Similar Algorithms in Different Sensory Systems and Animals

Abstract

Animals must both recognize and localize the sensory signals essential for their survival and reproduction. Certain cues contained in a signal define its identity and location. Such cues include, for example, the temporal pattern of song for species recognition in crickets and binaural disparities for sound localization in owls. One can predict potential cues and the methods of using them from consideration of the physical attributes of the signal for the task. The discovery of the real cue that is used by the animal. however, requires study of the animal's response to different potential cues.Once the real cue is identified, the next question is how is it encoded in the language of the nervous system? One can develop an algorithm for the solution of the coding problem. There are, however, many possible ways of solving the same problem. There is, however, no theoretical means of identifying the real algorithm. Analysis of neuronal responses to stimuli and study of the connections between neurons may inform us about the coding scheme because the connections and signals between neurons underlie the coding mechanisms, but what neurons tell depends on what question the investigator asks. As Barlow ( 1972) pointed out, " ... neurophysiology and sensation are best linked by looking at the flow of information rather than simpler measures of neuronal activity .. , A neurophysiological investigation of how information is transformed from lower-to higher-order stations may be difficult because information processing is highly nonlinear. Going in the opposite direction. the "topdown" approach is easier in some systems because one starts with the knowledge of what is encoded at the top or at a relatively higher station. The criterion for encoding is the stimulus selectivity of single neurons. The flow of information is therefore inferred from the stimulus selectivities recorded in interconnected stations. For example, the neurons of the external nucleus of the inferior colliculus in the barn owl respond selectively to a combination of interaural time and intensity differences. The neuronal selectivities for these binaural cues have been traced from this nucleus back to the first sites where the selectivities emerge and the pathways for the flow of information can be established .The approach that looks for the flow of information has been used only in a few complex neural systems including the visual system of the macaque monkey (for review, see Van Essen

Coincidence detection in the Hodgkin–Huxley equations

“…at around f S =1 kHz. Indeed, maximal vector strength, r, is inversely proportional to the main sound frequency, f S , as confirmed in experiment in hen, barn owl and dog (Goldberg and Brown, 1969;Konishi, 1990;Reyes et al, 1996). For experimental data, see fig.…”

Neural Syntax: Cell Assemblies, Synapsembles, and Readers

“…Multiple time-integrator (reader) mechanisms exist in the brain; each with a characteristic temporal window, and integrators with longer time constants can combine neural assemblies into long sequences. Different reader mechanisms may simultaneously monitor the activity of the same assembly patterns and may extract different types of meanings, for example temporal relationships for one feature and spiking intensity for another (Hirase et al, 1999; Huxter et al, 2003; Konishi 1990; Niessing and Friedrich, 2010). …”

Time and timing in the acoustic recognition system of crickets

“…Barlow (1961), Marr (1982) and Konishi (1990) emphasized the importance of understanding the computational goals and implemented algorithms for our understanding of sensory processing in different modalities and sensory pathways. The general computational goal of auditory processing of crickets and katydids for mate recognition is specified by the recognition of the conspecific song signal or, more parsimoniously by the discrimination of its own signal against all other signals (c.f.…”

“…For each component of a LN-model it is also possible to specify a computational goal and an appropriate algorithm (Table 1). From computational goals and algorithms the salient cues (Konishi, 1990) for signal discrimination can be derived that consist of temporal information given by pulse rate and duty cycle (Figures 3, 4). …”