Noisy neurons can certainly compute.
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Algorithms↗
Biomedical subjects
Emilio Salinas
Publications and source records attributed to Emilio Salinas.
When response variability increases neural network robustness to synaptic noise.
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.
Algorithms↗
How behavioral constraints may determine optimal sensory representations.
The sensory-triggered activity of a neuron is typically characterized in terms of a tuning curve, which describes the neuron's average response as a function of a parameter that characterizes a physical stimulus. What determines the shapes of tuning curves in a neuronal population? Previous theoretical studies and related experiments suggest that many response characteristics of sensory neurons are optimal for encoding stimulus-related information. This notion, however, does not explain the two general types of tuning profiles that are commonly observed: unimodal and monotonic. Here I quantify the efficacy of a set of tuning curves according to the possible downstream motor responses that can be constructed from them. Curves that are optimal in this sense may have monotonic or nonmonotonic profiles, where the proportion of monotonic curves and the optimal tuning-curve width depend on the general properties of the target downstream functions. This dependence explains intriguing features of visual cells that are sensitive to binocular disparity and of neurons tuned to echo delay in bats. The numerical results suggest that optimal sensory tuning curves are shaped not only by stimulus statistics and signal-to-noise properties but also according to their impact on downstream neural circuits and, ultimately, on behavior.
Animals↗
Inhibitory synchrony as a mechanism for attentional gain modulation.
Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neuron's firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.
Animals↗
Context-dependent selection of visuomotor maps.
BACKGROUND: Behavior results from the integration of ongoing sensory signals and contextual information in various forms, such as past experience, expectations, current goals, etc. Thus, the response to a specific stimulus, say the ringing of a doorbell, varies depending on whether you are at home or in someone else's house. What is the neural basis of this flexibility? What mechanism is capable of selecting, in a context-dependent way an adequate response to a given stimulus? One possibility is based on a nonlinear neural representation in which context information regulates the gain of stimulus-evoked responses. Here I explore the properties of this mechanism. RESULTS: By means of three hypothetical visuomotor tasks, I study a class of neural network models in which any one of several possible stimulus-response maps or rules can be selected according to context. The underlying mechanism based on gain modulation has three key features: (1) modulating the sensory responses is equivalent to switching on or off different subpopulations of neurons, (2) context does not need to be represented continuously, although this is advantageous for generalization, and (3) context-dependent selection is independent of the discriminability of the stimuli. In all cases, the contextual cues can quickly turn on or off a sensory-motor map, effectively changing the functional connectivity between inputs and outputs in the networks. CONCLUSIONS: The modulation of sensory-triggered activity by proprioceptive signals such as eye or head position is regarded as a general mechanism for performing coordinate transformations in vision. The present results generalize this mechanism to situations where the modulatory quantity and the input-output relationships that it selects are arbitrary. The model predicts that sensory responses that are nonlinearly modulated by arbitrary context signals should be found in behavioral situations that involve choosing or switching between multiple sensory-motor maps. Because any relevant circumstancial information can be part of the context, this mechanism may partly explain the complex and rich behavioral repertoire of higher organisms.
Action Potentials↗
Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation.
Higher organisms can establish complex associations between sensory events and motor responses. More remarkable than their complexity, however, is that the resulting sensory-motor maps can be selectively interchanged. For example, a person who speaks English and Spanish can read aloud "con once, sin once," going effortlessly from one language to the other. What is the neural basis of this capacity? Here, a network model is presented in which multiple maps between sensory stimuli and motor actions are possible, but only one of them, depending on behavioral context, is implemented at any given time. The key is a nonlinear representation in which the gain of sensory responses is regulated by context information. Neuronal responses can indeed show variations in gain, as has been documented in the case of proprioceptive signals such as eye and head position, which can modulate visually triggered activity. However, in contrast to these, the contextual cues used here need not bear any relationship to the physical attributes of the stimuli; in particular, spatial location is irrelevant. The model thus postulates the existence of sensory neurons that are nonlinearly modulated by arbitrary context signals, a plausible and testable prediction. The proposed mechanism allows a network of neurons to effectively change the functional connectivity between its inputs and outputs and may partially explain how animals can quickly adapt their behavior to varying environmental conditions.
Computer Simulation↗
Correlated neuronal discharges that increase coding efficiency during perceptual discrimination.
During a sensory discrimination task, the responses of multiple sensory neurons must be combined to generate a choice. The optimal combination of responses is determined both by their dependence on the sensory stimulus and by their cofluctuations across trials-that is, the noise correlations. Positively correlated noise is considered deleterious, because it limits the coding accuracy of populations of similarly tuned neurons. However, positively correlated fluctuations between differently tuned neurons actually increase coding accuracy, because they allow the common noise to be subtracted without signal loss. This is demonstrated with data recorded from the secondary somatosensory cortex of monkeys performing a vibrotactile discrimination task. The results indicate that positive correlations are not always harmful and may be exploited by cortical networks to enhance the neural representation of features to be discriminated.
Animals↗
Flutter discrimination: neural codes, perception, memory and decision making.
Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.
Animals↗
Background synaptic activity as a switch between dynamical states in a network.
A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.
Action Potentials↗
From sensation to action.
Key to understanding somatosensation is the form of how the mechanical stimuli are represented in the evoked neuronal activity of the brain. Here, we focus on studies that address the question of which components of the evoked neuronal activity in the somatosensory system represent the stimulus features. We review experiments that probe whether these neuronal representations are essential to somatosensation. We also discuss recent results that suggest how the somatosensory stimuli are represented in the brain during short-term memory. Finally, we review data that show the neuronal correlates of a decision during somatosensory perception.
Animals↗
Exploring the cortical evidence of a sensory-discrimination process.
Humans and monkeys have similar abilities to discriminate the difference in frequency between two consecutive mechanical vibrations applied to their fingertips. This task can be conceived as a chain of neural operations: encoding the two consecutive stimuli, maintaining the first stimulus in working memory, comparing the second stimulus with the memory trace left by the first stimulus and communicating the result of the comparison to the motor apparatus. We studied this chain of neural operations by recording and manipulating neurons from different areas of the cerebral cortex while monkeys performed the task. The results indicate that neurons of the primary somatosensory cortex (S1) generate a neural representation of vibrotactile stimuli which correlates closely with psychophysical performance. Discrimination based on microstimulation patterns injected into clusters of S1 neurons is indistinguishable from that produced by natural stimuli. Neurons from the secondary somatosensory cortex (S2), prefrontal cortex and medial premotor cortex (MPC) display at different times the trace of the first stimulus during the working-memory component of the task. Neurons from S2 and MPC appear to show the comparison between the two stimuli and correlate with the behavioural decisions. These neural operations may contribute to the sensory-discrimination process studied here.
Animals↗
Integrate-and-fire neurons driven by correlated stochastic input.
Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.
Action Potentials↗