Biology subjects

Machens, C.

Publications and source records attributed to Machens, C..

6 recordsLinked to original sources

bioRxiv2026

Direct cortical feedforward and feedback pathways differentially shape visual responses and selectively engage looped circuits

Nearly every computational account of hierarchical cortex rests on assumptions about the influence of direct cortico-cortical inputs and which neurons they act upon. Yet, disentangling their direct functional contributions from indirect, multisynaptic influences remains difficult. We examined how feedforward (FF) and feedback (FB) pathways between mouse primary visual cortex (V1) and posteromedial cortex (PM) modulate visual responses, combining retrograde labelling, multi-area two-photon imaging and optogenetic perturbations. High or low activity in either pathway was associated with both excitation and inhibition of neurons in the targeted areas. FF activity was associated with stronger additive changes, while FB activity was related to stronger multiplicative modulations and improvements in visual decoding. Optogenetic manipulations causally confirmed these asymmetries. Both pathways preferentially modulated reciprocating "looped" neurons, with FF exciting them and FB either exciting or inhibiting them. These findings demonstrate that feedforward and feedback pathways serve distinct functional roles within recurrent cortical loops.

neuroscience↗

bioRxiv2026

Control of Male Mouse Copulatory Behavior by Midbrain Dopamine Neurons

Ejaculation in male mice and humans requires intravaginal thrusting movements following penile intromission, yet the underlying neural mechanisms remain poorly understood. In this study, we used fiber photometry to record real-time activity of dopamine (DA) neurons in the ventral tegmental area (VTA) of male mice during copulation across six sexual interactions, in order to investigate how these dynamics change with experience and to examine their functional significance. Contrary to expectations, phasic VTA DA activity remained stable across sessions, showing time-locked responses to mounts, penile intromission, individual thrusts, and ejaculation, indicating experience-independent neural representations of all copulatory events. Notably, thrusting-related activity sharply declined just before ejaculation. Optogenetic inhibition of DA neurons disrupted thrusting behavior, but did not block ejaculation, suggesting DA signaling is critical for sustaining copulatory actions rather than triggering ejaculation itself. While prevailing theories frame VTA DA activity as primarily experience-dependent or centered on reward prediction, we here highlight its role in behavioral persistence during copulation. More broadly, our findings extend understanding of DA function beyond classical reward paradigms to the regulation of innate complex, goal-directed behaviors.

neuroscience↗

bioRxiv2025

Representational drift without synaptic plasticity

Neural computations support stable behavior despite relying on many dynamically changing biological processes. One such process is representational drift (RD), in which neurons responses change over the timescale of minutes to weeks, while perception and behavior remain unchanged. Generally, RD is believed to be caused by changes in synaptic weights, which alter individual neurons tuning properties. Since these changes alter the population readout, they require adaptation of downstream areas to maintain stable function, a costly and non-local problem. Here we propose that much of the observed drift phenomena can be explained by a simpler mechanism: changes in the excitability of cells without changes in synaptic weights. We show that such excitability changes can change the apparent tuning of neurons without requiring adaptation of population readouts in downstream areas. We use spike coding networks (SCN) to show that the extent of these tuning shifts matches experimentally observed changes. Moreover, specific decoders trained on one excitability setting perform poorly on others, while a general decoder can perform close to optimal across excitability changes if trained across many days. Our work proposes a simple mechanism without synaptic plasticity that explains experimentally observed RD, while downstream decoding and, by extension, behavior remain stable.

neuroscience↗

bioRxiv2025

Stochastic activity in low-rank recurrent neural networks

The geometrical and statistical properties of brain activity depend on the way neurons connect to form recurrent circuits. However, the link between connectivity structure and emergent activity remains incompletely understood. We investigate this relationship in recurrent neural networks with additive stochastic inputs. We assume that the synaptic connectivity can be expressed in a low-rank form, parameterized by a handful of connectivity vectors, and examine how the geometry of emergent activity relates to these vectors. Our findings reveal that this relationship critically depends on the dimensionality of the external stochastic inputs. When inputs are low-dimensional, activity remains low-dimensional, and recurrent dynamics influence it within a subspace spanned by a subset of the connectivity vectors, with dimensionality equal to the rank of the connectivity matrix. In contrast, when inputs are high-dimensional, activity also becomes potentially high-dimensional. The contribution of recurrent dynamics is apparent within a subspace spanned by the totality of the connectivity vectors, with dimensionality equal to twice the rank of the connectivity matrix. Applying our formalism to excitatory-inhibitory networks, we discuss how the input configuration also plays a crucial role in determining the amount of amplification generated by non-normal dynamics. Our work provides a foundation for studying activity in structured brain circuits under realistic noise conditions, and offers a framework for interpreting stochastic models inferred from experimental data.

neuroscience↗

bioRxiv2021

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Brain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", such that feedback signaling does not directly affect activity that is fed forward.

neuroscience↗

bioRxiv2018

State-dependent geometry of population activity in rat auditory cortex

The accuracy of the sensory neural code depends on the relationship between variability in population activity caused by the stimuli versus variability caused by noise. Whereas a large body of work has addressed this issue at a theoretical level, there have been few systematic empirical investigations using large-scale simultaneous recordings. We studied the geometry of the population code in the rat auditory cortex using sound stimuli varying across two orthogonal dimensions: the difference in sound level between the two ears, and the average level across the ears. We quantified the structure of the code across recordings spanning a wide range of brain states along the activation/inactivation continuum. We found that the nature of the code, both in terms of the stimulus and the noise subspaces, depends strongly on the degree of cortical activation. During the inactive (or synchronized) state, the code is characterized by a prevalence of contra-lateral tuning, a preference for loud sounds, gain-modulated tuning curves and coherent global noise fluctuations overlapping with the signal subspace. In contrast, in the most active (or desynchronized) recordings, there is an approximately symmetric preference between contra-, ipsi-, quiet and loud sounds, no systematic changes in the gain of lateralization tuning curves by sound intensity, and noise fluctuations span dimensions orthogonal to those evoked by the stimuli. Logistic regression could predict the sound lateralization with accuracy similar to the behaving rats, but only during cortical activation. Furthermore, also exclusively during the active state, the lateralization of sounds of different overall level could be decoded with the same accuracy, even by the same decoder, due to an orthogonal representation of level-differences and overall level across the population. Decoding accuracy from surrogate datasets with independent noise across neurons still displays strong state-dependence, suggesting that tuning curve reorganization plays a critical role in the overall effect of brain state on the population code. Our results provide an empirical foundation for the geometry and state dependence of cortical population codes.

neuroscience↗