at single-cell resolution, we discovered multimodal neurons with congruent spatial representations within multiple associative areas of the dorsal and ventral streams. Optogenetic sensory substitution and systematic silencing of these associative areas revealed that a single area in the dorsal stream is necessary and sufficient for cross-modal generalization. Our results identify and comprehensively describe a cortical circuit organization that underlies an essential cognitive function, providing a structural and functional basis for abstract reasoning in the mammalian brain. Neural mechanisms underlying cross-modal generalization of learned sensorimotor associations are not fully understood. Here authors show that mice use a specialized cortical circuit to generalize learned behaviors between vision and touch. A single region in the dorsal cortex is essential for forming abstract spatial representations that enable cross-modal flexibility.

Here the authors investigate the neural basis of coherence and contrast detection in the somatosensory system. Model-based analysis of the responses of neurons in the barrel cortex reveal different coding schemes according to the level of correlation in the spatiotemporal patterns of whisker stimulation. The cell populations they find in the primary somatosensory cortex are analogous to cell classes previously reported in two separate cortical areas of the visual system. As in other sensory modalities, one function of the somatosensory system is to detect coherence and contrast in the environment. To investigate the neural bases of these computations, we applied different spatiotemporal patterns of stimuli to rat whiskers while recording multiple neurons in the barrel cortex. Model-based analysis of the responses revealed different coding schemes according to the level of input correlation. With uncorrelated stimuli on 24 whiskers, we identified two distinct functional categories of neurons, analogous in the temporal domain to simple and complex cells of the primary visual cortex. With correlated stimuli, however, a complementary coding scheme emerged: two distinct cell populations, similar to reinforcing and antagonist neurons described in the higher visual area MT, responded specifically to correlations. We suggest that similar context-dependent coexisting coding strategies may be present in other sensory systems to adapt sensory integration to specific stimulus statistics.

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the “macroscopic” properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order “mean-field” statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the \"effective\" connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

In the visual cortex, inhibitory neurons alter the computations performed by target cells via combination of two fundamental operations, division and subtraction. The origins of these operations have been variously ascribed to differences in neuron classes, synapse location or receptor conductances. Here, by utilizing specific visual stimuli and single optogenetic probe pulses, we show that the function of parvalbumin-expressing and somatostatin-expressing neurons in mice in vivo is governed by the overlap of response timing between these neurons and their targets. In particular, somatostatin-expressing neurons respond at longer latencies to small visual stimuli compared with their target neurons and provide subtractive inhibition. With large visual stimuli, however, they respond at short latencies coincident with their target cells and switch to provide divisive inhibition. These results indicate that inhibition mediated by these neurons is a dynamic property of cortical circuits rather than an immutable property of neuronal classes. Inhibitory neurons in the visual cortex alter the computations of target cells by exerting division or subtraction effects, but what determines these different functions is not clear. Here the authors use visual stimuli and optogenetics to show that the effects mediated by somatostatin-expressing and parvalbumin-expressing neurons are driven by their response mode and timing.

replying to S.-H. Lee, A. C. Kwan & Y. Dan Nature508,http://dx.doi.org/10.1038/nature13128(2014) Several recent studies have examined the function of parvalbumin-expressing (PV + ) and somatostatin-expressing (SST + ) inhibitory neurons in V1 (refs 1 , 2 , 3 ). Although it is commonly agreed that these cell types alter the responses of pyramidal neurons in distinct ways—via divisive or subtractive inhibition—their specific roles remain a matter of debate. The Comment by Lee et al. 4 presents new data suggesting that the differences between the results of Lee et al . 2 compared to Atallah et al . 3 and Wilson et al . 1 could be explained by the strength and duration of laser stimulation used to optogenetically activate these two classes of inhibitory neuron. The data presented by Lee et al . 4 now clarify that PV + neurons, when probed with small amounts of optogenetic activation, do not significantly change the tuning of their target cells, confirming Atallah et al. 3 and Wilson et al . 1 . The new SST + results presented in the Comment 4 show that SST + neurons can subtract responses, consistent with Wilson et al. 1 , but we suggest that the switch of function of SST + neurons in their data between short (1 s) and long (4–5 s) stimulation reveals a core principle of inhibition in cortical networks rather than simply being a peculiarity of stimulation protocols. The fundamental difference between these two conditions resides in the temporal overlap between inhibitory neuron activation and target-cell responses: when these overlap, inhibition is divisive (causing no change in tuning width of target neurons), but when they do not overlap, inhibition is subtractive (and reduces tuning width).