The responses of single neurons in the temporal visual cortical areas of the macaque when more than one stimulus is present in the receptive field

Neurons in the temporal visual cortical areas of primates have large receptive fields, which can show considerable selectivity for what the stimulus is irrespective of exactly where it is in the visual field. This is called translation invariance. However, such results have been found when there is only one stimulus in the visual field. The question arises of how the visual system operates in a cluttered environment. To investigate this we measured the responses of neurons with face-selective responses in the cortex in the anterior part of the superior temporal sulcus of rhesus macaques performing a visual fixation task. We found that the response of neurons to an effective face centred 8.5° from the fovea was decreased to 71 if an ineffective face stimulus for that cell was present at the fovea. In a similar way, introduction of a parafoveal ineffective face stimulus decreased the responses of these neurons to an effective face stimulus at the fovea to 75. In addition to these interactions, it was found that an effective stimulus object at the fovea produced a larger response than when it was parafoveal, and that this weighting towards an object at the fovea was also seen when more than one object was present in the visual field. The implication of this weighting of the responses of neurons towards objects at the fovea, even in an environment with more than one object present, is that the output of the visual system provides information to subsequent systems particularly about objects at the fovea, so that learning about these objects (and less about other objects elsewhere in the visual field) is facilitated. © 1995 Springer-Verlag

The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex

It has been shown that it is possible to read, from the firing rates of just a small population of neurons, the code that is used in the macaque temporal lobe visual cortex to distinguish between different faces being looked at. To analyse the information provided by populations of single neurons in the primate temporal cortical visual areas, the responses of a population of 14 neurons to 20 visual stimuli were analysed in a macaque performing a visual fixation task. The population of neurons analysed responded primarily to faces, and the stimuli utilised were all human and monkey faces. Each neuron had its own response profile to the different members of the stimulus set. The mean response of each neuron to each stimulus in the set was calculated from a fraction of the ten trials of data available for every stimulus. From the remaining data, it was possible to calculate, for any population response vector, the relative likelihoods that it had been elicited by each of the stimuli in the set. By comparison with the stimuli actually shown, the mean percentage correct identification was computed and also the mean information about the stimuli, in bits, that the population of neurons carried on a single trial. When the decoding algorithm used for this calculation approximated an optimal, Bayesian estimate of the relative likelihoods, the percentage correct increased from 14 correct (chance was 5 correct) with one neuron to 67 with 14 neurons. The information conveyed by the population of neurons increased approximately linearly from 0.33 bits with one neuron to 2.77 bits with 14 neurons. This leads to the important conclusion that the number of stimuli that can be encoded by a population of neurons in this part of the visual system increases approximately exponentially as the number of cells in the sample increases (in that the log of the number of stimuli increases almost linearly). This is in contrast to a local encoding scheme (of 'grandmother' cells), in which the number of stimuli encoded increases linearly with the number of cells in the sample. Thus one of the potentially important properties of distributed representations, an exponential increase in the number of stimuli that can be represented, has been demonstrated in the brain with this population of neurons. When the algorithm used for estimating stimulus likelihood was as simple as could be easily implemented by neurons receiving the population's output (based on just the dot product between the population response vector and each mean response vector), it was still found that the 14-neuron population produced 66 correct guesses and conveyed 2.30 bits of information, or 83 of the information that could be extracted with the nearly optimal procedure. It was also shown that, although there was some redundancy in the representation (with each neuron contributing to the information carried by the whole population 60 of the information it carried alone, rather than 100), this is due to the fact that the number of stimuli in the set was limited (it was 20). The data are consistent with minimal redundancy for sufficiently large and diverse sets of stimuli. The implication for brain connectivity of the distributed encoding scheme, which was demonstrated here in the case of faces, is that a neuron can receive a great deal of information about what is encoded by a large population of neurons if it is able to receive its inputs from a random subset of these neurons, even of limited numbers (e.g. hundreds)

Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises

Dynamics of an ensemble of $N$-unit FitzHugh-Nagumo (FN) neurons subject to white noises has been studied by using a semi-analytical dynamical mean-field (DMF) theory in which the original $2 N$-dimensional {\it stochastic} differential equations are replaced by 8-dimensional {\it deterministic} differential equations expressed in terms of moments of local and global variables. Our DMF theory, which assumes weak noises and the Gaussian distribution of state variables, goes beyond weak couplings among constituent neurons. By using the expression for the firing probability due to an applied single spike, we have discussed effects of noises, synaptic couplings and the size of the ensemble on the spike timing precision, which is shown to be improved by increasing the size of the neuron ensemble, even when there are no couplings among neurons. When the coupling is introduced, neurons in ensembles respond to an input spike with a partial synchronization. DMF theory is extended to a large cluster which can be divided into multiple sub-clusters according to their functions. A model calculation has shown that when the noise intensity is moderate, the spike propagation with a fairly precise timing is possible among noisy sub-clusters with feed-forward couplings, as in the synfire chain. Results calculated by our DMF theory are nicely compared to those obtained by direct simulations. A comparison of DMF theory with the conventional moment method is also discussed.Comment: 29 pages, 2 figures; augmented the text and added Appendice

Biased competition through variations in amplitude of γ-oscillations

Experiments in visual cortex have shown that the firing rate of a neuron in response to the simultaneous presentation of a preferred and non-preferred stimulus within the receptive field is intermediate between that for the two stimuli alone (stimulus competition). Attention directed to one of the stimuli drives the response towards the response induced by the attended stimulus alone (selective attention). This study shows that a simple feedforward model with fixed synaptic conductance values can reproduce these two phenomena using synchronization in the gamma-frequency range to increase the effective synaptic gain for the responses to the attended stimulus. The performance of the model is robust to changes in the parameter values. The model predicts that the phase locking between presynaptic input and output spikes increases with attention

Brain mechanisms underlying flavour and appetite

Complementary neurophysiological recordings in macaques and functional neuroimaging in humans show that the primary taste cortex in the rostral insula and adjoining frontal operculum provides separate and combined representations of the taste, temperature and texture (including viscosity and fat texture) of food in the mouth independently of hunger and thus of reward value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by learning with olfactory and visual inputs. Different neurons respond to different combinations, providing a rich representation of the sensory properties of food. In the orbitofrontal cortex, feeding to satiety with one food decreases the responses of these neurons to that food, but not to other foods, showing that sensory-specific satiety is computed in the primate (including human) orbitofrontal cortex. Consistently, activation of parts of the human orbitofrontal cortex correlates with subjective ratings of the pleasantness of the taste and smell of food. Cognitive factors, such as a word label presented with an odour, influence the pleasantness of the odour and the activation produced by the odour in the orbitofrontal cortex. These findings provide a basis for understanding how what is in the mouth is represented by independent information channels in the brain; how the information from these channels is combined; and how and where the reward and subjective affective value of food is represented and is influenced by satiety signals. Activation of these representations in the orbitofrontal cortex may provide the goal for eating, and understanding them helps to provide a basis for understanding appetite and its disorders