Input-Driven Components of Spike-Frequency Adaptation Can Be Unmasked In Vivo

Brains adapt to new situations by retuning their neurons. The most common form of neuronal adaptation, typically observed with repetitive stimulations of passive sensory organs, is depression (responses gradually decrease until stabilized). We studied cortical adaptation when stimuli are acquired by active movements of the sensory organ. In anesthetized rats, artificial whisking was induced at 5 Hz, and activity of individual neurons in layers 2-5 was recorded during whisking in air (Whisking condition) and whisking against an object (Touch condition). Response strengths were assessed by spike counts. Input-layer responses (layers 4 and 5a) usually facilitated during the whisking train, whereas superficial responses (layer 2/3) usually depressed. In layers 2/3 and 4, but not 5a, responses were usually stronger during touch trials than during whisking in air. Facilitations were specific to the protraction phase; during retraction, responses depressed in all layers and conditions. These dynamic processes were accompanied by a slow positive wave of activity progressing from superficial to deeper layers and lasting for ϳ1 s, during the transient phase of response. Our results indicate that, in the cortex, adaptation does not depend only on the level of activity or the frequency of its repetition but rather on the nature of the sensory information that is conveyed by that activity and on the processing layer. The input and laminar specificities observed here are consistent with the hypothesis that the paralemniscal layer 5a is involved in the processing of whisker motion, whereas the lemniscal barrels in layer 4 are involved in the processing of object identity.

Section: Touch-specific Adaptationsupporting

confidence: 51%

Layer-Specific Touch-Dependent Facilitation and Depression in the Somatosensory Cortex during Active Whisking

Derdikman¹,

Yu²,

Haidarliu³

et al. 2006

“…3C) were similar to those found in receptor cells (10 -80 ms) (Benda, 2002) in which SFA arises mainly from encoder adaptation (Fig. 8, 2) (Benda and Herz, 2003;Gollisch and Herz, 2004). Thus, there is no evidence for an additional adaptation process other than in the afferent receptor cells and the intrinsic mechanisms of TN1 (Fig.…”

Section: Different Origins Sfa In the Three Cell Typessupporting

confidence: 82%

“…SFA plays a role in adjusting the neuron's dynamic range to the stimulus range (Brenner et al, 2000;Dean et al, 2005;Maravall et al, 2007), optimizing energy consumption (Heitwerth et al, 2005;Niven et al, 2007), and processing of time varying signals (French et al, 2001;Benda et al, 2005). The observed SFA can result from different mechanisms and sources of adaptation, such as the transduction process in receptors cells (Hudspeth et al, 2000;Gollisch and Herz, 2004;Albert et al, 2007), spike-dependent adaptation currents (Brown and Adams, 1980;Madison and Nicoll, 1984;Fleidervish et al, 1996), synaptic depression (Abbott et al, 1997;Chance et al, 1998) and inhibitory inputs (Finlayson and Adam, 1997;Ingham and McAlpine, 2005).…”

Section: Introductionmentioning

confidence: 99%

The Origin of Adaptation in the Auditory Pathway of Locusts Is Specific to Cell Type and Function

Hildebrandt¹,

Benda²,

Hennig³

2009

We investigated the origin of spike frequency adaptation within a layered sensory network: the auditory pathway of locusts. Spike frequency adaptation as observed in an individual neuron may arise because of intrinsic or presynaptic adaptation mechanisms. To separate the contribution of different mechanisms, we recorded from the same cell during acoustic and intracellular current stimulation. We studied three identified neuron types that are representative for each network layer and participate in processing auditory patterns and localizing sound sources. By comparing current and acoustic stimulation, three distinct patterns of the distribution of adaptation mechanisms within the sensory network emerged: (1) balanced influence of both intrinsic and presynaptic adaptation mechanisms in an interneuron that summates over several receptor afferents (TN1), (2) predominantly inhibiting input as the source for spike frequency adaptation in a cell that transmits both pattern representation and directional information (BSN1), (3) primarily intrinsic, spiketriggered adaptation currents within an interneuron coding exclusively for direction (AN2). The time courses of spike frequency adaptation differed significantly between the cells types. Using the adaptation time constants, we were able to predict signal transmission properties for the different cells. We conclude that the adaptation mechanisms differ greatly among interneurons within this sensory pathway and are a function of their role in information processing.

“…Adaptation mechanisms can be divided into two classes (Gollisch and Herz, 2004): (1) mechanisms operating at the output of the neuron, such as activation of voltage-dependent conductances (Sanchez-Vives et al, 2000a,b) or tonic hyperpolarization (Carandini and Ferster, 1997), both of which operate at the level of the somatic membrane potential and cannot be stimulus specific, and (2) mechanisms operating at the inputs to the neuron, such as synaptic depression and facilitation Tsodyks and Markram, 1997) or inhibition (Zhang et al, 2003), both of which may differentially affect different parts of the dendritic tree of the neuron and thus may be stimulus specific. Our data showed that in many neurons, the responses were enhanced for frequency f 1 when f 1 was deviant and for f 2 when f 2 was deviant ( Fig.…”

Section: Mechanisms Of Adaptationmentioning

confidence: 99%

Multiple Time Scales of Adaptation in Auditory Cortex Neurons

Ulanovsky¹,

Las²,

Farkas³

et al. 2004

660

671

Neurons in primary auditory cortex (A1) of cats show strong stimulus-specific adaptation (SSA). In probabilistic settings, in which one stimulus is common and another is rare, responses to common sounds adapt more strongly than responses to rare sounds. This SSA could be a correlate of auditory sensory memory at the level of single A1 neurons. Here we studied adaptation in A1 neurons, using three different probabilistic designs. We showed that SSA has several time scales concurrently, spanning many orders of magnitude, from hundreds of milliseconds to tens of seconds. Similar time scales are known for the auditory memory span of humans, as measured both psychophysically and using evoked potentials. A simple model, with linear dependence on both short-term and long-term stimulus history, provided a good fit to A1 responses. Auditory thalamus neurons did not show SSA, and their responses were poorly fitted by the same model. In addition, SSA increased the proportion of failures in the responses of A1 neurons to the adapting stimulus. Finally, SSA caused a bias in the neuronal responses to unbiased stimuli, enhancing the responses to eccentric stimuli. Therefore, we propose that a major function of SSA in A1 neurons is to encode auditory sensory memory on multiple time scales. This SSA might play a role in stream segregation and in binding of auditory objects over many time scales, a property that is crucial for processing of natural auditory scenes in cats and of speech and music in humans.