Ratphones: an affordable tool for highly controlled sound presentation in freely moving rats

[Abstract] Encoding and processing sensory information is key to understanding the environment and to guiding behavior accordingly. Characterizing the behavioral and neural correlates of these processes requires the experimenter to have a high degree of control over stimuli presentation. For auditory stimulation in animals with relatively large heads, this can be accomplished by using headphones. However, it has proven more challenging in smaller species, such as rats and mice, and has been only partially solved using closed-field speakers in anesthetized or head-restrained preparations. To overcome the limitations of such preparations and to deliver sound with high precision to freely moving animals, we have developed a set of miniature headphones for rats. The headphones consist of a small, skull-implantable base attached with magnets to a fully adjustable structure that holds the speakers and keeps them in the same position with respect to the ears

State-dependent geometry of population activity in rat auditory cortex

[Abstract] The accuracy of the neural code depends on the relative embedding of signal and noise in the activity of neural populations. Despite a wealth of theoretical work on population codes, there are few empirical characterizations of the high-dimensional signal and noise subspaces. We studied the geometry of population codes in the rat auditory cortex across brain states along the activation-inactivation continuum, using sounds varying in difference and mean level across the ears. As the cortex becomes more activated, single-hemisphere populations go from preferring contralateral loud sounds to a symmetric preference across lateralizations and intensities, gain-modulation effectively disappears, and the signal and noise subspaces become approximately orthogonal to each other and to the direction corresponding to global activity modulations. Level-invariant decoding of sound lateralization also becomes possible in the active state. Our results provide an empirical foundation for the geometry and state-dependence of cortical population codes

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability

Response of Spiking Neurons to Correlated Inputs

The effect of a temporally correlated afferent current on the firing rate of a leaky integrate-and-fire (LIF) neuron is studied. This current is characterized in terms of rates, auto and cross-correlations, and correlation time scale $\tau_c$ of excitatory and inhibitory inputs. The output rate $\nu_{out}$ is calculated in the Fokker-Planck (FP) formalism in the limit of both small and large $\tau_c$ compared to the membrane time constant $\tau$ of the neuron. By simulations we check the analytical results, provide an interpolation valid for all $\tau_c$ and study the neuron's response to rapid changes in the correlation magnitude.Comment: 4 pages, 3 figure

Reading out population codes with a matched filter

We study the optimal way to decode information present in a population code. Using a matched filter, the performance in Gaussian additive noise is as good as the theoretical maximum. The scheme can be applied when correlations among the neurons in the population are present. We show how the read out of the matched filter can be implemented in a neurophysiological realistic manner. The method seems advantageous for computations in layered networks

The mechanistic foundation of Weber’s law

[Abstract] Although Weber's law is the most firmly established regularity in sensation, no principled way has been identified to choose between its many proposed explanations. We investigated Weber's law by training rats to discriminate the relative intensity of sounds at the two ears at various absolute levels. These experiments revealed the existence of a psychophysical regularity, which we term time-intensity equivalence in discrimination (TIED), describing how reaction times change as a function of absolute level. The TIED enables the mathematical specification of the computational basis of Weber's law, placing strict requirements on how stimulus intensity is encoded in the stochastic activity of sensory neurons and revealing that discriminative choices must be based on bounded exact accumulation of evidence. We further demonstrate that this mechanism is not only necessary for the TIED to hold but is also sufficient to provide a virtually complete quantitative description of the behavior of the rats

Modelos de procesamiento cortical Multi-areal

Tesis doctoral inédita leida en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física Teórica. Fecha de lectura: 18-12-200