Biomedical subjects

Kenneth D Miller

Publications and source records attributed to Kenneth D Miller.

8 recordsLinked to original sources

PubMed2006

Adaptive filtering enhances information transmission in visual cortex.

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.

Adaptation, Physiological↗

PubMed2003

Different roles for simple-cell and complex-cell inhibition in V1.

Previously, we proposed a model of the circuitry underlying simple-cell responses in cat primary visual cortex (V1) layer 4. We argued that the ordered arrangement of lateral geniculate nucleus inputs to a simple cell must be supplemented by a component of feedforward inhibition that is untuned for orientation and responds to high temporal frequencies to explain the sharp contrast-invariant orientation tuning and low-pass temporal frequency tuning of simple cells. The temporal tuning also requires a significant NMDA component in geniculocortical synapses. Recent experiments have revealed cat V1 layer 4 inhibitory neurons with two distinct types of receptive fields (RFs): complex RFs with mixed ON/OFF responses lacking in orientation tuning, and simple RFs with normal, sharp-orientation tuning (although, some respond to all orientations). We show that complex inhibitory neurons can provide the inhibition needed to explain simple-cell response properties. Given this complex cell inhibition, antiphase or "push-pull" inhibition from tuned simple inhibitory neurons acts to sharpen spatial frequency tuning, lower responses to low temporal frequency stimuli, and increase the stability of cortical activity.

Animals↗

PubMed2003

Multiplicative gain changes are induced by excitation or inhibition alone.

We model the effects of excitation and inhibition on the gain of cortical neurons. Previous theoretical work has concluded that excitation or inhibition alone will not cause a multiplicative gain change in the curve of firing rate versus input current. However, such gain changes in vivo are measured in the curve of firing rate versus stimulus parameter. We find that when this curve is considered, and when the nonlinear relationships between stimulus parameter and input current and between input current and firing rate in vivo are taken into account, then simple excitation or inhibition alone can induce a multiplicative gain change. In particular, the power-law relationship between voltage and firing rate that is induced by neuronal noise is critical to this result. This suggests an unexpectedly simple mechanism that may underlie the gain modulations commonly observed in cortex. More generally, it suggests that a smaller input will multiplicatively modulate the gain of a larger one when both converge on a common cortical target.

Animals↗

PubMed2003

Understanding layer 4 of the cortical circuit: a model based on cat V1.

This paper reviews theoretical and experimental results on the processing of layer 4, the input-recipient layer, of cat primary visual cortex (V1). A wide range of experimental data can be understood from a model in which response tuning of layer 4 cells is largely determined by a local interplay of feedforward excitation (from thalamus) and feedforward inhibition (from layer 4 inhibitory interneurons driven by thalamus). Feedforward inhibition dominates excitation, inherits its tuning from the thalamic input and sharpens the tuning of excitatory cells. At least a strong component of the feedforward inhibition received by a cell is spatially opponent to the excitation it receives, meaning that inhibition is driven by dark in regions of the visual field in which excitation is driven by light, and vice versa. The idea of opponent inhibition can be generalized to mean inhibition driven by input patterns that are strongly anti-correlated with the patterns that excite a cell. This paper argues that dominant feedforward opponent inhibition may be a general principle of cortical layer 4. This leads to the suggestion that the properties that show columnar organization--invariance across the vertical depth of cortex--may be properties that are shared by 'opposite' (anticorrelated) stimulus pairs. This contrasts with the more common idea that a column represents a set of cells that all share similar stimulus preferences.

Animals↗

PubMed2003

Acoustic variability and distinguishability among mouse ultrasound vocalizations.

Auditory neurobiology has benefited significantly from ethological approaches using acoustic communication signals. Developing an ethological model in a genetically manipulable system such as the mouse would enhance the ability to investigate the processing, learning, and recognition of sounds. Characterizing the basic acoustic structure of mouse vocalizations would help lay a foundation for such a future study. Towards this goal, ultrasound vocalizations emitted by isolated mouse pups and pairs of adult males and females have been digitally recorded and examined. Previous work suggests that these calls may have communicative significance. An analysis of the natural variability in their spectral content, median frequency, duration, and repetition period reveals acoustic structure that could be used for recognizing the calls. Other parameters, like the rate of frequency modulation, may also be informative, but have not been examined. Pup isolation calls develop systematically between postnatal day 5 and 12 towards a more stereotyped vocalization--contracting from a wide range of values into narrower clusters of frequency and duration, and shifting from longer to shorter repetition periods. Most significantly, pup isolation and adult encounter calls fall into two distinct spectral and temporal categories, making it possible for a receiver to acoustically distinguish between them, and to potentially categorically perceive them along those dimensions.

Age Factors↗

PubMed2002

Opponent inhibition: a developmental model of layer 4 of the neocortical circuit.

We model the development of the functional circuit of layer 4 (the input-recipient layer) of cat primary visual cortex. The observed thalamocortical and intracortical circuitry codevelop under Hebb-like synaptic plasticity. Hebbian development yields opponent inhibition: inhibition evoked by stimuli anticorrelated with those that excite a cell. Strong opponent inhibition enables recognition of stimulus orientation in a manner invariant to stimulus contrast. These principles may apply to cortex more generally: Hebb-like plasticity can guide layer 4 of any piece of cortex to create opposition between anticorrelated stimulus pairs, and this enables recognition of specific stimulus patterns in a manner invariant to stimulus magnitude. Properties that are invariant across a cortical column are predicted to be those shared by opponent stimulus pairs; this contrasts with the common idea that a column represents cells with similar response properties.

Action Potentials↗

PubMed2002

Neural noise can explain expansive, power-law nonlinearities in neural response functions.

Many phenomenological models of the responses of simple cells in primary visual cortex have concluded that a cell's firing rate should be given by its input raised to a power greater than one. This is known as an expansive power-law nonlinearity. However, intracellular recordings have shown that a different nonlinearity, a linear-threshold function, appears to give a good prediction of firing rate from a cell's low-pass-filtered voltage response. Using a model based on a linear-threshold function, Anderson et al. showed that voltage noise was critical to converting voltage responses with contrast-invariant orientation tuning into spiking responses with contrast-invariant tuning. We present two separate results clarifying the connection between noise-smoothed linear-threshold functions and power-law nonlinearities. First, we prove analytically that a power-law nonlinearity is the only input-output function that converts contrast-invariant input tuning into contrast-invariant spike tuning. Second, we examine simulations of a simple model that assumes instantaneous spike rate is given by a linear-threshold function of voltage and voltage responses include significant noise. We show that the resulting average spike rate is well described by an expansive power law of the average voltage (averaged over multiple trials), provided that average voltage remains less than about 1.5 SDs of the noise above threshold. Finally, we use this model to show that the noise levels recorded by Anderson et al. are consistent with the degree to which the orientation tuning of spiking responses is more sharply tuned relative to the orientation tuning of voltage responses. Thus neuronal noise can robustly generate power-law input-output functions of the form frequently postulated for simple cells.

Action Potentials↗

PubMed2002

LGN input to simple cells and contrast-invariant orientation tuning: an analysis.

We develop a new analysis of the lateral geniculate nucleus (LGN) input to a cortical simple cell, demonstrating that this input is the sum of two terms, a linear term and a nonlinear term. In response to a drifting grating, the linear term represents the temporal modulation of input, and the nonlinear term represents the mean input. The nonlinear term, which grows with stimulus contrast, has been neglected in many previous models of simple cell response. We then analyze two scenarios by which contrast-invariance of orientation tuning may arise. In the first scenario, at larger contrasts, the nonlinear part of the LGN input, in combination with strong push-pull inhibition, counteracts the nonlinear effects of cortical spike threshold, giving the result that orientation tuning scales with contrast. In the second scenario, at low contrasts, the nonlinear component of LGN input is negligible, and noise smooths the nonlinearity of spike threshold so that the input-output function approximates a power-law function. These scenarios can be combined to yield contrast-invariant tuning over the full range of stimulus contrast. The model clarifies the contribution of LGN nonlinearities to the orientation tuning of simple cells and demonstrates how these nonlinearities may impact different models of contrast-invariant tuning.

Animals↗