We often face alternatives that we are free to choose between. Planning movements to select an alternative involves several areas in frontal and parietal cortex1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 that are anatomically connected into long-range circuits12. These areas must coordinate their activity to select a common movement goal, but how neural circuits make decisions remains poorly understood. Here we simultaneously record from the dorsal premotor area (PMd) in frontal cortex and the parietal reach region (PRR) in parietal cortex to investigate neural circuit mechanisms for decision making. We find that correlations in spike and local field potential (LFP) activity between these areas are greater when monkeys are freely making choices than when they are following instructions. We propose that a decision circuit featuring a sub-population of cells in frontal and parietal cortex may exchange information to coordinate activity between these areas. Cells participating in this decision circuit may influence movement choices by providing a common bias to the selection of movement goals.

Temporal and quantitative relations between excitatory and inhibitory inputs in the cortex are central to its activity, yet they remain poorly understood. In particular, a controversy exists regarding the extent of correlation between cortical excitation and inhibition. Using simultaneous intracellular recordings in pairs of nearby neurons in vivo, we found that excitatory and inhibitory inputs are continuously synchronized and correlated in strength during spontaneous and sensory-evoked activities in the rat somatosensory cortex.

Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.

Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding

The membrane conductance of a pyramidal neuron in vivo is substantially increased by background synaptic input. Increased membrane conductance, or shunting, does not simply reduce neuronal excitability. Recordings from hippocampal pyramidal neurons using dynamic clamp revealed that adaptation caused complete cessation of spiking in the high conductance state, whereas repetitive spiking could persist despite adaptation in the low conductance state. This behavior was reproduced in a phase plane model and was explained by a shunting-induced increase in voltage threshold. The increase in threshold allows greater activation of the M current (I(M)) at subthreshold potentials and reduces the minimum adaptation required to stabilize the system; in contrast, activation of the afterhyperpolarization current is unaffected by the increase in threshold and therefore remains unable to stop repetitive spiking. The nonlinear interaction between shunting and I(M) has other important consequences. First, timing of spikes elicited by brief stimuli is more precise when background spikes elicited by sustained input are prohibited, as occurs exclusively with I(M)-mediated adaptation in the high conductance state. Second, activation of I(M) at subthreshold potentials, which is increased in the high conductance state, hyperpolarizes average membrane potential away from voltage threshold, allowing only large, rapid fluctuations to reach threshold and elicit spikes. These results suggest that the shift from a low to high conductance state in a pyramidal neuron is accompanied by a switch from encoding time-averaged input with firing rate to encoding transient inputs with precisely timed spikes, in effect, switching the operational mode from integration to coincidence detection.

The spiking activity of nearby cortical neurons is not independent. Numerous studies have explored the importance of this correlated responsivity for visual coding and perception, often by comparing the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. Pairwise responses typically provide slightly more information so that encoding is weakly synergistic. The simple comparison between pairwise and summed individual responses conflates several forms of correlation, however, making it impossible to judge the relative importance of synchronous spiking, basic tuning properties, and stimulus-independent and stimulus-dependent correlation. We have applied an information theoretic approach to this question, using the responses of pairs of neurons to drifting sinusoidal gratings of different directions and contrasts that have been recorded in the primary visual cortex of anesthetized macaque monkeys. Our approach allows us to break down the information provided by pairs of neurons into a number of components. This analysis reveals that, although synchrony is prevalent and informative, the additional information it provides frequently is offset by the redundancy arising from the similar tuning properties of the two cells. Thus coding is approximately independent with weak synergy or redundancy arising, depending on the similarity in tuning and the temporal precision of the analysis. We suggest that this would allow cortical circuits to enjoy the stability provided by having similarly tuned neurons without suffering the penalty of redundancy, because the associated information transmission deficit is compensated for by stimulus-dependent synchrony.

Synchronized firing among neurons in the working brain is inferred to reflect coding by cell assemblies, which dynamically change their sizes and functional connections to encode various information. It therefore follows that, if synchronized firing reflects cell-assembly coding, it should show dynamic changes that depend on the tasks and events being processed and on the distance between the neurons. By using unique spike-sorting and multi-neuronal recording methods, we investigated such dynamics of synchrony in the prefrontal cortex of monkeys while they were successively performing two tasks in which working memory for either stimulus duration or color was required. Forty-eight percent of 1405 neuronal pairs showed firing synchrony during the performance of the tasks. Almost half of such neuronal pairs showed fixed synchrony and constantly fired together in both tasks. However, some neuronal pairs showed task-dependent synchrony that appeared in only one of the tasks. Moreover, the other neuronal pairs showed event-task-dependent synchrony that appeared during stimulus or retention periods in the tasks, but the periods showing synchrony varied between the tasks. Fixed synchrony and task-dependent synchrony were mostly observed among neighboring neurons and showed little variation of spike timings; the event-task-dependent synchrony, in contrast, was more often detected among distant neurons with larger variation of spike timings than the other two types of synchrony. These results suggest that some closely neighboring neurons have dynamic and sharp synchrony to represent certain situations (tasks), whereas some distant neurons show more dynamic and unstable synchronous firing to represent quickly changing events being processed in working memory.

Sensory stimuli reach the brain via the thalamocortical projection, a group of axons thought to be among the most powerful in the neocortex. Surprisingly, these axons account for only approximately 15% of synapses onto cortical neurons. The thalamocortical pathway might thus achieve its effectiveness via high-efficacy thalamocortical synapses or via amplification within cortical layer 4. In rat somatosensory cortex, we measured in vivo the excitatory postsynaptic potential evoked by a single synaptic connection and found that thalamocortical synapses have low efficacy. Convergent inputs, however, are both numerous and synchronous, and intracortical amplification is not required. Our results suggest a mechanism of cortical activation by which thalamic input alone can drive cortex.

Cortical sensory neurons are known to be highly variable, in the sense that responses evoked by identical stimuli often change dramatically from trial to trial. The origin of this variability is uncertain, but it is usually interpreted as detrimental noise that reduces the computational accuracy of neural circuits. Here we investigate the possibility that such response variability might in fact be beneficial, because it may partially compensate for a decrease in accuracy due to stochastic changes in the synaptic strengths of a network. We study the interplay between two kinds of noise, response (or neuronal) noise and synaptic noise, by analyzing their joint influence on the accuracy of neural networks trained to perform various tasks. We find an interesting, generic interaction: when fluctuations in the synaptic connections are proportional to their strengths (multiplicative noise), a certain amount of response noise in the input neurons can significantly improve network performance, compared to the same network without response noise. Performance is enhanced because response noise and multiplicative synaptic noise are in some ways equivalent. So if the algorithm used to find the optimal synaptic weights can take into account the variability of the model neurons, it can also take into account the variability of the synapses. Thus, the connection patterns generated with response noise are typically more resistant to synaptic degradation than those obtained without response noise. As a consequence of this interplay, if multiplicative synaptic noise is present, it is better to have response noise in the network than not to have it. These results are demonstrated analytically for the most basic network consisting of two input neurons and one output neuron performing a simple classification task, but computer simulations show that the phenomenon persists in a wide range of architectures, including recurrent (attractor) networks and sensorimotor networks that perform coordinate transformations. The results suggest that response variability could play an important dynamic role in networks that continuously learn.

The decoding scheme of a stimulus can be different from the stochastic encoding scheme in the neural population coding. The stochastic fluctuations are not independent in general, but an independent version could be used for the ease of decoding. How much information is lost by using this unfaithful model for decoding? There are discussions concerning loss of information (Nirenberg & Latham, 2003; Schneidman, Bialek, & Berry, 2003). We elucidate the Nirenberg-Latham loss from the point of view of information geometry.